Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
Smart Home Trends Social and Spatial Relationships
Undergraduate Dissertation Kartik Sharma A/2930/2016 Date: 02.12.2020 Word Count: 12,963
Guide: Prof. Abhishek Sorampuri Coordinator: Prof. Prabhjot Singh Sugga 2 Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
Declaration The research work embodied in this dissertation titled “Smart Home Trends: Social and Spatial Relationships” has been carried out by the undersigned as part of the undergraduate Dissertation programme in the Department of Architecture, School of Planning and Architecture, New Delhi, under the supervision of Prof. Abhishek Sorampuri. The undersigned hereby declares that this is his/her original work and has not been plagiarised in part or full form from any source.
_____________________ Signature of candidate
Name: KARTIK SHARMA Roll No.: A/2930/2016 Year and Section: 5th Year, Section A Date: 02.12.20
4 Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
Certificate This dissertation, titled “Smart Home Trends: Social and Spatial Relationships” by Kartik Sharma, roll number A/2930/2016, was carried out during the Fifth Year, Ninth Semester (2020) B.Arch. Program in the Department of Architecture, under our guidance during September - December 2020. On completion of the report in all aspects and based on the declaration by the candidate above, we provisionally accept this dissertation report and forward the same to the Department of Architecture, School of Planning and Architecture, New Delhi, India.
__________________________ Signature of Guide (PROF. ABHISHEK SORAMPURI)
__________________________ Signature of Coordinator (PROF. PRABHJOT SINGH SUGGA)
6 Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
Acknowledgements The completion of this dissertation was possible with the support and positivity of many that gave me the benefit of several valuable discussions and advices. I would like to thank my generously helpful guide Prof. Abhishek Sorampuri whose invaluable guidance and inputs enabled my research to achieve its objectives and reach a conclusion. I would also like to thank our coordinator Prof. Prabhjot Singh Sugga for their guidance through the semester. I also sincerely acknowledge the encouragement and co-operation rendered by my family, teachers and friends.
8 Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
Figure 1 This is a Visual abstract created to better express the theme and nature of this dissertation | Source: Author
Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
Abstract Keywords – Artificial Intelligence, Automation, Control, Domestication, Privacy, Security & Smart Technology. The house as we know it has constantly been evolving with the advent of new technologies. While a smart home can be the ultimate sustainable and sentient habitat given the trends of development in artificial intelligence enabled technologies, there are concerns of safety and security that arise due to the pervasive nature of smart home technologies. Despite positive growth in the research that is being undertaken in the realm of smart homes, it is majorly that of technical scope and hardly addresses the human interaction with the technological advancements which is essential for the inherent success of the smart home to successfully answer the questions – How do these existing smart devices affect one’s lifestyle? What impact does the increased automation have on the daily routines? How does smart technology in homes affect spaces within and around the house? What makes the smart home smart? The dissertation hence evaluates the available literature and partakes in case studies, interviews and surveys to identify the smart home trends through a sociotechnical and spatial lens. The dissertation uncovers a need for regulated automation and a programmable interface of smart technologies which will embrace the idea of the Smart home being a tool that enables a better life rather than it controlling the life of its residents. There are also findings that suggest there is a consumer inclination towards service based smart technologies that reduces house work, increases connectivity to the house and automates mundane parts of daily routines. This has also yielded in people preferring smart homes but are restricted due to their cost extensive nature of smart technologies. A shift of spatial usage and link with decreasing privacy of spaces has been identified in the research undertaken. The architecture of the house in simpler smart homes is largely the same but in more complex smart homes requires design and sensitive planning.
10 Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
Table of Contents Declaration ................................................................................................................. 4 Certificate ................................................................................................................... 6 Acknowledgements .................................................................................................... 8 Abstract .................................................................................................................... 10 List of Figures ........................................................................................................... 16 List of Tables ............................................................................................................ 20 Chapter 01: Introduction ........................................................................................ 22 1.1 Need for Research.......................................................................................... 22 1.2 Research Question ......................................................................................... 23 1.3 Aim ................................................................................................................. 23 1.4 Objective ......................................................................................................... 24 1.5 Scope and Limitations .................................................................................... 24 1.6 Research Structure and Framework ............................................................... 25 Chapter 02: Literature Review ............................................................................... 28 2.1 Technologies and Related Fields.................................................................... 28 2.2 Smart Homes .................................................................................................. 29 2.2.1 History of Smart Homes Research ........................................................... 29 2.2.2 Definitions ................................................................................................ 29 2.2.3 Components and Related fields ............................................................... 30 2.2.4 Categorisation of Smart Home Research ................................................. 30 2.2.5 Home as a complex entity ........................................................................ 33 2.2.6 Social and Spatial Distinctions, and Trends ............................................. 35 2.2.7 Control, Privacy and Security Concerns ................................................... 37 2.2.8 Role of Smart Home Technologies .......................................................... 38 2.2.9 Commercialisation and Future Proofing ................................................... 39 2.2.10 Domestication of Smart Technologies .................................................... 40 2.2.11 Smart Home Ecosystems ....................................................................... 41 2.2.12 Some Smart Home Trends..................................................................... 43 Chapter 03: Research Methodology ..................................................................... 48 3.1 Introduction ..................................................................................................... 48 3.2 Data collection and Analysis ........................................................................... 48
Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
3.2.1 Literature Review ..................................................................................... 48 3.2.2 Secondary Case Studies .......................................................................... 48 3.2.3 Virtual Primary Case Studies ................................................................... 49 3.2.4 Survey ...................................................................................................... 49 3.3 Analysis Framework ....................................................................................... 50 3.4 Smart Home Trends ....................................................................................... 52 Cumulative Findings and Analysis ........................................................................ 52 3.5 Conclusion and Way Forward ......................................................................... 52 Chapter 04: Data Collection and Analysis ........................................................... 54 4.1 Literature Review ............................................................................................ 54 4.2 Secondary Case Studies ................................................................................ 55 4.2.1 GatorTech Smart House, University of Florida ......................................... 57 4.2.2 Aware Home, Georgia Institute of Technology ......................................... 66 4.2.3 CyberManor Smart Home Experience Centre, California ......................... 74 4.3 Virtual Primary Case Studies .......................................................................... 86 4.3.1 “What’s Inside? Family” Smart Home, USA ............................................. 87 4.3.2 “Hey Ashley Renne” Smart Home, USA ................................................... 96 4.4 Survey .......................................................................................................... 101 4.4.1 Survey Questionnaire Structure ............................................................. 101 4.4.2 Findings and Analysis ............................................................................ 101 Chapter 05: Smart Home Trends ........................................................................ 106 Cumulative Findings and Analysis.......................................................................... 106 5.1 Smart Technology and User Perception ....................................................... 106 5.2 Domestication of Smart Technology ............................................................. 108 5.3 Smart Technology and Social Trends ........................................................... 108 5.4 Smart Technology and Spatial Trends .......................................................... 110 5.5 Smart Technology and the Effect on Architecture of the Home .................... 111 5.6 Essential Character of a Smart Home .......................................................... 113 Chapter 06: Conclusion and Way forward ......................................................... 116 Bibliography ........................................................................................................... 117 Appendix – I ...................................................................................................... 120 Definitions and Relationships ............................................................................. 120 I. Internet of Things (IoT): ................................................................................ 121
Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
II. Information and Communication Technology (ICT): .................................... 123 III. Smart Cities:............................................................................................... 125 IV. Big Data: .................................................................................................... 127 V. Dashboards: ............................................................................................... 128 VI. Artificial Intelligence (AI): ........................................................................... 129 VII. Machine Learning (ML): ............................................................................ 135 VIII. Deep Learning (DL): ................................................................................ 138 IX. Neural Networks: ....................................................................................... 139 i.
Convolutional Neural Networks (CNN): ..................................................... 139
ii. Recurrent Neural Networks (RNN): ........................................................... 140 X. Generative Adversarial Neural Networks (GANs): ...................................... 143 XI. Natural Language Processing (NLP): ........................................................ 145 XII. Algorithm:.................................................................................................. 146 i.
Recursive Algorithm: ................................................................................. 147
ii. Divide and Conquer Algorithm: ................................................................. 147 iii.
Dynamic Programming Algorithm: ......................................................... 147
iv.
Greedy Algorithm: .................................................................................. 147
v.
Brute Force Algorithm: ........................................................................... 147
vi.
Backtracking Algorithm: ......................................................................... 148
XIII. Computational Design: ............................................................................ 150 XIV. Parametric Design: .................................................................................. 151 XV. Data Science: ........................................................................................... 153 XVI. Computer Science: .................................................................................. 155 Respective Relationships with Defined Terms: ............................................... 157 Appendix – II ..................................................................................................... 158 Day in the Life of the Smith Family in Their Smart Connected Home ................. 158 Appendix – III .................................................................................................... 162 Survey Questionnaire and Data Collected .......................................................... 162 Appendix – IV .................................................................................................... 176 Smart Ecosystems .............................................................................................. 176 1. Amazon Alexa ............................................................................................. 176 2. IF This Then That (IFTTT) ........................................................................... 176 3. Google Assistant ......................................................................................... 177 14 Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
5. Home Assistant ........................................................................................... 178 4. SmartThings ................................................................................................ 178 6. Homekit ....................................................................................................... 179 Plagiarism Check .............................................................................................. 180
Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
List of Figures Figure 1 This is a Visual abstract created to better express the theme and nature of this dissertation | Source: Author ..................................................................................................................................................................... 9 Figure 2 Research Structure | Source: Author ...................................................................................................... 25 Figure 3 Research Framework | Source: Author ................................................................................................... 26 Figure 4 Set of Nine Parameters for Smart Home Research | (Wilson, Hargreaves and Hauxwell-Baldwin, 2014) .............................................................................................................................................................................. 31 Figure 5 Types of control depending on the interactions between Smart Home Technologies, users and Domestic life | (Hargreaves, Wilson and Hauxwell-Baldwin, no date) ................................................................. 40 Figure 6 Positive and Negative feedback loops between different forms of control shape the domestication or rejection of smart home technologies | (Hargreaves, Wilson and Hauxwell-Baldwin, no date) .......................... 41 Figure 7 Voice Assistant Trends | (Comcast, 2018) .............................................................................................. 42 Figure 8 Regional Market Growth Rates of Smart Home Technologies | (Mordor Intelligence, 2019) ................ 44 Figure 9 Market Penetration of Smart Home Technologies | Source: Statista, 2019 ........................................... 44 Figure 10 Analysis Framework | Source: Author .................................................................................................. 51 Figure 11 Case Study 1: The GatorTech Smart House | ........................................................................................ 55 Figure 12 Case Study 2: The Aware Home | ......................................................................................................... 55 Figure 13 Case Study 3: CyberManor Smart Home Experience Centre, California | ............................................. 56 Figure 14 The GatorTech Smart House | .............................................................................................................. 57 Figure 15 The GatorTech Smart House Ground Floor Plan | Source: Author ....................................................... 58 Figure 16 GatorTech Smart House Axonometric with Smart Components | (Helal et al., 2005) .......................... 60 Figure 17 Physical Privacy, GatorTech Smart House | Source: Author ................................................................. 63 Figure 18 Digital Privacy, GatorTech Smart House | Source: Author ................................................................... 63 Figure 19 Sensor Platform Architecture. Modular Design for Flexible configurations | (Helal et al., 2005)......... 65 Figure 20 The Aware Home Front Elevation | (Kidd et al., 1999) ......................................................................... 66 Figure 21 The Aware Home Ground Floor Plan | Source: Author ......................................................................... 67 Figure 22 The Aware Home Exploded Axonometric | Source: http://www.awarehome.gatech.edu/sites/default/files/documents/AwareHome_slides.pdf ............................ 68 Figure 23 Physical Privacy map, The Aware Home | Source: Author .................................................................... 71 Figure 24 Digital Privacy map, The Aware Home | Source: Author ...................................................................... 72 Figure 25 CyberManor Smart Home Experience Centre, California | ................................................................... 74 Figure 26 CyberManor Smart Home Experience Centre Floor Plan | Source: Author ........................................... 76 Figure 27 CyberManor Smart Home Experience Centre Axonometric with Smart Components | ........................ 79 Figure 28 Physical Privacy, CyberManor Smart Home Exhibition Centre | Source: Author .................................. 82 Figure 29 Digital Privacy, CyberManor Smart Home Exhibition Centre | Source: Author .................................... 83 Figure 30 "What's Inside? Family" Smart House Ariel View | ............................................................................... 86 Figure 31 "Hey Ashley Renne" Smart Home | ....................................................................................................... 86 Figure 32 "What's Inside? Family" Smart House Ariel View | Source: https://www.youtube.com/watch?v=Bx9hPzTxVqE&list=PL3BVMyyYBBREmgMWcnm6R9gZlXAhnsxv&index=28 ........................................................................................................................................................ 87 Figure 33 "What’s Inside? Family" Ground Floor Plan | Source: Author .............................................................. 88 Figure 34 The Wiring at the Main Server Room Level | Source: https://www.youtube.com/watch?v=UCeJ4e7hc40&list=PL3BVMyyYBBREmgMWcnm6R9gZlXAhn-sxv&index=7 .............................................................................................................................................................................. 90 Figure 35 Physical Privacy, “What’s Inside? Family" Smart Home | Source: Author ............................................ 93 Figure 36 Digital Privacy, "What's Inside? Family" Smart Home | Source: Author .............................................. 94
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School of Planning and Architecture, New Delhi
Figure 37 "Hey Ashley Renne" Smart Home Backyard View | Source: https://www.youtube.com/watch?v=9u9kqhHC6Ok&list=PLyVlgZDTxUQlL7shIOPIPTnq5yAianN5N&index=8.. 96 Figure 38 Survey Respondents' Geographic Locations | Source: Author ............................................................ 102 Figure 39 Cumulative Data Findings and Analysis points | Source: Author ........................................................ 106 Figure 40 “Just because it can be, doesn’t mean it should be.” Keeping this in mind, rate the following features in your home from 1-5 with 1 least likely to and 5 most likely to in your opinion be controlled remotely from another device say, your smartphone? | Source: Survey Findings, Appendix – III, Author ................................. 107 Figure 41 General relation of privacy and types of Smart home Technology Scenarios | Source: Author ......... 109 Figure 42 Traditional Generic Spatial Segregation | Source: Author .................................................................. 110 Figure 43 Digital Smart Interventions transforming private spaces to less private as a user comfort trade-off. | Source: Author .................................................................................................................................................... 110 Figure 44 IoT and Smart Technologies Dependencies | Source: Author ............................................................. 112 Figure 45 Smart Home Types identified on the nature of interaction of technology and architecture | Source: Author ................................................................................................................................................................. 113 Figure 46 Characteristics of IoT | Source: Labmanager ...................................................................................... 121 Figure 47 Internet of things examples | Source: Edureka ................................................................................... 123 Figure 48 The components of ICT | Source: Europeyou ...................................................................................... 123 Figure 49 The main components of a smart city | (Sadiku et al., 2016) ............................................................. 126 Figure 50 Big Data timeline with AI integration | Source: House of Bots ........................................................... 127 Figure 51 Branches of Artificial Intelligence | (GN, 2019) .................................................................................. 132 Figure 52 AI generated shape-adaptive floorplans | (Chaillou, 2019) ................................................................ 133 Figure 53 Machine Learning program data processing | Source: Edureka ........................................................ 136 Figure 54 Machine Learning Venn Diagram | Source: https://vas3k.com/blog/machine_learning/ ................. 137 Figure 55 Deep Learning Neural Network | Source: Edureka ............................................................................. 138 Figure 56 Deep Learning Neural Network Application | Source: https://vas3k.com/blog/machine_learning/ . 138 Figure 57 Deep Learning Neural Network Classifications | Source: https://vas3k.com/blog/machine_learning/ ............................................................................................................................................................................ 139 Figure 58 Convolutional Neural Networks | Source: https://vas3k.com/blog/machine_learning/.................... 140 Figure 59 Recurrent Neural Networks | Source: https://vas3k.com/blog/machine_learning/ .......................... 141 Figure 60 Neural Network types | Source: asimovinstitue.org ........................................................................... 142 Figure 61 Typical GAN Architecture | (Chaillou, 2019) ....................................................................................... 143 Figure 62 Training set (left) with Generated scheme (right) | (Chaillou, 2019).................................................. 144 Figure 63 City building Facades using GANs | (Kelly et al., 2019) ...................................................................... 145 Figure 64 Evolution of NLP | Source: Xenonstack ............................................................................................... 146 Figure 65 Morpheus Hotel | Source: Zaha Hadid Architects............................................................................... 149 Figure 66 Some CAD-CAM Softwares for Computational Design | Source: ArchSupply .................................... 151 Figure 67 Parametric form of Heydar Aliyev Centre | Source: Zaha Hadid Architects ....................................... 153 Figure 68 Artificial Intelligence and other field overlaps | Source: https://medium.com/ai-in-plain-english/datascience-vs-artificial-intelligence-vs-machine-learning-vs-deep-learning-50d3718d51e5 .................................. 154 Figure 69 Data Science | Source: https://dimensionless.in/role-of-computer-science-in-data-science-world/ . 154 Figure 70 Computer Science | Source: https://dimensionless.in/role-of-computer-science-in-data-science-world/ ............................................................................................................................................................................ 155 Figure 71 Site analysis using computer science | Source: DepthMapX .............................................................. 156 Figure 72 Country of origin | Source: Survey, Author ......................................................................................... 162 Figure 73 Field of Work | Source: Survey, Author ............................................................................................... 163 Figure 74 Age | Source: Survey, Author .............................................................................................................. 163 Figure 75 Number of People living with | Source: Survey, Author ...................................................................... 164 Figure 76 Number of people using smart phones | Source: Survey, Author ....................................................... 164
Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
Figure 77 Outlook on Smart Technologies | Source: Survey, Author .................................................................. 165 Figure 78 Buying Preference for Smart Home Technologies | Source: Survey, Author ....................................... 166 Figure 79 Buying Preferences by Age | Source: Survey, Author .......................................................................... 167 Figure 80 Preference of remotely controlling functions | Source: Survey, Author .............................................. 168 Figure 81 Preference of Remotely controlling functions by age | Source: Survey, Author ................................. 169 Figure 82 Preference of function automation | Source: Survey, Author ............................................................. 170 Figure 83 Average points for preference of function automation by age | Source: Survey, Author ................... 171 Figure 84 Purchasing preference when cost isn’t an issue | Source: Survey, Author.......................................... 172 Figure 85 Average points for Purchasing preference when cost isn’t an issue by age | Source: Survey, Author 173 Figure 86 Control division in a smart home | Source: Survey, Author ................................................................ 174 Figure 87 Primary reason for not purchasing smart home technologies | Source: Survey, Author .................... 174 Figure 88 Considering upgradation to smart homes | Source: Survey, Author .................................................. 175 Figure 89 Amazon Alexa based Smart Ecosystem | Source: https://www.youtube.com/watch?v=-_vtoUmkot4 ............................................................................................................................................................................ 176 Figure 90 IF This Then That based Smart Ecosystem | Source: https://www.youtube.com/watch?v=_vtoUmkot4 ........................................................................................................................................................ 177 Figure 91 Google Assistant based Smart Ecosystem | Source: https://www.youtube.com/watch?v=-_vtoUmkot4 ............................................................................................................................................................................ 177 Figure 92 Home Assistant based Smart Ecosystem | Source: https://www.youtube.com/watch?v=-_vtoUmkot4 ............................................................................................................................................................................ 178 Figure 93 Smart Things based Smart Ecosystem | Source: https://www.youtube.com/watch?v=-_vtoUmkot4 ............................................................................................................................................................................ 179 Figure 94 Smart Things based Smart Ecosystem | Source: https://www.youtube.com/watch?v=-_vtoUmkot4 ............................................................................................................................................................................ 179
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School of Planning and Architecture, New Delhi
Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
List of Tables Table 1 Various views of the Smart Home | (Wilson, Hargreaves and Hauxwell-Baldwin, 2014) ........................ 31 Table 2 STOF Framework | (Solaimani, Keijzer-Broers and Bouwman, 2015) ...................................................... 32 Table 3 Smart Home Technologies Based on Programming Types | (Wilson, Hargreaves and Hauxwell-Baldwin, 2014) ..................................................................................................................................................................... 34 Table 4 ICT and Spatial Categorisation based on Function |(Wilson, Hargreaves and Hauxwell-Baldwin, 2014) 36 Table 5 Smart Home Ecosystems | Source: Author .............................................................................................. 42 Table 6 Case Study Parameter Analysis Derivation Table | Source: Author ......................................................... 49 Table 7 Smart Technology, Function and Location, GatorTech Smart House | Source: Author ........................... 59 Table 8 Media Defined Zones, GatorTech Smart House | Source: Author ............................................................ 61 Table 9 Inputs, Outputs and Interface Engagements, GatorTech Smart House | Source: Author ........................ 62 Table 10 Chores, Smart technologies and Potential Users, GatorTech Smart House | Source: Author ................ 64 Table 11 Smart Technology, Function and Location, The Aware Home | Source: Author .................................... 69 Table 12 Media Defined Zones, The Aware Home | Source: Author .................................................................... 70 Table 13 Inputs, Outputs and Interface Engagements, The Aware Home | Source: Author ................................ 70 Table 14 Chores, Smart technologies and Potential Users, The Aware Home | Source: Author .......................... 73 Table 15 Smart Technology, Function and Location, The CyberManor Smart Home Experience Centre | Source: Author ................................................................................................................................................................... 77 Table 16 Smart Technology, Function and Location, The CyberManor Smart Home Experience Centre (continued) | Source: Author ................................................................................................................................ 78 Table 17 Media Defined Zones, CyberManor Smart Home Experience Centre | Source: Author ......................... 80 Table 18 Inputs, Outputs and Interface Engagements, CyberManor Smart Home Exhibition Centre | Source: Author ................................................................................................................................................................... 81 Table 19 Effect on Chores and House Activities, CyberManor Smart Home Exhibition Centre | Source: Author . 84 Table 20 Smart Technology, Function and Location, "What’s Inside? Family" Smart Home | Source: Author .... 89 Table 21 Media Defined Zones, "What's Inside? Family" Smart Home | Source: Author ..................................... 91 Table 22 Inputs, Outputs and Interface Engagements, "What's Inside? Family" Smart Home | Source: Author . 92 Table 23 Effect on Chores and House Activities, "What's Inside? Family" Smart Home | Source: Author ........... 95 Table 24 Smart Technology, Function and Location, "Hey Ashley Renne" Smart House | Source: Author ........... 97 Table 25 Media Defined Zones, "Hey Ashley Renne" Smart Home | Source: Author ........................................... 98 Table 26 Inputs, Outputs and Interface Engagements, "Hey Ashley Renne" Smart Home | Source: Author ....... 99 Table 27 Effect on Chores and House Activities, "Hey Ashley Renne" Smart Home | Source: Author ................ 100 Table 28 General Survey Questionnaire Structure | Source: Author .................................................................. 101 Table 29 Outline of the ICT impact matrix | (Moum, 2008) ............................................................................... 125 Table 30 Cross relationships table for Background study | Source: Author ....................................................... 157 Table 31 Average points by age for buying preference | Source: Survey, Author .............................................. 166 Table 32 Average points by Age for remotely controlling preferences | Source: Survey, Author ....................... 168 Table 33 Average points for Preference of function automation by age | Source: Survey, Author .................... 170 Table 34 Average points for Purchasing preference when cost isn’t an issue by age | Source: Survey, Author . 172
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School of Planning and Architecture, New Delhi
Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
Chapter 01: Introduction 1.1 Need for Research The house as we know it has constantly been evolving with the advent of new technologies. The idea of infusion of smart technologies and digitisation in the home is the “Third round of sociotechnical change” with the first being the introduction of electricity in the household (1890s) and the second being that of power and heating (1940s-1970s). Just as the change in spatial terminologies and roles back in these instances, Schwartz-Cowan in his work of Industrialisation of the Home, alerts that there is a scope of destabilisation or reinforcement of spatial and household roles. The smart home is a typology of residential architecture that has risen in the advent of data and computational technologies. The smart home is the culmination of the aspirations for a better living standard borrowing from the fields of architecture, engineering, data and computer sciences. It is envisioned as the product of application of Information and Communication Technologies over a network of such devices connected via the Internet of Things, hence forming an ecosystem within the habitat of the user and even connecting the user virtually to locations beyond their homes. While the aim of a smart home is in sync with comfortable, efficient and a largely automated lifestyle, there are still certain gaps that hinder the realisation of the smart home (Solaimani, KeijzerBroers and Bouwman, 2015). While a smart home can be the ultimate sustainable and sentient habitat given the trends of development in artificial intelligence enabled technology, there are concerns of safety and security that arise due to the pervasive nature of smart home technologies (Wilson, Hargreaves and Hauxwell-Baldwin, 2014). It is hence essential to identify the shortcomings by understanding the gaps in the research related to smart homes. Despite positive growth in the research that is being undertaken in the realm of smart homes, it is majorly that of technical scope and hardly addresses the human interaction with the technological advancements which is essential for the inherent success of the smart technology (Solaimani, Keijzer-Broers and Bouwman, 2015). As the pace of development of smart technologies picks up with devices such as Alexa, Google Nest home et cetera, it is important to understand the implications of these smart devices and sensors that monitor and automate the
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building services such as electricity, water, ventilation, et cetera. How do these existing smart devices affect one’s lifestyle? What impact does the increased automation have on the daily routines? How does smart technology in homes affect spaces within and around the house? What makes the smart home smart? All such questions require a degree of depth in their answers which will be elaborated upon through this dissertation.
1.2 Research Question How is the increasing sentiency within the realm of residential architecture shaping the perception, usage and typology of spaces, as well as the architectural language of the Smart Home? 1. How is the smart home different from the traditional typologies of homes prevalent today? 2. How will the inhabitants interact with the smart ecosystem and services? How will this affect the current style, perception and hierarchy of spaces within the house? 3. How are smart technologies and services shaping the architecture design and utilitarian aspects of spaces in a home? What would be the defining characters and features of a sentient home design? 4. While the smart home becomes a smarter home, how will it stay universally accessible and inclusive to all inhabitants of the house? 5. Is a smart home essential in the current global climate? What are the essential and non-essential technologies and elements within the smart home?
1.3 Aim This dissertation will scrutinise the smart home typology through the social and spatial lens by understanding and analysing the interactions of the users and their built environments with smart home technologies and services. Also, it will attempt to address any identifiable shift in the perception, usage and typologies of spaces in residential architecture while studying the increase in technological sentiency of the respective built environments.
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1.4 Objective This dissertation will majorly partake in socio-technical research by understanding and analysing the following to achieve the prescribed aim – 1. Smart Technologies which focus on Network Technology, communication and control, sensor technology and artificial intelligence; 2. Design, Development and Deployment of smart technology which focuses on topics such as Usefulness, Sense of control, Accessibility, Reliability, Privacy and Security. 3. Topics of user lifestyle, user spending power and trends;
1.5 Scope and Limitations The scope of the research is of socio-technical nature as it focuses majorly on the user’s interaction with the prevalent smart technology within their homes. The research will address this by studying these interactions for a general family structure and housing typology through a dual lens of social and spatial nature. There will be a focus on identifying trends for designing an effective smart home. Due to the COVID-19 pandemic, the scope of achieving the aims of this dissertation will largely depend on the online resources. Given the rapid development and constant innovation within this field, the research might only be capable of addressing the current trends within this field with only restricted future projections. Hence, this will call for future branches of research.
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1.6 Research Structure and Framework The Research framework outlines the structure of the dissertation in an attempt to carry out successful research on the topic at hand while effectively addressing the research question and satisfying the objectives.
Figure 2 Research Structure | Source: Author
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Figure 3 Research Framework | Source: Author
26 Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
Smart Home Trends: Social and Spatial Relationships
School of Planning and Architecture, New Delhi
Chapter 02: Literature Review Given the online nature of the dissertation due to the COVID-19 Pandemic, the literature review as a research tool plays a major role in data collection and formulating the analysis framework, as explained in the Research Methodology chapter.
2.1 Technologies and Related Fields The research at hand being technically grounded requires to have a basic understanding of terms and processes which are constantly innovating the field and practice of architecture. To do so, an interdisciplinary study of technical terms drawing from the literature of Internet of Things, Big Data, Computer and Data Sciences, and Artificial Intelligence was the ideal first step. This interdisciplinary study included the following topics, their relationship with each other and their relevant application in the realm of architecture – I. Internet of Things (IoT); II. Information and Communication Technology (ICT); III. Smart Cities; IV. Big Data; V. Dash Boards; VI. Artificial Intelligence (AI); VII. Machine Learning (ML); VIII. Deep Learning (DL); IX. Neural Networks; X. Generative Adversarial Neural Networks (GANs); XI. Natural Language Processing (NLP); XII. Algorithm; XIII. Computational Design; XIV. Parametric Design; XV. Data Science; XVI. Computer science; (Refer to Appendix – I) A table is drawn from the review of these relevant literature studies which is utilised in understanding the interdependencies and overlapping fields of interest within the 28 Smart Home Trends: Social and Spatial Relationships
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realm of the studied data. The smart home is a typology that is constantly driven by technological advancements that contribute to the increased comfort and betterment of life associated with the smart home.
2.2 Smart Homes While not only at the urban level in smart cities, the user-centric approach is essential to create inclusive designs with technological considerations at all scales of potential human participation. The need for such an inclusive approach is further required in the context of a smart home as despite a smaller scale, it still has to address the various expectations and requirements (social, emotional et cetera) of its residents.
2.2.1 History of Smart Homes Research The official use of the term “smart homes” was first used by the American Association of House Builders in 1984 but the smart homes as a phenomenon were observed in use since the early 1960s by hobbyists (Harper, 2003). A systemic analysis and key challenges for the Smart Home and its users reveals the need for user-focused research within its extents (Wilson, Hargreaves and Hauxwell-Baldwin, 2014). The authors of this paper highlight the abundance of smart home literature focussing on technology, which is growing exponentially while the literature on the users who utilise this typology and how they do so is relatively missing. Hence in the paper, “Smart Homes and Their Users”, the authors attempt to answer the questions and identify relevant fields of exploration for a user-centric approach for designing smart homes. With the dependency on Internet of Things and smart home appliances, it is also essential to understand the users’ relation to technology.
2.2.2 Definitions The paper by Wilson et al. defines the Smart Homes as, “Information and communication technologies (ICTs) distributed throughout rooms, devices and systems (lighting, heating, ventilation) relaying information to users and feeding back user or automated commands to manage the domestic environment.” (Wilson, Hargreaves and Hauxwell-Baldwin, 2014). The challenge identified was to recognise the issues as parts of a broader effort to redefine the notion of ‘‘smart’’ itself, seeing it as emerging within the users’ everyday lives and in the ways, technologies are used
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in the home, not as something that resides in technologies themselves. To this, Harper argues that it is the interactive technologies that makes the smart home smart (Harper, 2003). Solaimani et al. adopt the term Smart Living so as to address the general living conditions that are enhanced by smart technologies). Furthermore, the term smart home is argued to be restrictive as the presence of smart technology and devices extends the realm of the home well beyond its walls by connecting the users to the outside world creating an intelligent environment (in addition to the automation of the house) (Solaimani, Keijzer-Broers and Bouwman, 2015).
2.2.3 Components and Related fields The field of Smart living has been identified as a multidisciplinary domain including several disciples (e.g. robotics, artificial intelligence, service engineering, mobile computing), while various perspectives of users, systems, organisation et cetera are considered to identify a myriad of issues of design and beyond (e.g. usability, affordability, privacy and security, interoperability and standardisation, collaboration) (Solaimani, Keijzer-Broers and Bouwman, 2015). Harper also mentions the diverse and multidisciplinary research fields to be in conjunction with smart home, such as sociology, ethnography, feminist analysis, human-computer interaction (HCI), computer supported cooperative workflow (CSCW), artificial intelligence, building research and healthcare (Harper, 2003).
2.2.4 Categorisation of Smart Home Research The research found that the barriers perceived by users for the adoption of smart home lacked the clear sense of smart home benefits (Wilson, Hargreaves and HauxwellBaldwin, 2014). Wilson et al., upon conducting a systemic literature review of onehundred-and-fifty peer reviewed papers of various fields, identified a set of nine themes, which they organised in three sets of three.
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Figure 4 Set of Nine Parameters for Smart Home Research | (Wilson, Hargreaves and Hauxwell-Baldwin, 2014)
All these categories are essential to the scope of this dissertation and hence have been critically looked into while focusing on the sociotechnical implications. The authors use these groups to identify the presence or absence of cross-cutting relationships between the respective groups to formulate an organising framework. This organising framework is then further utilised to gather a deeper understanding of the major concerns of privacy and control. “…spaces not to be an inevitable outcome of assumed functional benefits.” This view is put forward by the authors in order to ensure “co-evolving” of society alongside technology.
Table 1 Various views of the Smart Home | (Wilson, Hargreaves and Hauxwell-Baldwin, 2014)
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On similar lines, the paper, “What we do and don’t know about the Smart Home”, attempts to analyse the available reliable smart home literature in an effort to realise the factors for successful large-scale commercialisation of the smart home (Solaimani, Keijzer-Broers and Bouwman, 2015). The authors, Solaimani et al., utilise a business model framework, STOF (Service, Technology, Organisation, and Finance) to identify future challenges in the smart home typology (Solaimani, Keijzer-Broers and Bouwman, 2015).
Table 2 STOF Framework | (Solaimani, Keijzer-Broers and Bouwman, 2015)
From various perspectives such as media companies, security providers, IT service providers and healthcare providers, the concept of smart living recognised the importance of improvement of living standards. With such a widespread scope, this research also identified literature and classified them according to the STOF framework to analyse the implications of such classifications. Just as Wilson et al., it is identified that Technology oriented research dominates the smart living research while the share of non-technical research of Service, Organisation and Finance remains low. This is reasoned by Solaimani et al. to be caused due to the smart living domain being a largely technical field and the projects being conducted within the R&D environment. The authors state that the relative absence of socio-technical, socioorganisational and economic studies could be since the field of Smart Living is still in its exploratory phase.
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Their research paper considers peer reviewed research papers, conference proceedings and book chapters with more than fifty citations, from reputed sources and classifies them according to the STOF framework for further analysis. Solaimani et al. have classified literature according to this framework as an attempt to give way for future research in the domain of smart living. In the concluding statements, just as Wilson et al., Solaimani et al. emphasise on the need for attention towards a cumulative research of social, technological, organisational, entrepreneurial and economical aspects. While considering the stated views of the smart homes, a list of potential users of the smart homes can be realised which includes the elderly, the vulnerable householders, mental and chronic health patients, rational energy users, technophiles and differentiated families. There is also emphasis on the complexity of the family structure as a factor that needs to be addressed in accordance to identifying the family as a plural and diverse entity consisting of diverse individuals with diverse aspirations and needs (Wilson, Hargreaves and Hauxwell-Baldwin, 2014).
2.2.5 Home as a complex entity Smart home technologies to date assumes everyday life as specific, repetitive and relatively predictable while on the contrary, it is organic, opportunistic and improvisational. While machine learning models might be able to replicate and increase predictability of daily situations to a certain extent, the variables involved in the algorithm require an in-depth multidisciplinary approach (Wilson, Hargreaves and Hauxwell-Baldwin, 2014). Emotions and moods of the users add to the complexity of the algorithmic approach and even if a successful machine learning program is developed, it will take away the control from the users, of their respective lives which is one of the major concerns of the smart home technologies. The emotionally charged environments, moods, routines, values, practices, culture, aspirations and memories of the diverse set of inhabitants of the smart home, cumulates the important considerations that drives the identity of the built. Hence, the character of the user-technology interface is essential and can be achieved in a number of ways.
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Table 3 Smart Home Technologies Based on Programming Types | (Wilson, Hargreaves and Hauxwell-Baldwin, 2014)
While in the beginning Wilson et al. address the nine identified themes exclusively, in conclusion, an attempt to identify connections across these themes to visualise the gaps in the available literature, limitations of the current research and the shortcomings of the smart homes is made. A general trend of disconnect of the sociotechnical with the functional and instrumental is observed. The social scientific research is hence of importance as it explores the important social concepts of house as a complex place and domestication of smart technology while the technical scientific research (functional and instrumental) focuses on hardware and software interfaces and user-technology development. The authors strongly endorse the need for socio-technical research as overlooking the complexity of the house and realising the smart home as simply a smart technology retrofit might lead to discomfort, loss of privacy and control for the inhabitants. With the development of artificial intelligence and other smart technologies, the concern for privacy and automation is paramount in the current society. Systems are required to increasingly understand and adapt to the users’ changing lifestyles, habits and routines. While advancement in the field of Artificial Intelligence is an important way forward, there is also a need for smart user interface designs for effective and easy calibrations and interactions (Farshidi, no date). Just as traditional homes, smart homes should too be designed for the plural and diverse family but still there is a need of hierarchy of control within the household as Harper mentions that the adults should exclusively be able to regulate and control the household commands while the children should not have such access (Harper, 2003). Harper argues that it is eminently necessary to understand the complexity of the home which would not be achievable via a singular perspective of sociological
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approach, but rather requires understanding of family economics, morals, culture, and traditions (Harper, 2003). The smart technology throughout the house can have variable usage. While some can be used often, some can be utilised not that frequently. There are also cases of technologies that include active or even passive modes of participation from the users. With the advent of artificial intelligence and the ever-growing field of engineering and data sciences, the smart home holds the capacity to automate all home tasks and functions which can be based on user habits. Despite this great potential, Wilson et al. put forth a number of reasons for regulated automation and rather supports the idea of a programmable interface of smart home technologies. They embrace the idea of the Smart home being a tool that enables a better life rather than it controlling the life of its users (Wilson, Hargreaves and Hauxwell-Baldwin, 2014). Harper in his research elaborates on the difference in functioning and structuring of a family from an organisation as a reason for low acceptance of smart homes as he argues that there is availability of and accessibility to technical support in case of a smart workplace, which is not possible to the same extent in the case of a smart home (Harper, 2003). This is largely correct but now, this gap has reduced since there is better user interface design and experience and better accessibility to technical support than it was in 2003.
2.2.6 Social and Spatial Distinctions, and Trends In order to understand the trends within a smart home, it is also essential to realise the house as both – a Consumer and a Producer (Hamill, 2003). Hamill elaborates on this by explaining the meaning of optimisation in the house which he translates to the utility for and welfare of the residents. The author identifies the trend of Capital replacing Labour within the household since capital enables the purchase of domestic technology such as washing machines, dishwashers et cetera replacing and reducing the manual labour input. This trend results in increase in leisure time for the residents. Hamill further attempts to establish a link between the consumer purchasing habits for time-saver technologies (e.g. washing machine) and time-user technologies (e.g. television), and the free time utilisation trends of the users. To this it is discovered that
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in the UK, the leisure goods and services are the largest percentage of expenditure within a household since 1998-1999 to 2003 i.e., 18%. This finding is reasoned for and concluded by the author for he argues that people prefer to spend capital on entertainment rather than spending it on service technologies as in the latter case, there would be an excess of free time with reduced capital available to be spent for recreational purposes (Hamill, 2003). Emphasis is laid on the need for ethnographic and sociological research on ICT in the domestic context as it highlights the presence of functionally and interpretively distinct spaces by the occupants. Building on this, the research has identified a base categorisation of ICT within the smart homes based on their functions (Wilson, Hargreaves and Hauxwell-Baldwin, 2014).
Table 4 ICT and Spatial Categorisation based on Function |(Wilson, Hargreaves and Hauxwell-Baldwin, 2014)
This classification further helps to distinguish the frequency of usage by various members of the family. While devices such as the fans and lights could be programmed and controlled via remotes and/or can be automated, the users of the home found such functions overly complicated which could be resolved using simple, pre-existing technology such as mechanical regulators and switched for fans and lights (Harper, 2003). This points towards a trend of preference of users choosing simple over smart. “The location of the interactive device needs to be related to the patterns of space usage in the home.” There is a focus on blurring existing barriers and thresholds within a home using smart technology such that similar tasks and chores within the home are clubbed together for efficient and easy functioning, such as management of groceries and ordering food, directly from within the kitchen.
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(Harper, 2003). This is currently possible via the smart refrigerator technology and more widely via the utilisation of smart phones. Harper also mentions the preference of voice commands to ease daily tasks which are currently possible to an extent via Natural Language Processing enabled programs and devices such as Google Nest and Alexa. New technologies have a tendency to initially disrupt lifestyles but with time as users get familiar and accustomed to it, these technologies blur with the background. This is well explained by Hamill with the example of a television in the UK where it disrupted not only the household regimes, but also affected cinema attendances and workplace routines when it was initially introduced. As the television became normalised, cheap and easily accessible, its existence became blurred in the background of peoples’ lives and people return to their original activities (Hamill, 2003).
2.2.7 Control, Privacy and Security Concerns Apart from the inherent benefits of betterment of users’ lives, the research explores the reasons why the smart home is not widely adopted as a typology. This can be traced to certain social barriers which need to be successfully addressed such as – Loss of control, reliability, trust, high cost, irrelevance pertaining to such technologies. This requires extensive research, development, testing and trialling before the commercialisation of such smart home technologies. Proper and efficient algorithms and diligently calibrated smart sensors are required to increase reliability and trust while an intuitive and interactive interface can increase the sense of control and familiarity. Flexible standards and operations increase the interoperability and respective compatibility of devices which increases the functional reliability and manageability (Wilson, Hargreaves and Hauxwell-Baldwin, 2014). In principle, the reliability and control increase with user empowerment and user-friendliness of technology. “Smart homes should never be unpredictable or do unpredictable things.” (Wilson, Hargreaves and Hauxwell-Baldwin, 2014). There is a large public concern about smart homes and technologies being increasingly intrusive in nature. The largely general perception is since the smart home
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monitors the domestic environment and records data of intimate natures. The approaches that deal to ease this concern have been identified by the authors (Wilson, Hargreaves and Hauxwell-Baldwin, 2014) – x Clearly defined and guaranteed levels of privacy; x Safety and security of technologies; x Accountability and Transparency on the levels of service providers, regulating bodies et cetera;
2.2.8 Role of Smart Home Technologies The scope of smart technology is to provide and improve comfort, convenience, security and entertainment. The smart home typology is explored in the healthcare and social care sectors often as the technology, in all its various forms enables a state of well-being and care for the users who require it and increases ease of use for the caretakers (Wilson, Hargreaves and Hauxwell-Baldwin, 2014). The main aim of Smart Home Technologies should be to aid the existing life and lifestyles and not to replace it. Wilson et al. also identify and recognise the ‘smartness’ in the traditional domestic environments in the form of utilisation of various surfaces et cetera for communication which is similar to the idea of communication in the smart homes using screens on the surfaces, the difference being the additional real-time computational power available in the latter. “Introducing new technology changes service expectations and user patterns which in turn change the subsequent demands, wants and needs for the new technology and the resources they consume, normalising ever more the way of living.” (Wilson, Hargreaves and Hauxwell-Baldwin, 2014). This, stated in their paper translates to the trend of normalisation of smart technologies as their introduction accustoms the users’ expectations overtime while the processes of their functioning and servicing also familiarise the users with the idea of new technologies. The authors also call for identification of essential and non-essential smart technologies which would affect the quality of life of the users in respectively different ways. Smart Home technologies need to be developed with a sensitive understanding and respectfulness of the human emotions by realising that humans value time, roles and relationships in the domestic life more and does not prefer losing control over the 38 Smart Home Trends: Social and Spatial Relationships
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emotional aspects and connections in their lives via processes like automation (Wilson, Hargreaves and Hauxwell-Baldwin, 2014). The User centric design of smart home technologies is essential. Therefore, the belief put forth in the conclusion of the paper is such that the smart home should inherently aim to control the busy, demanding and chaotic domestic life rather than controlling the user itself (Wilson, Hargreaves and Hauxwell-Baldwin, 2014).
2.2.9 Commercialisation and Future Proofing There is a need of “future proofing” the smart home which will again result in increasing the reliability. This can be achieved by providing a modular, flexible and retrospectively compatible design of technology and its surroundings. Wilson et al. elaborate further for the need of future proofing as it would safeguard against changes in regulatory frameworks, standards and policy objectives (especially in the energy domain). This calls for a robust software design and a distinct functional scope for such technologies such that it does not constantly change the structure of the household design with every update (Wilson, Hargreaves and Hauxwell-Baldwin, 2014). Solaimani et al. elaborate on the perspectives of technology and user-centrism while identifying the importance of Critical Design Issues (CDI) which are essential for the development and provision of Smart Living concepts. The adoption and consideration of these relevant perspectives and CDIs by the practitioners and researchers cumulate the sustainability of the product under development (Solaimani, Keijzer-Broers and Bouwman, 2015). On similar lines, Harper states that it is the extensive push of the technological side rather than focussing on the user centric approach to smart homes, clubbed with high costs of smart technologies and the old housing stock that restricts the smart homes from being a hit (Harper, 2003).
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2.2.10 Domestication of Smart Technologies Hamill quoted Gronau (1977) which largely divides a person’ day into four main categories – 1. Market work, which is paid; 2. Personal and biological maintenance, e.g., eating and sleeping; 3. Household work, which a third party could be paid to do; 4. Leisure, where a third-party production is conceptually impossible; All these mentioned tasks during the day are affected by the introduction of domestic technologies (Hamill, 2003).
Figure 5 Types of control depending on the interactions between Smart Home Technologies, users and Domestic life | (Hargreaves, Wilson and Hauxwell-Baldwin, no date)
Domestication is defined in terms of Artefactual, Perpetual and Relational Controls (Hargreaves, Wilson and Hauxwell-Baldwin, no date). As highlighted by the authors, the positive feedback loop of these controls as shown in the following diagrams is what determines the Domestication or Rejection of smart Technologies. Hence, reliability, useability, accessibility and ease of control are the values that ensure the domestication of smart technologies.
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Figure 6 Positive and Negative feedback loops between different forms of control shape the domestication or rejection of smart home technologies | (Hargreaves, Wilson and Hauxwell-Baldwin, no date)
2.2.11 Smart Home Ecosystems In the current global climate where smart home technology is rapidly gaining ubiquity, there exist a number of options for building a smart ecosystem based on needs and requirements. In 2018, there had been increase in the options and opportunity of home assistants with voice command activation. It has been found in a report published in 2018 that 72% of people who own voice-activated speakers use them as part of their everyday routine (Comcast, 2018). This indicates to the bridging of the gap of accessibility and user friendliness which is now enabling users to inculcate smart technologies in their daily routines and lifestyles. This Smart voice enabled task enabled lifestyle trend shows that the user utilises the voice assistants largely at home and in the car while within the home, the current location of these assistants lies largely in the living room (Comcast, 2018). While the same voice assistants are occurring in various locations, the tasks
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they are asked to accomplish vary based on their locations and the respective functions of those locations.
Figure 7 Voice Assistant Trends | (Comcast, 2018)
The Smart Ecosystems push each other as they try to outpace each other as the “best smart alternative” which in turn is driving the growth of the smart home industry, exactly as healthy competition grows any other business field. The following widely used smart home ecosystem alternatives have been identified. A number of services are compatible with a number of these mentioned ecosystems, either exclusively or even more so, commonly over multiple ecosystems (Refer to Appendix – IV).
Table 5 Smart Home Ecosystems | Source: Author
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2.2.12 Some Smart Home Trends While understanding the market trends, a report published by Mordor Intelligence shows that the significant contributor to the growth of the smart home market is the smart HVAC systems (Mordor Intelligence, 2019). This report also highlights the purchasing trends of the most popular smart technology items in North America which leads as the market leader in the field with the largest demand being for smart connected cameras, then for video doorbells, connected lightbulbs, smart locks and finally the smart speakers. This points to family safety being the most important consideration for upgradation to a smart home. In another survey conducted by Mordor Intelligence, it was highlighted that there was an inclination of users towards purchasing smart technology that helped increase the convenience in the users’ lives. One in six Americans owns a smart speaker in 2018, a figure that’s up 128% from January 2017. It is estimated that 16% of Americans own a smart speaker or around 39 million people. Customers use Amazon Echo for many purposes, with one-third using it as an information provider responding to questions and over 40% as an audio speaker for listening to streaming music according to a recent survey (Mordor Intelligence, 2019). The penetration rate of smart home technology is also on the rise in each segment. As observable from the graph below, the control, comfort and security are in the top among the most widely adopted applications. This data fully reflects the smart home trends which dominate the market today and will most probably shape it in the next years.
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Figure 8 Regional Market Growth Rates of Smart Home Technologies | (Mordor Intelligence, 2019)
Figure 9 Market Penetration of Smart Home Technologies | Source: Statista, 2019
The strides in the technological market and industries are the major driver for the innovation that are visible in the smart home market as it too is a technology driven field. This constant growth and new innovations in the field are enabling smart home technologies to be ubiquitous. In this attempt, there are certain trends that have been identified from the same which are quintessential for developing future technologies in the favourable direction. It is not only the advances in technologies, but also the
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increasing speeds of internet that has improved the speed and quality of IoT enabled devices. There is an increase in standardisation of the smart home technologies. This is mainly to improve the installation, management, compatibility and usage of the smart technologies. Machine learning and Artificial Intelligence is also enabling technology to provide a better response and function based on the users’ requirements. This further has given rise to increasing robotic help within the house ranging from robotic vacuum cleaners to smart lawn mowers. This increase in the popularity has also allowed for increasingly affordable price tags to such smart technologies and robots, but still not quite affordable for all. The in-house healthcare has benefitted to a large extent with a possibility of smart technology and robotic assisted living for senior citizens and others in need. The relatively new field of smart wearables has contributed majorly (Marr, 2020). A general trend of increasing connectivity, better security, integration of voice assistants, enhanced functionality and ubiquity of artificial intelligence is clear. But despite these, there is also an increasing effort of going off the energy grid using smart solar and other in-sync smart energy technologies. While for the management trends for the smart technologies installed, “Trends in smart home technology may change, but this one will, probably, only expand” (Digiteum, 2020), which clearly hints towards seamless integration of user lifestyles, habits and routines with the smart technologies.
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Chapter 03: Research Methodology 3.1 Introduction This chapter is a derivative of the research framework established and explores the considerations, aspects and desired outcomes of each step in the method adopted for research. The Dissertation employs Qualitative and Quantitative methods for analysis while also undertaking a Phenomenological approach which states that lived experiences are valid forms of data collection.
3.2 Data collection and Analysis The data collected from the following tools acts as the baseline for formulating the analysis and successfully evaluating the topic of this dissertation i.e., Smart Home Trends and the corresponding social and spatial relationships.
3.2.1 Literature Review The literature study forms a major percentage for evaluating the dissertation topic as due to the limitations of this dissertation, primary data collection and analysis is largely restricted in light of the COVID-19 pandemic. The literature review as a tool will aim to identify certain parameters and considerations that will drive the process of scrutinising the case studies undertaken.
3.2.2 Secondary Case Studies The 3 secondary case studies are tools for formulating a deeper understanding of the context and scope of the research at hand by utilising and analysing real world examples. These cases are research based smart home projects undertaken by the mentioned universities and a smart home centre and provide an in depth understanding into the heart of the concept of a Smart Home. The following 3 smart home projects are chosen – 1. GatorTech Smart House, University of Florida; 2. Aware Home, Georgia Institute of Technology; 3. CyberManor Smart Home Experience Centre, USA;
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3.2.3 Virtual Primary Case Studies The 2 Virtual Primary case studies identified will be evaluated via virtual user and spatial interactions to develop an understanding of the smart environments. The mode of virtual interaction will be via the posted online content. This collected data will then be synthesised as per the Analysis framework drawn in this chapter. The following 2 smart residences, both of which belong to YouTube have been chosen – 1. “What’s Inside? Family” Smart Home, USA 2. “Hey Ashley Renne” Smart Home, USA
Table 6 Case Study Parameter Analysis Derivation Table | Source: Author
3.2.4 Survey The survey planned will be sources of primary data collection and will guide in formulating a more effective dissertation. It would be based on users’ outlook on technologies and their utilisation.
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3.3 Analysis Framework The following framework for analysis has been formulated to assist the methodology of research undertaken. The diagram focusses on mapping out the methodology of tackling the scope of this dissertation so as to resolve the research questions and attain the prescribed aim in an effective and efficient manner.
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Figure 10 Analysis Framework | Source: Author
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3.4 Smart Home Trends Cumulative Findings and Analysis Following the Data Collection from the afore mentioned primary and secondary sources, the findings would be analysed and evaluated as per the following themes 1. Smart Technology and User Perception 2. Domestication of Smart Technology 3. Smart Technology and Social Trends 4. Smart Technology and Spatial Trends 5. Smart Technology and Effect on Architecture of the Home 6. Essential characters of a Smart Home
3.5 Conclusion and Way Forward An appropriate conclusion will be drawn based on the findings and a suitable way forward will be proposed for future research.
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Chapter 04: Data Collection and Analysis 4.1 Literature Review The literature review as a tool has enabled the identification of certain parameters and considerations that will drive the process of scrutinising the case studies undertaken. The case studies will further be evaluated as per the analysis framework provided in the Research Methodology Chapter.
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4.2 Secondary Case Studies The 3 secondary case studies are tools for formulating a deeper understanding of the context and scope of the research at hand by utilising and analysing real world examples. These cases are research based smart home projects undertaken by the mentioned universities and provide an in depth understanding into the heart of the concept of a Smart Home. The collected data will then be synthesised as per the Analysis framework drawn in the Research Methodology chapter.
Figure 11 Case Study 1: The GatorTech Smart House | Source: https://www.researchgate.net/publication/224645278_Enabling_a_Plug-andplay_integration_of_smart_environments
Figure 12 Case Study 2: The Aware Home | Source: https://www.cc.gatech.edu/fce/house/house.html
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Figure 13 Case Study 3: CyberManor Smart Home Experience Centre, California | Source: https://cybermanor.com/
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4.2.1 GatorTech Smart House, University of Florida A. Introduction The GatorTech Smart House is envisioned as a Programmable Pervasive Space for developing scalable and cost effective ways to develop and deploy extensible smart technologies within the home (Helal et al., 2005). This is directly in-line with evolving a future-proof and consumer friendly smart home product. The goal of this project, as stated by the development team is, “…to create assistive environments such as homes that can sense themselves and their residents and enact mappings between the physical world and remote monitoring and intervention services.” (Helal et al., 2005). The users of the smart house are the student and faculty research teams tasked and determined to create a programmable, scalable, cost-effective and pervasive smart space.
Figure 14 The GatorTech Smart House | Source: https://www.researchgate.net/publication/224645278_Enabling_a_Plug-andplay_integration_of_smart_environments
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Figure 15 The GatorTech Smart House Ground Floor Plan | Source: Author
B. Smart features and Corresponding Locations The smart features incorporated in the house are based on the smart technologies available in 2005 and certain spaces within the smart home were earmarked for future smart technologies with awaited advancement to carry out the required tasks such as in case of cognitive assistance. The following table outlines the smart technologies and their corresponding location and function as utilised within the respective smart home setting. This gives a perspective on the perception of smart technologies and their respective placements within the home settings. The Deployment of Smart Home Technologies is not restricted to the house, but spills beyond the walls of the house.
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Table 7 Smart Technology, Function and Location, GatorTech Smart House | Source: Author
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Figure 16 GatorTech Smart House Axonometric with Smart Components | (Helal et al., 2005)
C. Media Defined Zones In an attempt to understand the categorisation of smart technologies within the house, the technologies’ function character and spatial placement is utilised to identify Activity Centres and Coordination Zones within the house. Smart technologies such as the smart projector and other technologies with larger displays are placed in the more social parts of the house as it promotes greater volume of interaction while the smart mirror and smart phones have relatively smaller interactive displays which work fine as they are to be utilised largely on a per resident basis. The screen size of the display is observed to be roughly proportionate to the number of users of the display.
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Table 8 Media Defined Zones, GatorTech Smart House | Source: Author
D. Inputs, Outputs and Interface Engagements The Smart technologies under the Input & Output category largely tie together to the more social aspects of the house by giving users access to the Front Porch from the comfort of ones’ bed, in this case. There is also dominance of Smart Technologies that control home security within this category.
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Table 9 Inputs, Outputs and Interface Engagements, GatorTech Smart House | Source: Author
It is observed that majority of the devices are based on the “set and forget” mode with only very minimal inputs required only usually in case of updating a setting. But in case of security smart features it’s in line with “repetitive” mode, where there is a larger need for control which has been provided via the provision of coordinate displays in suitable locations. The requirement for more interaction and control is observed for security-based functions.
E. Levels of Privacy The maps provide contrast and effect of digitisation of the smart home. The digital privacy [It is defined as the connection to interactive socially active smart devices that enable audio/ visual communications while having the ability to be controlled by other smart interfaces] as observed in this case is almost negligible which can be attributed to the increased number of smart, internet enabled interactive devices within the previously private spaces of the house. The comparatively private spaces such as the dining area and other spaces in the house are rendered to be more digitally public with possibility of social interactions via technologies such as socially distant dining, smart closets, smart phones and smart wearables. Smart Interactive devices have the capacity to increase the degree of sociability of a space.
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Figure 17 Physical Privacy, GatorTech Smart House | Source: Author
Figure 18 Digital Privacy, GatorTech Smart House | Source: Author
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F. Effect on Chores and House Activities In this case study, out of the 25 Smart Technologies and Devices installed within the home, 8 of them, i.e., 32% of smart technologies installed actively contribute to enabling an easy lifestyle by reducing and easing the chores around the house. While majority of these chores are to be automated, the potential of interaction with the smart devices is largely based on the level of maturity and the nature of need of the smart home resident.
Table 10 Chores, Smart technologies and Potential Users, GatorTech Smart House | Source: Author
G. Internet of Things Architecture The structure followed consists of actuator (interactive) and sensor (observative) based smart technologies which are governed by Processor, Communication and Memory modules that are connected to the main power. The location of these modules has not been made available by the project team.
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Figure 19 Sensor Platform Architecture. Modular Design for Flexible configurations | (Helal et al., 2005)
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4.2.2 Aware Home, Georgia Institute of Technology A. Introduction The Aware home is conceptualised to be as an experimental laboratory for smart home research which was developed by the Georgia Institute of Technology in the USA. The construction began in 1999. “…living lab for research in ubiquitous computing for everyday activities”, is how the paper by Kidd et al. defines this project. The intent of this project focuses on producing a smart environment which is capable of knowing information about itself and the whereabouts and activities of its inhabitants (Kidd et al., 1999). The home is targeted towards creating a long term, comfortable and safe environment for aged occupants. For the purpose of establishing and commencing research, the home was initially inhabited by students on one of the floors of the house. While the house is two storeyed, the plan of both floors is identical.
Figure 20 The Aware Home Front Elevation | (Kidd et al., 1999)
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The Aware home in totality houses – 1. Two Identical and independent living space; 2. Two baths; 3. Two Bedrooms; 4. One Office; 5. One Kitchen; 6. One Laundry Room; 7. Shared Basement with a. Home entertainment area b. Control Room for Centralised Computing Services
Figure 21 The Aware Home Ground Floor Plan | Source: Author
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While the following are some smart technologies integrated within the Aware Home lab, there have been other student developed interactions such as the Whole Home Gaming (Entire house adapted and programmed as an interactive gaming environment), Connected Living Demonstrations and Ambient Alerting (Using smart surfaces such as mirrors and displays to notify and alert inhabitants of routines et cetera). The essence of the smart home is the human-home symbiosis which is the key for seamless interactions within the smart home (Kidd et al., 1999).
Figure 22 The Aware Home Exploded Axonometric | Source: http://www.awarehome.gatech.edu/sites/default/files/documents/AwareHome_slides.pdf
B. Smart features and Corresponding Locations The smart features incorporated in the house are based on the smart technologies available till 2008. This smart home setting has largely been experimented on by
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students and faculties for research purposes by retrofitting newly developed smart technologies. The following table outlines the smart technologies which were initially installed and their corresponding location and function as utilised within the respective smart home setting. This gives a perspective on the perception of smart technologies and their respective placements within the home settings. There is an increase observed in the outreach of smart technologies within the larger expanse of the house with sensors being deployed within the entire home setting to make holistic living a better experience.
Table 11 Smart Technology, Function and Location, The Aware Home | Source: Author
C. Media Defined Zones In the media defined spaces, the coordinate displays are regulated via smart wearables being the only deployed source but this can be attributed to the existence of the control room that acts as the central means of coordination within the house. A central control room exists at the basement level which also functions as a centre for coordination around the house.
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Table 12 Media Defined Zones, The Aware Home | Source: Author
D. Inputs, Outputs and Interface Engagements In this case study, the Control Room is where both, the Inputs and the Storage of digital information takes places in addition to the processing. The Control Room functions not just as a coordination, but also as a store of data and processing.
Table 13 Inputs, Outputs and Interface Engagements, The Aware Home | Source: Author
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E. Levels of Privacy In case of the Aware home, a similar trend of decreasing privacy is observed like in the case of The GatorTech Smart House. The privacy within this scenario is largely reduced in the house not just because of smart data connections, but also by the existence of a central control room that has the ability to control all aspects of the smart home.
Figure 23 Physical Privacy map, The Aware Home | Source: Author
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Figure 24 Digital Privacy map, The Aware Home | Source: Author
F. Effect on Chores and House Activities In this case study, out of the 10 Smart Technologies and Devices installed within the home, 5 of them, i.e., 50% of smart technologies installed actively contribute to enabling an easy lifestyle by reducing and easing the chores around the house. The senior citizen centric approach allows for larger applications of smart technologies being available to them and their respective household help while automation mostly only exists in service based smart technologies.
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Table 14 Chores, Smart technologies and Potential Users, The Aware Home | Source: Author
G. Internet of Things Architecture The Control Room in the basement of the Aware Home acts as the centre for data processing and storage. All the smart technologies and devices relay information and produce relevant outputs according to programmed commands established in the Control Room.
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4.2.3 CyberManor Smart Home Experience Centre, California A. Introduction The CyberManor Smart Home Experience Centre is a restored one room schoolhouse in California. The goal of this smart home centre is to showcase the best of entertainment, lighting control, security, and comfort systems that enhance how one lives, plays, and works in their home – installed in existing homes. These systems can be controlled inside or outside the home with the smart phones, tablets, or smart watches that majority of the people possess in today’s global technical climate (Zuiden, 2015).
Figure 25 CyberManor Smart Home Experience Centre, California | Source: https://cybermanor.com/
The Experience Centre has been developed by CyberManor as a default template for the smart home experience design for its customers. Hence the primary users of this smart home space are the potential customers who are looking to be convinced for adopting a smart home practice. The Cyber Manor Smart Home Experience Centre houses – 1. 2. 3. 4. 5. 6.
Three Bedrooms; Two Bathrooms; One Living Room; One Family Room; One Garage, Wine Cellar;
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7. Backyard; This development aims to aid the realisation of the benefits of computer assisted living which can be governed by available remote smart technologies such as smart phones and tablets. This case study will hence provide an insight to retrofit smart home design exploration possibilities. As part of marketing, the CyberManor helps envisioning the smart home centre as a residence of The Smiths – Don and Taylor, who are a working married couple. Their day outlines the usage of all the installed smart technologies in a realistic way through the day based on which the case study has explored the usage capabilities of the smart home (CyberManor, 2015) (Refer to Annexure - II for a detailed account).
B. Smart features and Corresponding Locations The following diagram outlines the smart technologies and their corresponding location and function as utilised within the respective smart home setting. The smart home technologies are placed to increase connectivity within and outside the house while also building on the concept of Smart Living.
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Figure 26 CyberManor Smart Home Experience Centre Floor Plan | Source: Author
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Table 15 Smart Technology, Function and Location, The CyberManor Smart Home Experience Centre | Source: Author
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Table 16 Smart Technology, Function and Location, The CyberManor Smart Home Experience Centre (continued) | Source: Author
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Figure 27 CyberManor Smart Home Experience Centre Axonometric with Smart Components | Source: https://cybermanor.com/
C. Media Defined Zones A major concentration of smart technologies lies in the Activity Centres while the Coordination zones also show increasing number of screens for managing various aspects of the smart technologies. In this case study, the unit element that is enabling control over the smart home are the Apple products in the form of iPad and Apple watches which while enable coordination and increase accessibility in a modular manner, also drive the cost of the smart home installation. But this increases the manageability, flexibility and reliability of the smart technologies as the repurpose of ubiquitous technologies as smart technologies ensures good customer support and up to date software upgrades, making the installations future proof.
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Table 17 Media Defined Zones, CyberManor Smart Home Experience Centre | Source: Author
D. Inputs, Outputs and Interface Engagements The Input category includes the programmable keypads for the residents and the visitors of the house to increase manageability and cost effectiveness within the smart home setup since otherwise another touch screen could have been installed. The Output category includes multimedia smart technologies along with smart monitoring devices. While these multimedia smart technologies are largely output based, they can be turned into interactive (input and output) technologies by syncing with other smart technologies around the house and/ or by plugging in Smart Plugs, Amazon Fire TV stick et cetera. The bedrooms house only the Electricity controls as usually the last chore in a persons’ day is to switch off the lights of the house (CyberManor, 2015). Smart technologies placed according to one’s routine usage hence promoting Domestication via Positive Artefactual and Perpetual control.
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Table 18 Inputs, Outputs and Interface Engagements, CyberManor Smart Home Exhibition Centre | Source: Author
E. Levels of Privacy In this case study, there is a largely similar trend of digital and physical privacy observed other than in the bedroom where the digital privacy is maintained based on the presence of only the Electrical Monitoring System. But despite that, the digital privacy of all spaces in the house is largely reduced since mojority of the residents in the house utilise a smart phone with active internet connections.
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Figure 28 Physical Privacy, CyberManor Smart Home Exhibition Centre | Source: Author
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Figure 29 Digital Privacy, CyberManor Smart Home Exhibition Centre | Source: Author
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Smart Wearables and smart phones increase social outreach throughout the house which results in a decrease in privacy but this can be regulated by the users. Digital Privacy can be controlled by the users and is a matter of choice.
F. Effect on Chores and House Activities In this case study, out of the 32 Smart Technologies and Devices installed within the home, 14 of them, i.e., ~42% of smart technologies installed actively contribute to enabling an easy lifestyle by reducing and easing the chores around the house. The automation and smart phone enabled control makes the users’ life more comfortable by reducing the housework time through automations and reducing the waiting time for leisure and other activities via remote smart coordination access.
Table 19 Effect on Chores and House Activities, CyberManor Smart Home Exhibition Centre | Source: Author
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G. Internet of Things Architecture Due to a retrofit nature of the smart home centre and the choice of smart technologies, the data processing largely takes place within the device (e.g. iPad, smartphone) itself. In other cases, the sensors feed in the information to the cloud via the internet connections around the house for processing and storing processes. Cloud based storage services offer a better alternative to control centres as data privacy can be maintained on the cloud via differentiated user accounts.
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4.3 Virtual Primary Case Studies The 2 Primary virtual case studies identified will be evaluated via virtual user and spatial interactions to develop an understanding of the smart environments. The mode of virtual interaction will be via the online content uploaded. This collected data will then be synthesised as per the Analysis framework drawn in the Research Methodology chapter.
Figure 30 "What's Inside? Family" Smart House Ariel View | Source: YouTube
Figure 31 "Hey Ashley Renne" Smart Home | Source: YouTube
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4.3.1 “What’s Inside? Family” Smart Home, USA A. Introduction The YouTube channel, “What’s Inside? Family” creates Vlogs and gives an insight into the family of its creator. The playlist titled “Our New Home”, has 37 videos and outlines the construction of their family smart home. The family has in total 5 members with 2 adults and 3 children, and also a small family pet dog. The Husband, Dan, is the main creator behind their YouTube channels who works from the studio within the house. The son – Lincoln, the oldest is 14 years old while the other two are daughters – London and Claire who appear to be in primary and middle schools respectively. Since this home was completed in the end of 2019, just before the COVID-19 pandemic, all residents have enjoyed the comfort of their large, high ceilings and smart home.
Figure 32 "What's Inside? Family" Smart House Ariel View | Source: https://www.youtube.com/watch?v=Bx9hPzTxVqE&list=PL3BVMyyYBBREmgMWcnm6R9gZlXAhnsxv&index=28
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Figure 33 "What’s Inside? Family" Ground Floor Plan | Source: Author
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B. Smart features and Corresponding Locations The following table outlines the smart technologies and their corresponding location and function as utilised within the respective smart home setting. While the house functions are largely based on the single ground floor storey, the first story houses a small family lounge and also the major smart network connection centres. There is also a central electrical control at the lower storey which centrally controls the lighting of the house.
Table 20 Smart Technology, Function and Location, "What’s Inside? Family" Smart Home | Source: Author
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Figure 34 The Wiring at the Main Server Room Level | Source: https://www.youtube.com/watch?v=UCeJ4e7hc40&list=PL3BVMyyYBBREmgMWcnm6R9gZlXAhn-sxv&index=7
C. Media Defined Zones In addition to the mentioned Activity Centres, The Office Studio and majorly Lincoln’s room can also be classified as Activity Centres due to a proper computer-work setup being established. As in the other smart homes studied till now, the coordination in this home also takes place via various smart screens, smartphones and tablets. Despite these options, the residents enjoy and prefer control via Amazon Alexa which enables voice control over the installed smart technologies.
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Table 21 Media Defined Zones, "What's Inside? Family" Smart Home | Source: Author
D. Inputs, Outputs and Interface Engagements There is not any smart device/ smart technology explicitly in the Input Category while there is more focus on installation of Interactive technologies to increase control and manageability of the smart home. Minimum/ no input devices are observed to be installed. Interactive devices with input and output capabilities are preferred for cost and efficiency purposes. While the Output category has Smart speakers and smart lighting, those are programmed and controlled via the interactive technologies. As a general aesthetic approach, the speakers and other smart technologies are designed to be largely hidden within the architecture of the house to promote a minimal and simple design.
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Table 22 Inputs, Outputs and Interface Engagements, "What's Inside? Family" Smart Home | Source: Author
E. Levels of Privacy The trend of Physical and Digital Privacy observed is similar to the secondary cases studied. There is again a higher degree of decrease in the privacy despite smart interactive devices enabling a remote digital social life. This is largely due to a central control/ server room which records and stores all collected data and hence can hinder in private functions of various residents within the house. But despite this factor, in a house with young teenagers, kids and pets, this is an effective method to ensure cyber and physical security of the residents.
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Figure 35 Physical Privacy, “What’s Inside? Family" Smart Home | Source: Author
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Figure 36 Digital Privacy, "What's Inside? Family" Smart Home | Source: Author
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F. Effect on Chores and House Activities In this case study, out of the 12 Smart Technologies and Devices installed within the home, 8 of them, i.e., 66% of smart technologies installed actively contribute to enabling an easy lifestyle by reducing and easing the chores around the house.
Table 23 Effect on Chores and House Activities, "What's Inside? Family" Smart Home | Source: Author
The major technologies (simple functional and entertainment based) installed within this house are such that these can be universally accessed by all the residents.
G. Internet of Things Architecture The Smart home under study has a Server Room on the second storey of the house which controls the automation, process and storage of the smart technologies around the house. There is also a Light and Electricity central control on the ground floor which regulates the electric controls of the entire house. While one can switch the house down centrally from this location, the same can be done via a simple voice prompt to Alexa, the home voice assistant, as demonstrated by Dan. There is an increasing level of trust in voice assistants observed.
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4.3.2 “Hey Ashley Renne” Smart Home, USA A. Introduction The Smart residence that the famous YouTuber Ashley Renne resides in, is located in the USA. Her YouTube channel is a Sustainable and Vegan Lifestyle channel where she shares holistic ways to attain a healthier body, home and planet. The house is a three-storeyed, solar powered, new construction which houses smart sensors and other smart technologies as retrofits. This house will be home to a family of three which includes a new born and also a pet dog. The family also drives an electric smart car from Tesla Motors.
Figure 37 "Hey Ashley Renne" Smart Home Backyard View | Source: https://www.youtube.com/watch?v=9u9kqhHC6Ok&list=PLyVlgZDTxUQlL7shIOPIPTnq5yAianN5N&index=8
B. Smart features and Corresponding Locations The following table outlines the smart technologies and their corresponding location and function as utilised within the respective smart home setting. This smart home is a retrofit type smart home where smart technologies are deployed throughout the home with voice assistant support.
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Table 24 Smart Technology, Function and Location, "Hey Ashley Renne" Smart House | Source: Author
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C. Media Defined Zones The technologies for coordination are largely placed in high usage spaces and rooms and social spaces of the house. This could be attributed to the work-from-home lifestyle of Ashley’s YouTube channel and also her passion for a vegan lifestyle which is the reason why a number of these devices are placed in the kitchen and also the living spaces. Coordination based Smart technologies are placed according to spatial usage tendencies and routines.
Table 25 Media Defined Zones, "Hey Ashley Renne" Smart Home | Source: Author
D. Inputs, Outputs and Interface Engagements There are a number of voice-controlled devices placed within the house which increases the degree of accessibility and control. There are also programmable interface queues which can be run based on keyword commands and prompts, making coordination via voice easier.
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Table 26 Inputs, Outputs and Interface Engagements, "Hey Ashley Renne" Smart Home | Source: Author
E. Privacy Levels The Internet of Things structure is such that it stores the data on cloud. Hence the privacy trends are largely similar to those observed in the CyberManor Case study undertaken. A comparative study of the Physical and Digital Privacies was not possible due to the restriction of available content for mapping out a potential plan.
F. Effect on Chores and House Activities In this case study, out of the 20 Smart Technologies and Devices installed within the home, 14 of them, i.e., 70% of smart technologies installed actively contribute to enabling an easy lifestyle by reducing and easing the chores around the house. There are also cameras and sensors that enable to keep an eye on the family dog while ensuring security and service/ chore automation.
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Table 27 Effect on Chores and House Activities, "Hey Ashley Renne" Smart Home | Source: Author
G. Internet of Things Architecture Being a smart home of largely a retrofit style, there architecture is structured to store majority of data online, on the cloud. The processing and computing of tasks takes place within the smart technology itself. The majority of these devices run on batteries and are rechargeable.
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4.4 Survey Survey to be based on users’ outlook on smart technologies and their utilisation. The survey questionnaire will be based on the findings and trends identified from the literature study. The following table elaborates on the structure of the survey and the thought behind the questions formulated.
4.4.1 Survey Questionnaire Structure
Table 28 General Survey Questionnaire Structure | Source: Author
4.4.2 Findings and Analysis The survey has been filled by 147 respondents from different countries, of different age groups and from different fields of work. The majority of the respondents were from India while other being from Egypt, USA, South Korea, Singapore and so on. The largest age group undertaking this survey was that of 18-24 (47%) and also 45-54 (18%). The majority family sizes of the respondents were 2-4 (66%) and a roughly 24 members of the house were smartphone users in all respondent’s families (72%). This data reflects the dominant nuclear family trend and the younger generations of
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the house being more technologically savvy than their grandparents (5-6 family set to have live-in grandparents’ majority). The majority of the respondents were from the fields of engineering and architecture (45.6%).
Figure 38 Survey Respondents' Geographic Locations | Source: Author
The larger general perspective towards smart home technologies is observed to be that they are very helpful and make life easier (79.6% of the respondents think so). The respondents also have shown concern towards the security risks (35.4%) and also the intrusive nature of smart technologies (24.5%). The purchasing trend is inclined towards energy efficiency and cost saving with 57 people opting for smart energy sensors as their first choice buy. The second choice was that of smart security devices with 52 people and then home automation sensors and devices with 48 people. Home assistants were one of the least likely products to be purchased among certain people while it still remained a popular choice overall. The survey also finds that people are also warming up to the ideas of immediate control of devices and automation in their lives but not yet fully comfortable with it. Furthermore, the tasks of Lighting, Ventilation and Temperature Controls were the most popular choices to being remotely controlled and automated. Tasks involving household work and routine based activities were warmly received within the
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idea of automation but still were perceived as tasks that need not require urgent automation and control. There are still concerns of home security visible with trends of a low average of points visible in both the fields of control and automation. This highlights the lack of reliability on smart technologies. Despite this previous finding, there is a general want to switch to smart homes due to their functional aspects. While considering the scenarios of control within a smart household, the majority of respondents believe and want that each space will have functions accessible to only certain residents based on their maturity and responsibility in the house (44.9%) and also that smaller spaces would be controlled individually with a central override option (34%). The least preferred idea among the respondents was that of one person controlling all aspects of the smart home (4.1%). The major issue identified which is causing a hinderance in adopting smart technologies is the high cost (55.1%) for not being able to update to a smart home. There are still other concerns such as that of privacy and security (26.5%) which makes the people wary of purchasing smart technologies. Finally, when asked about their want to upgrade to smart homes if the cost was not an issue, the majority opted Yes (63.3%), the second majority was of Maybe (31.3%) which shows the trend of people warming up to the idea of a smart home. Hence, the major concern of people that is keeping them from upgrading to smart homes is the cost. Another major observation is that the people are not aware of the advances in smart technologies and when asked about what they would want to personally include in their smart home setups, a large number of them were either content with the given scenarios or were suggesting already existing smart solutions while some aspiring for larger levels of home ambience personalisation and mood detection based automation which employs a larger developed deployment of artificial intelligence in the current technologies. There are also concerns for home security and reliability observed. Restricted by the time for the survey, the domain of this survey remains slightly shallow and hence across the age groups, there are mostly similar trends. There is deviation observed in the age group 55-64 where the respondents show inclinations towards the need for more comfortable home environments with increased
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automation and ease of access to control household and lifestyle-based tasks while other age groups largely stay focused on the energy efficiency, cost effectiveness and security-based features of smart home technologies. (Refer to Appendix – III for survey questionnaire and results)
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Chapter 05: Smart Home Trends Cumulative Findings and Analysis
Figure 39 Cumulative Data Findings and Analysis points | Source: Author
5.1 Smart Technology and User Perception The field of smart home technologies is gaining ubiquity with increasing development but the survey conducted and reports from companies such as Comcast suggests that there still exists low reliability of the masses on these technologies. This can be further supported by the findings of the survey that demonstrates people’s concern for safety and security. There are also concerns for loss of privacy and control. As illustrated by Hamill, there is a trend of capital replacing labour but this is only in the case where abundant capital is available. People who have restricted reserves of capital prefer spending on leisure activities. In the Smart Home Case Studies undertaken, the secondary case studies which were experimental homes and exhibition centres, the chore relieving smart technologies were less (maximum 50% in Aware Home) while the primary virtual case studies showed a higher percentage (up
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to 70% in Ashley Renne’s home). This can be attributed to the fact that the inhabited smart homes need to more holistically tend to the daily needs of its residents. In the current day, smart technologies have been found to be capital extensive and hence despite a want to upgrade to a comfortable, smart home life, the people are unable to afford them. While there are concerns for privacy, control and security, there exists also a gap in user knowledge that is identified in the survey conducted which further hinders the user perception with exaggerated stigmas and fears of smart technologies. The purchase of smart technologies for the masses has been a reaction to their needs and aspirations, and hence a preference towards smart, automated and money saving HVAC and lighting systems can be observed. There are also trends of purchase of smart technology systems largely among technology enthusiasts and masses with smart technology awareness. The survey undertaken also highlights the need for comfort of older age groups and henceforth their choice of increased automation of daily routines and household tasks while the younger population aspires for a more energy efficient house as they can themselves manage the daily chores and therefore prefer smart technologies that save capital, over time.
Figure 40 “Just because it can be, doesn’t mean it should be.” Keeping this in mind, rate the following features in your home from 1-5 with 1 least likely to and 5 most likely to in your opinion be controlled remotely from another device say, your smartphone? | Source: Survey Findings, Appendix – III, Author
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5.2 Domestication of Smart Technology In principle, Domestication of Smart Technologies increases with increasing user friendliness and empowerment offered. The key goal should be that smart technologies perpetually control the busy routines and not the users. With the use of increasingly ubiquitous technologies, the smart home can be made future proof; another attribute that increases the degree of domestication. The retrofit and massproduced smart devices and services are more likely to be future proof as they are standardised and upgradable. The increasing efficiency and reliability of voice assistants is also playing a major role in the process of domestication. Through the medium of literature review, the following factors have been identified to have a key role in the domestication of smart home technologies.
5.3 Smart Technology and Social Trends The people prefer simple over complicated. The idea of smart is only observed to take off when there is an actual need for it. Remote access, coordination and automation of tasks is mainly maintained via screens and coordination displays which enable the users to overlook the processes undertaken by the smart homes. As predicted by Harper, with the development of Natural Language Processing, voice assistants are gaining popularity as preferred methods of instruction of smart home technologies (as also noted in the case study “What’s Inside? Family”). Safety and security being one of the major concerns for residents, the devices and services identified with it are observed to have the most interactive capabilities to maintain trust and reliability of the users. The smart technologies should not all be automated but offer options for
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regulated automation and interaction to ensure the ones’ control over the same and not the other way around. The visual display and online dashboards are some good practices for ensuring users’ control. The control of the smart home is becoming more accessible and ubiquitous with the increasing number of smartphones and tablets in the market. New technologies act as disrupters when introduced but with time, use and ubiquity merges them into the daily routines of the users. With the increasing digitisation of the house, one’s social capabilities extend manifold. While this can be seen as intrusive, it is under the users’ control that this can be restricted and reprimanded as their perusal.
Figure 41 General relation of privacy and types of Smart home Technology Scenarios | Source: Author
The idea of privacy within the smart home as explored in the case studies, demonstrates that privacy is further undermined in the case of a centralised control room/ centre while options for cloud storage and computing can offer a more private alternative. In the case of a home with children and pets, it is still ideal to have a centralised override access to safeguard them and ensure their safety.
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Figure 42 Traditional Generic Spatial Segregation | Source: Author
Figure 43 Digital Smart Interventions transforming private spaces to less private as a user comfort trade-off. | Source: Author
In the survey conducted, it was highlighted that people prefer that each space should have functions available to each resident based on their level of maturity and responsibility in the house while entertainment based and simpler functional technologies should be made universally accessible to all residents.
5.4 Smart Technology and Spatial Trends The locating of smart home technologies should be as such that it complements ones’ daily routines and habits while also should be in accordance to the habitual functional usage within the home. There is also a focus of smart home technologies on blurring the thresholds of activity spaces by providing for similar tasks to take place together. These trends are confirmed in findings of the virtual case studies conducted.
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Furthermore, the findings from the case studies points towards the following relationships of spaces and task coordination as per user comfort and habits – 1. Hallways – Smart Thermostats, Smart Lighting Remote and Smart Screen Coordination 2. Bedrooms – Security, Start of day chores, End of day chores and Selective Social Interactions 3. Living Room, Dining Room and Kitchen – Security, Daily Need and Major Social Coordination and Interactions Trends of coordination help identify a shift to a more voice-based communication between the users and the smart home as a means of increasing ubiquity of the smart home technologies. There is also a clear shift towards using ones’ smartphones and tablets for remotely controlling smart devices. For the touch-based coordination devices, the size of the display for coordination is found to be roughly proportional to the number of users in the vicinity of its placement. This trend can be supplemented by the provision of voice enabled assistants but the need for an interactive dashboard display cannot be overlooked. Based on the spatial need, the requirement for an input only/ input & output/ output only smart device is decided. Ideally, the “input only” devices are not promoted but rather interactive devices are opted for due to their dual capabilities. The Smart Home is not restricted to the walls of the house but is boundless since one can control various aspects of the house remotely from any location with an internet connection.
5.5 Smart Technology and the Effect on Architecture of the Home The architecture of the smart home is observed to be evolving to cater to increasing data capacities. This is through designated server/ control rooms in cases of utilisation of high security and control measures and increasing number of optics and fibre cables alongside increased electrification. More plugs, sockets, faster and more accessible
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internet connections for starters are the basic requirements for creating an efficient smart home. There is a visible increase in the number of robotic devices around the home and the spatial aspects will be required to adapt and maximise their efficiency. An example of a cleaning Roomba can be taken which in itself can navigate through changing surfaces in the house but needs some clear space available to access the floor areas and perform its cleaning function. While there would be more adaptation towards these smart home robots, there is still some changes that can potentially emerge as a result of the architecture of the house adapting to these smart robots.
Figure 44 IoT and Smart Technologies Dependencies | Source: Author
When designing a smart home, the smart home can be that of a Purely Overlay Retrofit Type (as Ashley Renne’s smart home) which would include market bought smart sensors and devices, and subscription-based services. The only requirement for these would be a fast internet connection and electrical connections. The second type of smart home can be an Architectural Overlay Retrofit Type which would include some architecture treatment to the installed market bought devices. While the smart home devices available in the market are largely designed to match various architectural aesthetics, an architectural oversight to these installations is sometimes required (as
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also noted in the case study “What’s Inside? Family” with the smart blinds and hidden speakers). The final type of smart home that can be categorised is one designed from the Ground Up. This type of smart home is a resilient hub for smart home devices and houses provisions for robust surveillance, control and automation (as noted in the case study “What’s Inside? Family”). A control room can be designed for centralising data storage, processing and coordination under this type of smart home.
Figure 45 Smart Home Types identified on the nature of interaction of technology and architecture | Source: Author
5.6 Essential Character of a Smart Home The essence of a smart home lies in the improvement of quality of life of its residents via the deployment of smart technologies. The essentials for a smart home are a fast internet connection and apt electricity provision. Furthermore, given the pervasive nature of smart home technologies, the Smart home should be domesticated. While in terms of appearance, there is a vast difference in a smart home and a regular home, the fundamental idea of a home remains the same. It is hence eminently necessary to understand the complexity of the home and its fundamentals of morals, culture, traditions, practices, values, routines and memories as these formulate the identity of the home. The smart technologies assume daily life of the users’ as specific and repetitive, while it is on the contrary, organic, opportunistic and improvisational. Artificial Intelligence as a field is showing promise but it is still unable to comprehend the full gamut of human emotion. As at the time of this dissertation, a universally essential smart technology does not exist, as the definition of essential is variable. The closest technology one can identify as essential is the smartphone which has the ability to not
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only communicate and coordinate, but also create and consume media via the internet.
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Chapter 06: Conclusion and Way forward A general trend of increasing connectivity, better privacy and security, integration of voice assistants, enhanced functionality and ubiquity of artificial intelligence is clear. But despite these, there is also an increasing effort of going off the energy grid using smart solar and other in-sync smart energy technologies. A number of such smart home trends and their implications have been identified within this dissertation based on the current world scenario and the current state of smart home technologies. The field of Smart Homes is ultimately a technology-based industry and therefore will grow with technological innovations and progress. Moving from touch to voice in coordination, the next trend to gain popularity could be augmented reality. Moore’s law gives us a good idea of how rapid the growth of this industry can be especially when it is found that the actual growth of the industry is nearly outpacing it. Hence this dissertation hopes to function as the basis for future sociotechnical research in the field of smart homes where innovations are just beginning to emerge as artificial intelligence prepares to break out of its nativity.
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Bibliography 1. Aishwarya (2018) ‘Introduction to Recurrent Neural Network’, GeeksforGeeks, 3 October. Available at: https://www.geeksforgeeks.org/introduction-to-recurrent-neural-network/ (Accessed: 1 September 2020). 2. Atul (2018) ‘Machine Learning Tutorial’, Edureka, 29 May. Available at: https://www.edureka.co/blog/machine-learning-tutorial/ (Accessed: 1 September 2020). 3. Bawany, N. Z. and Shamsi, J. A. (2015) ‘Smart City Architecture: Vision and Challenges’, International Journal of Advanced Computer Science and Applications, 6(11). doi: 10.14569/IJACSA.2015.061132. 4. Brownlee, J. (2019) ‘A Gentle Introduction to Generative Adversarial Networks (GANs)’, Machine Learning Mastery, 16 June. Available at: https://machinelearningmastery.com/whatare-generative-adversarial-networks-gans/ (Accessed: 1 September 2020). 5. Chaillou, S. (2019) AI + Architecture | Towards a New Approach. Harvard University. Available at: https://www.academia.edu/39599650/AI_Architecture_Towards_a_New_Approach (Accessed: 7 August 2020). 6. Comcast (2018) What our tech habits reveal about the future of smart homes, Quartz. Available at: https://qz.com/1482503/what-our-tech-habits-reveal-about-the-future-of-smarthomes/ (Accessed: 6 November 2020). 7. CyberManor (2015) ‘Day in the Life of the Smith Family in Their Smart Connected Home’. CyberManor. 8. Data Dashboards. Definition, Design Ideas plus 3 examples (no date) Klipfolio.com. Available at: https://www.klipfolio.com/resources/articles/what-is-data-dashboard (Accessed: 31 August 2020). 9. DeAngelis, S. F. (2014) ‘Artificial Intelligence: How Algorithms Make Systems Smart’, Wired, 5 September. Available at: https://www.wired.com/insights/2014/09/artificial-intelligencealgorithms-2/ (Accessed: 1 September 2020). 10. Digiteum (2020) ‘Smart Home Technology Trends — the Future of Your House in Smart Home Solutions’, Digiteum, 3 April. Available at: https://www.digiteum.com/smart-hometrends (Accessed: 10 November 2020). 11. Farshidi, A. (no date) ‘Concepts and Techniques in Designing Smart Homes’. Available at: https://www.academia.edu/9237742/Concepts_and_Techniques_in_Designing_Smart_Home s (Accessed: 14 September 2020). 12. Fasoulaki, E. (2008) ‘Genetic Algorithms in Architecture: a Necessity or a Trend?’, p. 11. 13. Frankenfield, J. (2020) How Artificial Intelligence Works, Investopedia. Available at: https://www.investopedia.com/terms/a/artificial-intelligence-ai.asp (Accessed: 31 August 2020). 14. GN, C. K. (2019) Artificial Intelligence: Definition, Types, Examples, Technologies, Medium. Available at: https://medium.com/@chethankumargn/artificial-intelligence-definition-typesexamples-technologies-962ea75c7b9b (Accessed: 31 August 2020).
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15. Hamill, L. (2003) ‘Time as a Rare Commodity in Home Life’, in Harper, R. (ed.) Inside the Smart Home. London: Springer-Verlag London Limited, pp. 63–79. Available at: https://www.springer.com/gp/book/9781852336882. 16. Hargreaves, T., Wilson, C. and Hauxwell-Baldwin, R. (no date) ‘Control in the Smart Home’, p. 18. 17. Harper, R. (2003) ‘Inside the Smart Home: Ideas, Possibilites and Methods’, in Harper, R. (ed.) Inside the Smart Home. London: Springer-Verlag London Limited, pp. 1–15. Available at: https://www.springer.com/gp/book/9781852336882. 18. Helal, S. et al. (2005) GatorTech Smart House: A Programmable Pervasive Space. IEEE Computer Society. 19. Information and Communication Technology (2008) FOLDOC. Available at: https://web.archive.org/web/20130917072505/http://foldoc.org/Information+and+Communicati on+Technology (Accessed: 31 August 2020). 20. Kelly, T. et al. (2019) ‘FrankenGAN: guided detail synthesis for building mass models using style-synchonized GANs’, ACM Transactions on Graphics, 37(6), pp. 1–14. doi: 10.1145/3272127.3275065. 21. Kidd, C. D. et al. (1999) ‘The Aware Home: A Living Laboratory for Ubiquitous Computing Research’, in Streitz, N. A. et al. (eds) Cooperative Buildings. Integrating Information, Organizations, and Architecture. Berlin, Heidelberg: Springer Berlin Heidelberg (Lecture Notes in Computer Science), pp. 191–198. doi: 10.1007/10705432_17. 22. Kitchin, R. (2013) ‘The real-time city? Big data and smart urbanism’, Springer Science+Business Media Dordrecht, (2013), p. 14. doi: 10.1007/s10708-013-9516-8. 23. Lateef, Z. (2019) ‘Types of Artificial Intelligence You Should Know’, Edureka, 18 June. Available at: https://www.edureka.co/blog/types-of-artificial-intelligence/ (Accessed: 31 August 2020). 24. Latifi, M., Mahdavinezhad, M. J. and Diba, D. (2016) ‘UNDERSTANDING GENETIC ALGORITHMS IN ARCHITECTURE’, THE TURKISH ONLINE JOURNAL OF DESIGN, ART AND COMMUNICATION, 6(AGSE), pp. 1385–1400. doi: 10.7456/1060AGSE/023. 25. Malaeb, J. (2019) ‘AIA Artificial Intelligence in Architecture General Understanding and Prospective Studies’. Shanghai Jiao Tong University. Available at: https://www.academia.edu/40398871/AIA_Artificial_Intelligence_in_Architecture_GENERAL_ UNDERSTANDING_AND_PROSPECTIVE_STUDIES (Accessed: 7 August 2020). 26. Man and machine: How algorithms change architecture (2018) Allplan - A Nemetschek Company. Available at: https://blog.allplan.com/en/how-algorithms-change-architecture (Accessed: 1 September 2020). 27. Marr, B. (2020) The 5 Biggest Smart Home Trends In 2020, Forbes. Available at: https://www.forbes.com/sites/bernardmarr/2020/01/13/the-5-biggest-smart-home-trends-in2020/ (Accessed: 10 November 2020). 28. Mayor’s Office of Technology and Innovation, New York City (2015) ‘Building a Smart+Equitable City’. Mayor’s Office of Technology and Innovation, New York City. Available at: https://www1.nyc.gov/assets/forward/documents/NYC-Smart-Equitable-City-Final.pdf (Accessed: 31 August 2020). 29. Mordor Intelligence (2019) Smart Homes Market | Growth, Trends, Forecast (2020 - 2025). Available at: https://www.mordorintelligence.com/industry-reports/global-smart-homesmarket-industry (Accessed: 10 November 2020). 118 Smart Home Trends: Social and Spatial Relationships
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30. Moum, A. (2008) ‘ICT and the Architectural Design Process – Introduction of an ICT Impact Matrix’, Norwegian University of Science and Technology, p. 12. 31. Muklashy, W. (2018) ‘How Machine Learning in Architecture Is Liberating Designers’, Redshift by Autodesk, 3 May. Available at: https://www.autodesk.com/redshift/machine-learning-inarchitecture/ (Accessed: 1 September 2020). 32. OpenAI Charter (2018) OpenAI. Available at: https://openai.com/charter/ (Accessed: 31 August 2020). 33. Poulsgaard, K. (2020) ‘Kåre Poulsgaard, Head of Innovation at 3XN/GXN on AI in Architecture | Archdaily’. Available at: https://www.youtube.com/watch?v=qqdfbMVf_lo. 34. Sadiku, M. N. O. et al. (2016) ‘Smart Cities’, International Journal of Scientific Engineering and Applied Science (IJSEAS), 2(10), p. 4. 35. Singh, K. (2019) ‘Role of Computer Science in Data Science World | Data Science Blog’, DIMENSIONLESS TECHNOLOGIES PVT.LTD., 7 January. Available at: https://dimensionless.in/role-of-computer-science-in-data-science-world/ (Accessed: 1 September 2020). 36. Solaimani, S., Keijzer-Broers, W. and Bouwman, H. (2015) ‘What we do - and dont - know about the Smart Home: an analysis of the Smart Home literature’, Indoor and Built Environment, 24(3), pp. 370–383. 37. ‘Types of Algorithms’ (2019) EDUCBA, 19 May. Available at: https://www.educba.com/typesof-algorithms/ (Accessed: 1 September 2020). 38. vas3k (2018) Machine Learning for Everyone. Available at: http://vas3k.com/blog/machine_learning/ (Accessed: 1 September 2020). 39. What is IoT (Internet of Things) and How Does it Work? (2020) IoT Agenda. Available at: https://internetofthingsagenda.techtarget.com/definition/Internet-of-Things-IoT (Accessed: 30 August 2020). 40. What is Natural Language Processing? (no date). Available at: https://www.sas.com/en_us/insights/analytics/what-is-natural-language-processing-nlp.html (Accessed: 1 September 2020). 41. Wilson, C., Hargreaves, T. and Hauxwell-Baldwin, R. (2014) ‘Smart homes and their users: a systematic analysis and key challenges’, Personal and Ubiquitous Computing, 19(2), pp. 463–476. doi: 10.1007/s00779-014-0813-0. 42. Zuiden, G. van (2015) Smart Home Retrofit Experience Center : cyberManor. Available at: https://cybermanor.com/experience-center/ (Accessed: 28 October 2020).
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Appendix – I Definitions and Relationships The following section deals with definitions of key terms in the relevant fields of this research at hand and is an undertaking to better understand the technological jargon associated with this topic. Hence this section will deal with: 1. Important Terms 1.1. Key definitions and functioning; 1.2. Scope within architecture; 2. Respective relationships within the defined terms; Terms to be defined – I.
Internet of Things (IoT);
II.
Information and Communication Technology (ICT);
III.
Smart Cities;
IV.
Big Data;
V.
Dash Boards;
VI.
Artificial Intelligence (AI);
VII.
Machine Learning (ML);
VIII.
Deep Learning (DL);
IX.
Neural Networks;
X.
Generative Adversarial Neural Networks (GANs);
XI.
Natural Language Processing (NLP);
XII.
Algorithm;
XIII.
Computational Design;
XIV.
Parametric Design;
XV.
Data Science;
XVI.
Computer science;
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1. Important Terms: I. Internet of Things (IoT): Key Definition and functioning: “The internet of things, or IoT, is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.” (What is IoT (Internet of Things) and How Does it Work? 2020)
Figure 46 Characteristics of IoT | Source: Labmanager
Scope within architecture: Traditional
fields
of embedded
systems, wireless
systems, automation (including home and building
sensor
networks,
automation),
and
control
others
all
contribute to enabling the Internet of things. In the consumer market, IoT technology is most synonymous with products pertaining to the concept of the "smart home", including
devices
and appliances (such
as
lighting
fixtures, thermostats,
home security systems and cameras, and other home appliances) that support one or
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more common ecosystems, and can be controlled via devices associated with that ecosystem, such as smartphones and smart speakers. IoT has other field applications based on similar concept of utilising smart sensors and devices which can be observed in: x Smart homes -
Utilising an ecosystem of devices to increase comfort inside and outside the house.
x Healthcare and Monitoring -
Using IoT enabled devices in hospitals that link patients’ health to nurse stations in real-time.
x Transportation -
Smart Traffic control, Smart Parking, Electronic toll collection systems, Logistics, Vehicular control, Road Safety and assistance, et cetera.
x Building and Home Automation; -
Creation of smart buildings that monitors and regulates energy consumption, user behaviour and building management systems.
x Manufacturing industry; -
Packing and tracking of individual elements from the start to end enables a fully automated system of manufacturing while monitoring and regulating the process via smart sensors.
x Agriculture; -
Automate farming techniques, take informed decisions to improve quality and quantity, minimise risk and waste, and reduce effort required to manage crops using collected data via smart sensors.
x Energy Management; x Metropolitan Scale Deployment and Smart Cities; -
Sensors regulating the city power grids, water supply et cetera based on data collected from across the city via sensors.
x Environmental Monitoring, et cetera;
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Figure 47 Internet of things examples | Source: Edureka
II. Information and Communication Technology (ICT): Key Definition and functioning: “Information
and
communications
technology (ICT)
is
an
extensional
term
for information technology (IT) that stresses the role of unified communications and the integration of telecommunications (telephone lines and wireless signals) and computers, as well as necessary enterprise software, middleware, storage and audiovisual systems, that enable users to access, store, transmit, and manipulate information.” (Information and Communication Technology, 2008)
Figure 48 The components of ICT | Source: Europeyou
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Scope within architecture: ICT is not only prevalent in the form of sensors in the cityscape and households but also plays an essential role in the architectural design process:
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Table 29 Outline of the ICT impact matrix | (Moum, 2008)
III. Smart Cities: Key Definition and functioning:
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The smart city concept integrates information and communication technology (ICT), and various physical devices connected to the IoT network to optimize the efficiency of city operations and services and connect to citizens. Smart city technology allows city officials to interact directly with both community and city infrastructure and to monitor what is happening in the city and how the city is evolving. ICT is used to enhance quality, performance and interactivity of urban services, to reduce costs and resource consumption and to increase contact between citizens and government. (Mayor’s Office of Technology and Innovation, New York City, 2015)
Figure 49 The main components of a smart city | (Sadiku et al., 2016)
Scope within architecture: All components of a smart city will be integrated using service-oriented architecture. Smart city architecture is essentially a large-scale distributed system which is inherently complex and decentralized. (Bawany and Shamsi, 2015) Kitchin observed that a large number of designated Smart Cities fail to incorporate the attributes of culture, politics, policy, and governance and that a technological solution alone is not capable of addressing the deep-rooted structural malaise inextricably to their social dynamics. (Kitchin, 2013)
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The conceptualisation of Smart City, therefore, varies from city to city and country to country, depending on the level of development, willingness to change and reform, as well as the resources and aspirations. A smart city would have a different connotation in India than, say, Europe.
IV. Big Data: Key Definition and functioning: “Big data is a field that treats ways to analyse, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Big
data
challenges
include capturing
data, data
storage, data
analysis,
search, sharing, transfer, visualization, querying, updating, information privacy and data
source.
Big
data
was
originally
associated
with
three
key
concepts: volume, variety, and velocity.
Figure 50 Big Data timeline with AI integration | Source: House of Bots
Scope within architecture:
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Big data has an ever-increasing scope in the field of architectural practice. This is true for entirety of the design, building and post- construction process. Big data analytics collects the generated data from the field of use or the source (environmental and contextual data, construction management, user occupancy and behavioural patterns, et cetera) using IoT and ICT devices and processes the collected data using methods of computer and data sciences which makes processing such large amounts of data simpler, efficient and automated. This data can then be available in dashboards et cetera, for the perusal of appropriate professionals. At an urban scale, big data analytics can help visualise and create effective cities and urban landscapes by making sense of the large data sets collected by the various sensors and devices within the setting. Using artificial intelligence, processing big data sets can reveal sensitive data from within the arithmetic process that can add more value to the scheme to be designed/ upgraded.(Chaillou, 2019)
V. Dashboards: Key Definition and functioning: A data dashboard is an information management tool that visually tracks, analyses and displays key performance indicators (KPI), metrics and key data points to monitor the health of a business, department or specific process. They are customizable to meet the specific needs of a department and company. (Data Dashboards. Definition, Design Ideas plus 3 examples, no date) Behind the scenes, a dashboard connects to your files, attachments, services and API’s, but on the surface displays all this data in the form of tables, line charts, bar charts and gauges. A data dashboard is the most efficient way to track multiple data sources because it provides a central location for businesses to monitor and analyse performance. Real-time monitoring reduces the hours of analysing and long line of communication that previously challenged businesses.
Scope within architecture: With architecture as an industry is becoming more data sensitive, dashboards have a crucial role to play. The dashboards can –
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i.
Present Operational and Analytical Data;
ii.
Present interactive data visualisations;
iii.
Be easy to read and comprehend;
iv.
Ease accessibility and mobility of the practice;
v.
Make reporting more efficient;
vi.
Automate data collection and processing;
The dashboards can provide a decentralized practice where data can be stored and processed under a variety of dashboard types with distinctions in energy analysis, construction management, design efficiency, project management and so on.
VI. Artificial Intelligence (AI): Key Definition and functioning: “The science and engineering of making intelligent machines, especially intelligent computer programs is Artificial Intelligence.” – John McCarthy, Father of Artificial Intelligence. 1956. Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. (Frankenfield, 2020) Computer Science defines AI research as the study of artificial agents: any device that perceives its environments and takes actions that maximise its chance of successfully achieving the goal. AI is not a subset of computer science. AI depends on big data but goes beyond the scope of data science. (Malaeb, 2019)
Types of Learning in Artificial Intelligence –
i.
Artificial Narrow Intelligence (ANI):
Also known as Weak AI, ANI is the stage of Artificial Intelligence involving machines that can perform only a narrowly defined set of specific tasks. At this stage, the machine does not possess any thinking ability, it just performs a set of pre-defined
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functions. Examples of Weak AI include Siri, Alexa, Self-driving cars, and so on. Almost all the AI-based systems built till this date fall under the category of Weak AI. ii.
Artificial General Intelligence (AGI):
Also known as Strong AI, AGI is the stage in the evolution of Artificial Intelligence wherein machines will possess the ability to think and make decisions just like us humans. There are currently no existing examples of Strong AI, however, it is believed that we will soon be able to create machines that are as smart as humans. iii.
Artificial Super Intelligence (ASI):
Artificial Super Intelligence is the stage of Artificial Intelligence when the capability of computers will surpass human beings. ASI is currently a hypothetical situation as depicted in movies and science fiction books, where machines have taken over the world.
Based on the functionality of AI-based systems, AI can be categorized into the following types – i.
Reactive Machines AI:
This type of AI includes machines that operate solely based on the present data, taking into account only the current situation. Reactive AI machines cannot form inferences from the data to evaluate their future actions. They can perform a narrowed range of pre-defined tasks. E.g. Chess playing programs, et cetera. ii.
Limited Memory AI:
Like the name suggests Limited Memory AI, can make informed and improved decisions by studying the past data from its memory. Such an AI has a short-lived or a temporary memory that can be used to store past experiences and hence evaluate future actions. E.g. Self-driving cars, Siri, et cetera. iii.
Theory of Mind AI:
The Theory of Mind AI is a more advanced type of Artificial Intelligence. This category of machines is speculated to play a major role in psychology. This type of AI will focus mainly on emotional intelligence so that human believes and thoughts can be better comprehended. The Theory of Mind AI has not yet been fully developed but rigorous research is happening in this area.
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iv.
Self-aware AI:
This type of AI is a little farfetched given the present circumstances. However, in the future, achieving a stage of superintelligence might be possible.
Branches of Artificial Intelligence – i.
Machine Learning:
Machine Learning is the science of getting machines to interpret, process and analyse data in order to solve real-world problems. ii.
Deep Learning:
Deep Learning is the process of implementing Neural Networks on high dimensional data to gain insights and form solutions. Deep Learning is an advanced field of Machine Learning that can be used to solve more advanced problems. E.g. Selfdriving cars, Alexa, Siri et cetera. iii.
Natural Language Processing:
Natural Language Processing (NLP) refers to the science of drawing insights from natural human language in order to communicate with machines. E.g. Twitter uses NLP to filter out terroristic language in their tweets, Amazon uses NLP to understand customer reviews and improve user experience. iv.
Robotics:
Robotics is a branch of Artificial Intelligence which focuses on different branches and application of robots. AI Robots are artificial agents acting in a real-world environment to produce results by taking accountable actions. E.g. Sophia the Humanoid. v.
Expert Systems:
An expert system is an AI-based computer system that learns and reciprocates the decision-making ability of a human expert. Expert systems use if-then logical notations to solve complex problems. It does not rely on conventional procedural programming. Expert systems are mainly used in information management, medical facilities, loan analysis, virus detection and so on.
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vi.
Fuzzy Logic:
Fuzzy logic is a computing approach based on the principles of “degrees of truth” instead of the usual modern computer logic i.e. Boolean in nature. New computing methods based on fuzzy logic can be used in the development of intelligent systems for decision making, identification, pattern recognition, optimization, and control. (Lateef, 2019)
Figure 51 Branches of Artificial Intelligence | (GN, 2019)
With the potential of AI to be used for harming humanity, OpenAI has been co-founded by Elon Musk to ensure safe development of Artificial General Intelligence so as to benefit all of humanity with its wonderous potential. (OpenAI Charter, 2018)
Scope within architecture: Artificial Intelligence already has had significant effects on the architectural practice and the built environment. In the design stage, new softwares for research, ideation, representation, rendering et cetera have been developed which are pushing the practice into a new era of innovation. The advent of AI in architectural design has already been employed – decoding big data and various relevant datasets to produce a suitable design. Some examples of such softwares is Revit by Autodesk.
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There are different techniques and information machines that are utilised from within the vast field of AI to aid the designer in their process. Some of the techniques utilised are that of Machine Learning, Deep Learning, Neural Networks which include Artificial Neural Networks, General Adversarial Neural Networks and so on. (Chaillou, 2019)
Figure 52 AI generated shape-adaptive floorplans | (Chaillou, 2019)
AI also works in real-time data analysis and controlling various parameters within the space such as energy consumption, building services, et cetera. The potential of AI has also made possible the spatial typologies of Smart homes, Smart Cities which are a combined resultant of advancements in IoT, ICT, Big Data and Artificial Intelligence. Other areas where AI aids architecture are: (Malaeb, 2019) i.
Research and planning;
ii.
Construction;
iii.
Building Information Modelling (BIM);
iv.
Risk Mitigation;
v.
Project Planning;
vi.
Site management and productivity;
vii.
Offsite work- Transportation, Manufacturing;
viii.
Smart Cities;
ix.
Smart Homes;
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x.
Real Estate- Profit mapping;
xi.
Insurance Companies;
xii.
Parametric Architecture;
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VII. Machine Learning (ML): Key Definition and functioning: One major difference between Artificial Intelligence and Computer science is Machine learning. (Malaeb, 2019) Machine Learning is a subset of AI and is based on the idea that machines should be given the access to data, and should be left to learn and explore for themselves. It deals with the extraction of patterns from a large data set. It enables the computers or the machines to make data-driven decisions rather than being explicitly programmed for carrying out a certain task. These programs or algorithms are designed in a way that they learn and improve over time when are exposed to new data. (Atul, 2018) Machine Learning algorithms are an evolution of normal algorithms. They make programs “smarter”, by allowing them to automatically learn from the data provided. The algorithm is mainly divided into: i.
Training Phase: The algorithm identifies and learns about the parameters presented to it in the training data set to establish its own set of quantifiable metrics and parameters which it will utilise in the testing phase.
ii.
Testing Phase: Based on the parameters established during the training phase, the program will use those to perform the task for which it had been designed.
Under Machine Learning there are three categories: i.
Supervised Learning:
Supervised Learning is the one, where you can consider the learning is guided by a teacher. We have a dataset which acts as a teacher and its role is to train the model or the machine. Once the model gets trained it can start making a prediction or decision when new data is given to it.
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Figure 53 Machine Learning program data processing | Source: Edureka
ii.
Unsupervised Learning:
The model learns through observation and finds structures in the data. Once the model is given a dataset, it automatically finds patterns and relationships in the dataset by creating clusters in it. What it cannot do is add labels to the cluster. E.g. It cannot say this a group of apples or mangoes, but it will separate all the apples from mangoes. iii.
Reinforcement Learning:
It is the ability of an agent to interact with the environment and find out what is the best outcome. It follows the concept of hit and trial method. The agent is rewarded or penalized with a point for a correct or a wrong answer, and on the basis of the positive reward points gained the model trains itself. And again, once trained it gets ready to predict the new data presented to it. (Atul, 2018)
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Figure 54 Machine Learning Venn Diagram | Source: https://vas3k.com/blog/machine_learning/
Scope within architecture: In design fields, though, creatives are reaping the benefits of machine learning in architecture, finding more time for creativity while computers handle data-based tasks. While some designers will have trouble pivoting from traditional roles, others will embrace new creative freedoms afforded by machine learning in architecture. Seeing these advances as tools rather than obstacles can lead to freedom from the constraints of old models. With AI and ML being further developed as tools in the field, it is important for the architect to restructure their attention to different parts of the design problem, says Jim Stoddart, Research scientist associate at Autodesk Research. This means that based on machine learning, automated data-based designs are the future of the practice. Despite this shift, creativity will remain the realm of the human mind. And thanks to AI, humans are increasingly being afforded the ability to create and design the world they want to live in and leave the dirty work to the machines. (Muklashy, 2018) In the spatial dimension, machine learning can be applied to datasets to determine and predict behavioural patterns in various spaces to understand new dimensions of space design. E.g. Relating spatial dimensions of a location to the duration of conversations between people to better understand the spatial qualities that drive the duration of such longer or shorter interactions. (Poulsgaard, 2020)
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VIII. Deep Learning (DL): Key Definition and functioning: Deep Learning is a subset of Machine Learning where similar Machine Learning Algorithms are used to train Deep Neural Networks so as to achieve better accuracy in those cases where former was not performing up to the mark. (Atul, 2018) Deep learning models are capable of learning to focus on the right features by themselves, requiring little guidance from the programmer. Basically, deep learning mimics the way our brain functions i.e. it learns from experience. Deep learning uses the concept of artificial neurons that functions in a similar manner as the biological neurons present in our brain.
Figure 55 Deep Learning Neural Network | Source: Edureka
In the above diagram, the layer is encoded with relevant algorithms for identification of the required parameter to produce a suitable result. The input layer is the first stage of breaking down the dataset provided. The following hidden layers further simplify the data using logic-based algorithms and finally the output layer produces the suitable result.
Figure 56 Deep Learning Neural Network Application | Source:
https://vas3k.com/blog/machine_learning/
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Figure 57 Deep Learning Neural Network Classifications | Source:
https://vas3k.com/blog/machine_learning/
Scope within architecture: Deep Learning as a subset of AI and ML, is refining the architectural practice of designing and space making by refining the effect of AI and ML in their (aforementioned) architectural scope.
IX. Neural Networks: Key Definition and functioning: Any neural network is basically a collection of neurons and connections between them. Neuron is a function with a bunch of inputs and one output. Its task is to take all numbers from its input, perform a function on them and send the result to the output. When doing real-life programming nobody is writing neurons and connections. Instead, everything is represented as matrices and calculated based on matrix multiplication for better performance. (vas3k, 2018)
i.
Convolutional Neural Networks (CNN):
They are used to search for objects on photos and in videos, face recognition, style transfer, generating and enhancing images, creating effects like slow-motion and improving image quality.
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Figure 58 Convolutional Neural Networks | Source: https://vas3k.com/blog/machine_learning/
Convolution can be represented as a layer of a neural network, because each neuron can act as any function. In the above image, the object in the input image is understood as a series of matrices of smaller logics which accumulates and forms the next layer and so on, until the logic of the neural network identifies the important defining features for identifying the object in the domain.
ii.
Recurrent Neural Networks (RNN):
Recurrent Neural Network (RNN) are a type of Neural Network where the output from previous step are fed as input to the current step. In traditional neural networks, all the inputs and outputs are independent of each other, but in cases like when it is required to predict the next word of a sentence, the previous words are required and hence there is a need to remember the previous words. (Aishwarya, 2018) Recurrent networks gave us useful things like neural machine translation, speech recognition and voice synthesis in smart assistants. RNNs are the best for sequential data like voice, text or music. (vas3k, 2018)
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Figure 59 Recurrent Neural Networks | Source: https://vas3k.com/blog/machine_learning/
Scope within architecture: The above-mentioned neural networks exhibit the potential of AI in not just architecture, but its universal potential. These neural networks can be used to create more efficient technology base for smart cities and smart homes. The development and application of these neural networks will expand the realisation of architecture research and planning the domain of research is no longer restricted to texts and human perception of images and statistics. CNNs help to decode and gather definite information and insights from previously unused sources of images, statistics et cetera and automate the process of research.
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Figure 60 Neural Network types | Source: asimovinstitue.org
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X. Generative Adversarial Neural Networks (GANs): Key Definition and functioning: Generative Adversarial Networks, or GANs for short, are an approach to generative modelling using deep learning methods, such as convolutional neural networks. Generative modelling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset. GANs are a clever way of training a generative model by framing the problem as a supervised learning problem with two sub-models: the generator model that we train to generate new examples, and the discriminator model that tries to classify examples as either real (from the domain) or fake (generated). The two models are trained together in a zero-sum game, adversarial, until the discriminator model is fooled about half the time, meaning the generator model is generating plausible examples. (Brownlee, 2019)
Figure 61 Typical GAN Architecture | (Chaillou, 2019)
The GAN architecture was first described in the 2014 paper by Ian Goodfellow, et al. titled “Generative Adversarial Networks.”
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Scope within architecture: Generative Adversarial Neural Networks offer a great deal of promise in architectural design practice. With the advent of GANs, the scope of AI from an analytical tool has expanded to being a generative tool as well. The potential of GANs have been explored in creating non-trivial technical floor layout plans while using standard optimization techniques by Stanilas Chaillou, in this thesis done at Harvard GSD. An architectural design tool focusing on GANs is created which is able to generate multiple original, differentiated, optimised and uniquely stylised options of floorplans from scratch (given data sets).
Figure 62 Training set (left) with Generated scheme (right) | (Chaillou, 2019)
Similarly, using GANs’ generative ability, facades can be generated on various city blocks as shown by the students at UCL, KAUST and University of Leeds. (Kelly et al., 2019)
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Figure 63 City building Facades using GANs | (Kelly et al., 2019)
XI. Natural Language Processing (NLP): Key Definition and functioning: It is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyse large amounts of natural language data. (Malaeb, 2019) Natural language processing helps computers communicate with humans in their own language and scales other language-related tasks. For example, NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important. Human language is astoundingly complex and diverse. We express ourselves in infinite ways, both verbally and in writing. Not only are there hundreds of languages and dialects, but within often misspell or abbreviate words, or omit punctuation. When we speak, we have regional accents, and we mumble, stutter and borrow terms from other languages. While supervised and unsupervised learning, and specifically deep learning, are now widely used for modelling human language, there’s also a need for syntactic and semantic understanding and domain expertise that are not necessarily present in these machine learning approaches. NLP is important because it helps resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics. (What is Natural Language Processing? no date)
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Challenges
in
natural
language
processing
frequently
involve speech
recognition, natural language understanding, and natural-language generation.
Figure 64 Evolution of NLP | Source: Xenonstack
Scope within architecture: With NLP exploring and refining fronts of communication between humans and machines, this has definite potential for the architectural practice. While improving the human connection with IoT and ICT, NLP will strengthen the typologies of smart infrastructure and design. As AI strides forward, it is currently important to define the taxonomy of adjectives the are used to communicate with the intelligent machine for it to work properly. (Chaillou, 2019) But in the future, with advancements in NLP and AI, the machine with improved cognition of the human language will be able to become a better tool in the hands of the architect and the client.
XII. Algorithm: Key Definition and functioning: In mathematics and computer science, an algorithm is a finite sequence of welldefined, computer-implementable instructions, typically to solve a class of problems or to perform a computation.
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“To make a computer do anything, you have to write a computer program. To write a computer program, you have to tell the computer, step by step, exactly what you want it to do. The computer then ‘executes’ the program, following each step mechanically, to accomplish the end goal. When you are telling the computer what to do, you also get to choose how it’s going to do it. That’s where computer algorithms come in. The algorithm is the basic technique used to get the job done.” (DeAngelis, 2014) Algorithms are of various types depending on their functions and range.
i.
Recursive Algorithm:
This is one of the most interesting Algorithms as it calls itself with a smaller value as inputs which it gets after solving for the current inputs. In more simpler words, It’s an Algorithm that calls itself repeatedly until the problem is solved.
ii.
Divide and Conquer Algorithm:
This is another effective way of solving many problems. In Divide and Conquer algorithms, divide the algorithm into two parts, the first parts divide the problem on hand into smaller subproblems of the same type. Then on the second part, these smaller problems are solved and then added together (combined) to produce the final solution of the problem.
iii.
Dynamic Programming Algorithm:
These algorithms work by remembering the results of the past run and using them to find new results. In other words, dynamic programming algorithm solves complex problems by breaking it into multiple simple subproblems and then it solves each of them once and then stores them for future use.
iv.
Greedy Algorithm:
These algorithms are used for solving optimization problems. In this algorithm, we find a locally optimum solution (without any regard for any consequence in future) and hope to find the optimal solution at the global level.
v.
Brute Force Algorithm:
This is one of the simplest algorithms in the concept. A brute force algorithm blindly iterates all possible solutions to search one or more than one solution that may solve
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a function. Think of brute force as using all possible combinations of numbers to open a safe.
vi.
Backtracking Algorithm:
Backtracking is a technique to find a solution to a problem in an incremental approach. It solves problems recursively and tries to get to a solution to a problem by solving one piece of the problem at a time. A backtracking algorithm solves a subproblem and if it fails to solve the problem, it undoes the last step and starts again to find the solution to the problem.
¶7\SHV RI Algorithms’, 2019)
A promising field for algorithms is optimization algorithms. In her book Introduction to Genetic Algorithms, MIT Press, 1998, Melanie Mitchell defines Genetic algorithms as – “In computer
science and operations
research,
a genetic
algorithm (GA)
is
a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection.” A common use of genetic algorithms is used as a function optimization and generation. (Fasoulaki, 2008) Also, genetic algorithms are useful tools in pattern recognition, feature selection, image understanding, optimization, evolution, automated programming, machine learning, and teaching behaviour of the robot and so on.
Scope within architecture: With architecture as a practice and humanity as a whole grows to become dependent on various branches of computer sciences for the day-to-day, the need for and dependence on efficient and smart algorithms increases.
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Architecture using CAD-CAM softwares for design generation, optimisation, visualisation is all dependent on various types of algorithms carrying out various smaller tasks to produce the final output. This use and dependency on efficient algorithms have given rise to styles of Parametricism and computational design that are able to define the form and other aspects of the design by defining parameters using a set of algorithms. In practice, Genetic algorithms and other optimisation algorithms aid in the emergence of concepts and concept, and the latter as a means of solving the problems of structural, mechanical, thermal, lighting and more efficient. Genetic algorithms are rapidly replacing traditional design process; and as soon as a need to have a more active role in the future of architecture. (Latifi, Mahdavinezhad and Diba, 2016) In addition to complex calculations which could hardly be made by hand, algorithmic designing for future construction holds additional major potential. As part of the increasing proportion of digitalization and automation, calculated data could, for example, be transferred directly to a 3D printer or other machine which could then swiftly produce the appropriate components. Automation could accelerate, simplify and avoid errors in the construction process to a huge degree, particularly in conjunction with the transparency of an Open BIM. (Man, and machine: How algorithms change architecture, 2018) With the right algorithm, the most diverse requirements for a building can be gathered together. From this, the computer calculates possible
shapes
specifications
which
entered.
meet Every
all
the new
parameter thus influences the overall design. Figure 65 Morpheus Hotel | Source: Zaha Hadid Architects
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XIII. Computational Design: Key Definition and functioning: Computational design is the application of computational strategies to the design process. While designers traditionally rely on intuition and experience to solve design problems, computational design aims to enhance that process by encoding design decisions using a computer language. The rising complexity of modular design and systems introduced computation in the field to ensure feasibility and scalability. It also retained the simplicity of rule-based design. The first CAD software was developed in 1959 for engineering and soon was explored by Nicholas Negroponte as to how machines can enhance the creative process and architecture production in his book “The Architectural Machine”, published in 1970 by MIT Press. The success of computational design is owed to its features that allow: i.
Rigorous control of geometries which boosts design feasibility, reliability and cost;
ii.
Facilitation of easy collaboration with designers;
iii.
Possibility of multiple design iterations than the traditional hand done method;
Despite this, there are shortcomings that gave way to evolution in this field of design such as the repeated nature of tasks made the job mundane and the lack of control over complex shapes. This gave rise to parametric design which addressed these problems and boosted computational design as a practice. (Chaillou, 2019)
Scope within architecture: Design and CAD-CAM tools have made drafting, calculations and visualisation tasks for designers easier and enables efficient management of time and resources of the designer. A number of softwares are used today in the industry such as AutoCAD, Rhino, Revit, et cetera by designers for practicing computational design.
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Figure 66 Some CAD-CAM Softwares for Computational Design | Source: ArchSupply
XIV. Parametric Design: Key Definition and functioning: Parametric design is a process based on algorithmic thinking that enables the expression of parameters and rules that, together, define, encode and clarify the relationship between design intent and design response. The architect/ designer commands a computer system to carry out a set number of tasks by isolating key parameters that would impact the final output of that design stage. These parameters are variable in nature and hence can be adjusted by the designer to produce a set of designs from the same key parameters. Parametric modelling systems can be divided into two main types: i.
Propagation-based systems where one computes from known to unknowns with a dataflow model;
ii.
Constraint systems which solve sets of continuous and discrete constraints;
Parametric design deals with the shortcomings of simple computational design as it is developed to tackle with the repetitive nature of tasks and complex shapes based on variable input parameters and algorithms. Patrick Schumacher, in his book, “Parametricism, a New Global style for Architecture and Urban Design”, demonstrated Parametricism as a result of growing awareness of the notion of parameters within the architectural practice.
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Parametric design has reached a plateau both conceptually and technically in the past 10 years owing to various shortcomings that it hasn’t been able to address to the maximum extent. It assumes architecture as a culmination of simple and fixed parameters and hence fails to address the complexity of actual space planning and design. (Chaillou, 2019)
Scope within architecture: Parametric design softwares are used to ease the process of architecture design and planning and have been extensively used by firms like Zaha Hadid Architects for the realisation of their projects. Grasshopper, developed in 2000 is one of the most extensively used parametric design tool which utilises a visual programming interface for ease of use. Also, Building Information Modelling (BIM) is a more profound revolution, driven by Parametricism where information is used as parameters for building design. This was also started in the early 2000s, by Autodesk. Parametric design offers the potential for “unprecedented levels of control over the building’s geometry” when combined with mathematics. This fusion has enabled Zaha Hadid to translate her iconic forms into reality. Some softwares for parametric design include Autodesk 3DS Max, Autodesk Maya, Grasshopper 3D, Autodesk Revit, Autodesk Dynamo, et cetera.
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Figure 67 Parametric form of Heydar Aliyev Centre | Source: Zaha Hadid Architects
XV. Data Science: Key Definition and functioning: Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data. Data science can undertake tasks of decision making, predictive analytics and visualisation. Despite the variety of tasks that can be completed, data science as a field in isolation requires continuous human monitoring and analysis and can take large durations of time. Artificial intelligence can help speed up and increase efficiency of data science as a field. AI depends on big data but goes beyond data science. (Malaeb, 2019)
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Figure 68 Artificial Intelligence and other field overlaps | Source: https://medium.com/ai-in-plain-english/datascience-vs-artificial-intelligence-vs-machine-learning-vs-deep-learning-50d3718d51e5
Data science enables us to solve a problem with a series of well-defined steps. i.
Collecting data
ii.
Pre-processing data
iii.
Analysing data
iv.
Driving insights and generating reports
v.
Taking decision based on insights
Figure 69 Data Science | Source: https://dimensionless.in/role-of-computer-science-in-data-science-world/
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Scope within architecture: With larger dependency on computational methods and technologies for design, data science as a field is a lucrative and vast field with immense potential in architecture. Big Data analysis, computational and parametric designing, advent of smart spatial typologies and even data driven approach to the design process are all driven by advancements in the field of data science.
XVI. Computer Science: Key Definition and functioning: Computer Science is the study of computers and computational systems. Unlike electrical and computer engineers, computer scientists deal mostly with software and software systems; this includes their theory, design, development, and application. Principal areas of study within Computer Science include artificial intelligence, computer systems, and networks, security, database systems, human-computer interaction, vision and graphics, numerical analysis, programming languages, software engineering, bioinformatics and theory of computing. (Singh, 2019)
Figure 70 Computer Science | Source: https://dimensionless.in/role-of-computer-science-in-data-science-world/
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Scope within architecture: Since the better part of the century now, architecture has evolved with advancements in computer sciences. Big Data analysis, computational and parametric designing, advent of smart spatial typologies and even data driven approach to the design process are all driven by advancements in the field of computer sciences.
Figure 71 Site analysis using computer science | Source: DepthMapX
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Respective Relationships with Defined Terms:
Table 30 Cross relationships table for Background study | Source: Author (Kindly refer to link – https://drive.google.com/file/d/17xSJN2x407nDUwRa8sBC_sX7ed6buDAK/view?usp=sharing ; for viewing digital copy)
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Appendix – II Day in the Life of the Smith Family in Their Smart Connected Home
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(CyberManor, 2015)
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Appendix – III Survey Questionnaire and Data Collected Platform – Google survey Duration – About 3-5 minutes (All Questions are Mandatory) Detailed Analysis Excel Sheet link – https://docs.google.com/spreadsheets/d/1THLcn8f2odUCSsKrzcbhRkxHKs7l_ArRyJDZAWzTcoA/edit#gid=79006536
Questionnaire – 1. Which country do you live in? (Drop Down choices)
Figure 72 Country of origin | Source: Survey, Author
2. How would you best describe your field of work/ education? (Drop Down choices)
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Figure 73 Field of Work | Source: Survey, Author
3. What is your age? a. <18 b. 18-24 c. 25-34 d. 35-44 e. 45-54 f. 55-64 g. 65+
Figure 74 Age | Source: Survey, Author
4. How many people live with you? (Including you) a. I live alone b. 2-4 c. 5-6
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d. More than 6
Figure 75 Number of People living with | Source: Survey, Author
5. How many people living with you use smart phones? (Including you) a. 1 b. 2-4 c. 5-6 d. More than 6
Figure 76 Number of people using smart phones | Source: Survey, Author
6. What is your outlook on Smart Technologies? (Single Choice MCQ) a. They are Intrusive and threaten my privacy b. They are very helpful and make life easier c. They have a difficult learning curve
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d. They are at security risk due to hackers and robbers
Figure 77 Outlook on Smart Technologies | Source: Survey, Author
7. Let’s say you’re buying smart home technologies, rate the following from 1-5 with 1 least likely to buy and 5 most likely to buy. (Scoring type) a. Home Assistants with enabled voice commands and interactive interfaces for recreation, assistance and also potentially controlling other smart devices and applications b. Smart sensors and controllers for energy optimisation within the house c. Smart devices which reduce the household work by automating the tasks such as Smart Washing machines, Smart Floor Sweepers et cetera d. Smart Security devices which ensure and monitor the safety and security of your house with live feedback options such as smart locks, smart doorbells, smart security cameras, et cetera
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Figure 78 Buying Preference for Smart Home Technologies | Source: Survey, Author
Table 31 Average points by age for buying preference | Source: Survey, Author
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Figure 79 Buying Preferences by Age | Source: Survey, Author
8. “Just because it can be, doesn’t mean it should be.” Keeping this in mind, rate the following features in your home from 1-5 with 1 least likely to and 5 most likely to in your opinion be controlled remotely from another device say, your smartphone or a home assistant. (Scoring type) a. House Locks and Security b. Lighting, ventilation and temperature controls c. Household tasks such as washing, cleaning, yard maintenance et cetera d. Lifestyle based features such as warming up coffee in the morning, setting and updating daily routines, et cetera
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Figure 80 Preference of remotely controlling functions | Source: Survey, Author
Table 32 Average points by Age for remotely controlling preferences | Source: Survey, Author
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Figure 81 Preference of Remotely controlling functions by age | Source: Survey, Author
9. “Just because it can be, doesn’t mean it should be.” Again, keeping this in mind, rate the following household activities from 1-5 with 1 least likely to and 5 most likely to be automated. (Scoring type) a. House Locks and Security b. Lighting, ventilation and temperature controls c. Household tasks such as washing, cleaning, yard maintenance et cetera d. Lifestyle based features such as warming up coffee in the morning, setting and updating daily routines, et cetera
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Figure 82 Preference of function automation | Source: Survey, Author
Table 33 Average points for Preference of function automation by age | Source: Survey, Author
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Figure 83 Average points for preference of function automation by age | Source: Survey, Author
10. Smart Home Technologies are being designed and deployed in various ways to make life easier. Rate the following reasons from 1-5 with 1 being the least likely reason and 5 being the most likely reason for wanting to upgrade to a smart home despite higher pricing of currently available technologies. (Scoring type) a. To save time by scheduling and automating household tasks b. To cater to senior citizens needs within the home since smart home enables monitoring, safety and can increase access and social connectivity c. To increase house security d. To improve energy efficiency of the house and its usage e. To simply give into the trend because its trendy
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Figure 84 Purchasing preference when cost isn’t an issue | Source: Survey, Author
Table 34 Average points for Purchasing preference when cost isn’t an issue by age | Source: Survey, Author
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Figure 85 Average points for Purchasing preference when cost isn’t an issue by age | Source: Survey, Author
11. One idea of the Smart Home is to remotely give access to the user for controlling aspects of the home. In your family, how do you think the control of the home would be divided? (Single Choice MCQ) a. One person would control all aspects of the house b. Smaller spaces will be controlled individually and exclusively by residents c. Smaller spaces will be controlled individually with a central override option d. Each space will have functions accessible to only certain residents based on their maturity, responsibility in the household, et cetera
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Figure 86 Control division in a smart home | Source: Survey, Author
12. If you are considering purchasing Smart Home technology, what is the primary reason which you feel would stop you from making the purchase? (Single Choice MCQ) a. High Cost b. Privacy and Security Concerns c. Concerns regarding ease of use and management d. Other (List)
Figure 87 Primary reason for not purchasing smart home technologies | Source: Survey, Author
13. If given the opportunity to, what feature would you want to add/ upgrade in a smart device/ smart service? (Open Responses) (Refer to link)
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14. Considering the strides in the field of smart technologies, would you consider upgrading to a smart house if cost isn’t an issue? (Single Choice MCQ) a. Yes b. No c. Maybe
Figure 88 Considering upgradation to smart homes | Source: Survey, Author
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Appendix – IV Smart Ecosystems 1. Amazon Alexa Amazon Alexa, also known simply as Alexa, is a virtual assistant AI technology developed by Amazon, first used in the Amazon Echo smart speakers developed by Amazon Lab. It is capable of voice interaction, music playback, making to-do lists, setting alarms, streaming podcasts, playing audiobooks, and providing weather, traffic, sports, and other real-time information, such as news. Alexa can also control several smart devices using itself as a home automation system. Users are able to extend the Alexa capabilities by installing "skills" (additional functionality developed by third-party vendors, in other settings more commonly called apps such as weather programs and audio features).
Figure 89 Amazon Alexa based Smart Ecosystem | Source: https://www.youtube.com/watch?v=-_vtoUmkot4
2. IF This Then That (IFTTT) IFTTT derives its name from the programming conditional statement “if this, then that.” What the company provides is a software platform that connects apps, devices and services from different developers in order to trigger one or more automations involving those apps, devices and services.
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Figure 90 IF This Then That based Smart Ecosystem | Source: https://www.youtube.com/watch?v=-_vtoUmkot4
3. Google Assistant Google Assistant is an artificial intelligence–powered virtual assistant developed by Google that is primarily available on mobile and smart home devices. Unlike the company's previous virtual assistant, Google Now, the Google Assistant can engage in two-way conversations.
Figure 91 Google Assistant based Smart Ecosystem | Source: https://www.youtube.com/watch?v=-_vtoUmkot4
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5. Home Assistant Home Assistant is a free and open-source home automation software designed to be the central home automation control system for controlling smart home technology. Home Assistant "core" application software itself is written in Python and its main focus is on local-control and privacy.
Figure 92 Home Assistant based Smart Ecosystem | Source: https://www.youtube.com/watch?v=-_vtoUmkot4
4. SmartThings SmartThings' primary products include a free SmartThings app, a SmartThings Hub, as well as various sensors and smart devices. The SmartThings native mobile application allows users to control, automate, and monitor their home environment via mobile device. The application is configured to fit each user's needs. The app's Smart Setup area, accessible from the app's dashboard, facilitates the process of adding new devices. Customers can use the app to connect multiple devices at once or follow a dedicated path to configure one device at a time. The hub connects directly to a home's internet router and is compatible with communication protocols such as ZigBee, Z-Wave, and IP-accessible devices. It serves to connect sensors and devices to one another and to the cloud, allowing them to communicate with the SmartThings native app.
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SmartThings is integrated with IFTTT ("if this then that"), which enables users to trigger events when certain things happen on different web applications.
Figure 93 Smart Things based Smart Ecosystem | Source: https://www.youtube.com/watch?v=-_vtoUmkot4
6. Homekit HomeKit is a software framework by Apple, made available in the IOS that lets users configure, communicate with, and control smart-home appliances using Apple devices. It provides users with a way to automatically discover such devices and configure them.
Figure 94 Smart Things based Smart Ecosystem | Source: https://www.youtube.com/watch?v=-_vtoUmkot4
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Plagiarism Check
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Fin.
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