2. To familiarize the accessories and communication techniques for IOT 3. To familiarize the different platforms and Attributes for IOT OUTCOMES Course Outcomes: 1. Outline the basic concepts of IOT and its present developments. 2. Analyze the architectural overview of IOT 3. Summarize various protocols in data link and network layer 4. Summarize various protocols in transport, session and service layer 5. Analyse data analytics for IOT 6. Create an application using IOT Module 1: Introduction to internet of things (7 Hours) IoT-An Architectural Overview– Building an architecture, Main design principles and needed capabilities, An IoT architecture outline, standards considerations. M2M and IoT Technology Fundamentals- Devices and gateways, Local and wide area networking, Data management, Business processes in IoT, Everything as a Service (XaaS), M2M and IoT Analytics, Knowledge Management Module 2:IOT Architecture: (8 Hours) Node Structure - Sensing, Processing, Communication, Powering, Networking - Topologies, Layer/Stack architecture, IoT standards, Cloud computing for IoT, Bluetooth, Bluetooth Low Energy, beacons. Module 3:IOT Data link layer & Network layer protocols(8 Hours) PHY/MAC Layer(3GPP MTC, IEEE 802.11, IEEE 802.15), Wireless HART,Z-Wave, Bluetooth Low Energy, Zigbee Smart Energy, DASH7 - Network Layer-IPv4, IPv6, 6LoWPAN, 6TiSCH,ND, DHCP, ICMP, RPL, CORPL, CARP Module 4:Transport, Session and Service layer protocols (8 Hours) Transport Layer (TCP, MPTCP, UDP, DCCP, SCTP)-(TLS, DTLS) – Session Layer-HTTP, CoAP, XMPP, AMQP, MQTT , Service Layer Protocols & Security : Service Layer -oneM2M, ETSI M2M, OMA, BBF – Security in IoT Protocols – MAC 802.15.4 , 6LoWPAN, RPL, Application Layer Module 5:Data Analystics for IOT (8 Hours) Services/Attributes: Big-Data Analytics and Visualization, Dependability, Security, Maintainability. Data analytics for IoT: A framework for data-driven decision making , Descriptive, Predictive and Prescriptive Analytics , Business Intelligence and Artificial Intelligence Importance of impact and open innovation in data-driven decision making. Module 6: Case Studies (6 Hours) Smart cities, Smart Grid, Electric vehicle charging, Environment, Agriculture, Productivity Applications Reference Books: 1. Arshdeep Bahga and Vijai Madisetti : A Hands-on Approach “Internet of Things”, Universities Press 2015. 2. Oliver Hersent, David Boswarthick and Omar Elloumi “The Internet of Things”, Wiley,2016. 3. Samuel Greengard, “ The Internet of Things”, The MIT press, 2015 4. Adrian McEwen and Hakim Cassimally “Designing the Internet of Things “Wiley,2014. 5. Jean- Philippe Vasseur, Adam Dunkels, “Interconnecting Smart Objects with IP: The Next Internet” Morgan Kuffmann Publishers, 2010. 6. Adrian McEwen and Hakim Cassimally, “Designing the Internet of Things”, John Wiley and sons, 2014 7. Lingyang Song/Dusit Niyato/ Zhu Han/ Ekram Hossain,” Wireless Device-to-Device Communications and Networks, CAMBRIDGE UNIVERSITY PRESS,2015 8. Jan Holler, VlasiosTsiatsis, Catherine Mulligan, Stefan Avesand, StamatisKarnouskos, David Boyle, “From Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence”, 1 st Edition, Academic Press, 2014 18EI3024
ROBOTICS AND FACTORY AUTOMATION
Course Objectives 1. To educate the fundamental concepts or robotics 2. To educate on the robot drives and power transmission systems 3. To educate vision system for robotics Course Outcomes 1. Recall the concept of robotics 2. Summarize building blocks of automation Instrumentation Engineering
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