Research Highlights from the Centre for Internet of Things and Pervasive Systems

C-IoT Research

Research Highlights from the Centre for Internet of Things and Pervasive Systems

July 2021

WELCOME

The Internet of Things is transforming all aspects of our lives, from entertainment, social engagement, and healthcare, to business, industry, and achieving Net Zero targets. This transformation has been underpinned by advances across the spectrum of electronics and computer science, for example enabling enhanced connectivity, intelligence, battery-life, ambient sensing, and computational ability.

The Centre for IoT and Pervasive Systems (C-IoT) is a hub for Internet of Things research and education in the School of Electronics and Computer Science at the University of Southampton. Established two decades ago as the Pervasive Systems Centre, today’s Centre remains at the forefront of multidisciplinary IoT research. We aim to encourage collaboration, spark new ideas and share learning to advance the next generation of IoT technologies.

We draw on the breadth of expertise within the School of Electronics and Computer Science and the wider University, bringing together specialists in sensor technology, energy harvesting, wireless communication, data management, artificial intelligence, and the many other technologies which contribute to the IoT stack. Our researchers seek solutions to real-world problems, working with partners in industry and academia to maximise impact. We collaborate with companies and other stakeholders from a wide range of sectors including health, manufacturing, construction, energy, defence, automotive and aerospace.

This magazine provides a snapshot of research highlights across the spectrum of our research in IoT, and provides insight into some of the key challenges and accomplishments in this domain.

Professor Geoff Merrett Head of Centre for IoT and Pervasive Systems School of Electronics and Computer Science

SENSOR TECHNOLOGIES

Southampton has been at the forefront of sensor research for several decades – before the evolution of the IoT we were advancing technologies in areas such as thick-film printed sensors and micro-electromechanical systems (MEMS) – the microchipbased sensor systems that are now embedded in every smartphone – which have since become integral to the IoT. Our researchers also conducted early work on networking sensors together to gather richer and more robust data, a principle that underpins today’s IoT systems.

While numerous types of sensor are now commercially available and widely used in the IoT, there is still room for innovation and optimisation. Our researchers are continuing to investigate new materials and techniques for sensing, and finding ways to measure the previously unmeasurable.

Sensor technology research comes with a number of considerations and constraints. Sensors must be the right size for their application – this could mean small enough to be embedded into a credit-card sized system, or fabricated at the nanoscale for molecular-level sensing. For mass deployment, they must be cheap to produce and have low power requirements. Because they are likely to be densely deployed, a lower accuracy may be tolerated when compared with other systems. However, in the case of niche, safety-critical applications, cost is less of a consideration and accuracy is paramount.

Flexible, wearable sensors

Southampton has a track record of groundbreaking research into sensors for smart fabrics.

The development of wearable sensors presents particular challenges – they must be very small, flexible, durable and potentially washable, as well as being able to maintain their sensitivity when integrated into a textile.

Our researchers have discovered new ways to tackle these challenges, for example by developing active inks that can be printed onto fabrics, ultra-thin dyes that can be incorporated into textiles, temperature-sensing filaments that can be woven into the structure of a fabric, and methods to encapsulate textile-based gas sensors to improve their durability without affecting their sensitivity.

This field of research opens up a huge range of potential IoT applications, particularly in fields such as fitness and healthcare, helping people maintain their independence in later life, and smart city applications where wearable sensors could enable crowdsourced data on measurables such as air quality.

Printed sensors

Another longstanding area of research at Southampton is the development of sensor systems that use active ‘inks’ to detect or respond to things in the environment. These are fabricated using a screen-printing process, making them much simpler and to produce than traditional integrated circuits, which require expensive cleanroom facilities.

One such process uses a substrate of ceramic, printed with silver as the conducting material, most of which can then be isolated with a layer of glass. The exposed active area is then covered with a printed layer of the active sensing material, which has been mixed with powdered glass and liquid to form an ink. This is then fired at high temperature to produce a sintered structure. Robust, reliable and cheap to produce, these sensors are ideal for environmental and agricultural settings. They have been used to

detect salt levels in farmland and could be tailored for many other types of pollution monitoring.

Another example of a screen-printed sensor being developed at Southampton is a motion sensor for use in healthcare. The sensor monitors the respiratory rate of patients – either in hospital or at home – transmitting data to clinicians and enabling them to respond to any significant changes in a patient’s breathing rate.

Sensors for extreme conditions

Our researchers are also expanding the boundaries of what can be measured by designing sensors for harsh conditions – for example, the extreme heat of a jet engine. Although turbine blades will melt at temperatures below 1,400 degrees Celsius, they actually operate at temperatures above this in order to maximise the thermal efficiency and power output of the engine. To make this possible, cooled air is pumped through holes in the blades, creating a layer of insulation between the blade and the hot gas flowing over it. If the air flow fails, the turbine blade will melt, so being able to monitor the temperature of the blade while the plane is in flight would be incredibly useful. However, it is a big challenge, as most electronics will only operate upto around 120 degrees Celsius.

Our researchers are looking for a solution in the form of acoustic sensors. The sensors send acoustic waves over the turbine blade surface to measure the speed of sound. The speed of sound is very temperature dependent, enabling the researchers to infer temperature changes from the measurements. The use of acoustic waves means the actual sensors and electronics can be placed away from the intense heat of the

turbine and early results show they have the potential for taking measurements with a high level of precision.

https://www.e-textiles.ecs.soton.ac.uk

Other specialist sensors designed specifically for extreme conditions include custom-built, super-robust sensors used to monitor movement and pressure beneath glacial ice.

Nanoscale sensors

Another branch of sensor research is looking at novel ways to detect microscopic targets. Working in the University’s state-of-the-art cleanrooms, researchers are developing sensor systems that take advantage of the properties of zinc oxide nanowires. A cheap, plentiful material with low environmental impact, zinc oxide reacts readily to positive or negative charges. By analysing these reactions it’s possible to identify particular micro or nanoscale targets, such as pollution particles or abnormalities in DNA that could indicate disease. Our researchers are focusing on biomedical applications, but the technology could also be adapted for environmental and other types of sensing.

“If you can transmit the turbine blade temperature data in real time from anywhere in the world, you can see what’s happening much more quickly, especially if you apply predictive techniques such as machine learning and AI to help spot early trends.”

Professor Nick Harris Head of Smart Electronic Materials and Systems

“Having sensing systems that are integrated into textiles, so that all people have to do is get dressed — that’s the focus of our research. They can be used to check on people’s welfare by tracking their location and activities, and monitor the environment we live in.’

Professor Steve Beeby Professor in Smart Electronic Materials and Systems

Sensors are an essential element of all IoT systems; by gathering information about their environment they act as the interface between the physical and cyber worlds.

enable the discovery, sharing and processing of data at the large scale required by the IoT, while reducing the security risks and access issues associated with centralised systems.

A benefit of centralised data stores is that they make it easy to apply machine learning and AI techniques to datasets. However, there is a risk that centralised stores become silos where the data can only be processed by the silo owner, which in turn limits the innovation potential of that data. In addition, central data stores present a greater cyber security risk. To overcome this, our researchers are looking at secure federated approaches to machine learning on IoT data.

DATA MANAGEMENT, ANALYSIS AND INTERFACING

The IoT is all about data, with billions of connected devices generating information in vast quantities. This creates new challenges around how IoT data can be structured, accessed and analysed to harness its full potential, while respecting

At the device level, cheap, low-power sensors may collect ‘noisy’ data, so there’s a need for techniques to correlate or recalibrate the data to ensure it is fit for purpose. Limited power and bandwidth at device level also limits the amount of local processing that can be carried out and the amount of data that can be transmitted.

Further up the IoT stack there are issues around how to integrate data from multiple sources in a meaningful way, to make sense of the physical phenomena it represents. Data infrastructures need to be able to gather data on a large scale and also enable data discovery – that is, finding the specific parts of the data needed for a particular purpose – while ensuring privacy and security. And techniques are needed to analyse real-time data streams as well as historical datasets.

Our researchers are working across the spectrum of data management, analysis and interfacing, in areas such as the design of infrastructures for the collection, sharing and analysis of data, techniques for intelligent data analysis and assigning data provenance, and the meaningful engagement of individuals and policymakers with the IoT data ecosystem.

Decentralised data infrastructures

The storage, management and processing of IoT data relies on effective data infrastructures which are decentralised. These data infrastructures are systems consisting of data assets, the different organisations that operate and maintain them, and the processes and policies that underpin the use and management of data. Our researchers are investigating how distributed, decentralised data infrastructures can

Establishing data provenance

Another area of research at Southampton focuses on data provenance – that is, information about how data has been generated, stored and processed in the past.

Provenance is important because knowledge about how data has been gathered and processed can affect decisions about how to analyse it, for example to allow for anomalies in the data collection. However, in an IoT context, it’s unrealistic for every device and every processing node in a network to generate providence information about each piece of data.

As part of an Engineering and Physical Sciences Research Council-funded project, our researchers

2 https://dais-ita.org/pub

are looking at ways to analyse data to retrospectively understand its provenance. By examining datasets for characteristics that indicate links or relationships between different pieces of data, they can infer what has happened to the data between certain points in the data flow, without the need for every IoT device to be provenance enabled. They have tested their techniques using existing datasets – for example data on smart city transport systems, parking and pollution levels – to evaluate their results and refine the tools.

Making smarter data decisions

Our researchers are using AI and machine learning techniques to improve IoT data management and processing. Southampton is a partner in the Distributed Analytics and Information Science International Technology Alliance (DAIS ITA) , a joint UK/US governmentfunded project focusing on the design of information systems for use in disaster management or defence settings. The project has looked at scenarios such as the deployment of IoT systems and computation in fast-changing and poorly connected settings – for example the aftermath of an earthquake or a combat zone. The focus of research at Southampton was how best to distribute computational tasks in this type of dynamic context, where there is a need to process data from numerous sources but where processing capacity and network reliability are limited, and where multiple organisations are working together but may want to retain autonomy over their own assets.

The research involved designing intelligent algorithms and modelling them using real

datasets to see how they performed in different scenarios, for example with either constrained or abundant computational resources, combined with either very unstable or robust communications networks. The principles could be applied further to any system that draws on data from multiple IoT devices and where there may be a need to balance competing tasks – for example to prioritise traffic management and scale back other types of monitoring across a smart city infrastructure.

Other projects that are using smart algorithms to improve data management include the use of machine learning to intelligently optimise the points at which an IoT device shares its data, in order to balance the energy resource available for sensing and transmitting data and therefore maximise the lifespan of the device. Another study looked at how to enable sensors to describe the data they were collecting in a machine-readable way, so that an automated system could understand what it could infer from the data. This was applied to sensors used in hazardous industrial environments, such as carbon monoxide and heat sensors, to improve health and safety monitoring systems.

Incentivising reliable usergenerated data

IoT systems are not always solely reliant on machine-generated data – some also use information contributed by people. One aspect of our data management research focuses on the reliability of user-generated data – and the fact that users may manipulate the data they reveal about themselves. One example is electric vehicle (EV) charging – in the future the use of EVs will be

users’ data preferences.

“When it comes to the IoT and data, what excites me are these issues around scale, distribution and decentralisation — how we can discover, share and process data on this very large scale.”

Professor Thanassis Tiropanis Professor in Web and Internet Science

“Just like a work of art or antique, data has a past and a history. Where did it come from, how was it created, processed, managed and stored? Knowing this gives you a better understanding of how you should use it.”

Professor Age Chapman

Head of Digital Health and Biomedical Engineering

widespread but the grid may not have the capacity to charge them all at the same time. Smart algorithms can be used to schedule charging to avoid grid overload, informed by data about user needs and preferences to prioritise charging times. For example, some users might need to get to work at a specific time every day, while others might want to go shopping but with no fixed schedule.

However, this type of system relies on people being truthful about their requirements. Southampton is at the forefront of research that uses game theory, which is a way of modelling the behaviour of self-interested users, or agents (which could be software or people) within a system, to understand how people interact with EV charging systems. The work looks at how to deal with the challenge of self-interest by incentivising people to be truthful, giving them an advantage such as a financial discount if they can be more flexible about when they charge their car.

The researchers used computational modelling with real data from large-scale EV trials about the timing of car use, distances travelled and how much battery power was drained, to validate their charging system algorithms. Researchers also trialled a user interface in the form of an online game, where the player runs an EV delivery service and has to interact with smart charging mechanisms to estimate how much electricity they would need and how much they’d be willing to pay for it. This showed that for optimal user interaction, these interfaces need to be very simple, asking broad questions rather than seeking fine-grained data about users’ preferences and needs.

Looking ahead, our researchers have begun a five-year Citizen-Centric AI Systems project 3 , funded by UK Research and Innovation through a Turing AI Acceleration Fellowship – a prestigious award made as part of the UK government’s AI strategy. This will dig deeper into questions around incentive mechanisms, looking at how people can interact with and understand them. The research will include working with industry partners, including EA Technology, Siemens Mobility and Jaguar Land Rover, to test new mechanisms in real EV

POWERING THE IOT

charging systems. It will also look at smart energy and smart transportation more broadly, for example examining ride sharing and on-demand transport systems and how people can be incentivised to provide information about their needs truthfully to optimise these systems.

Optimising crowdsourced data

Another area where people and devices interact to collect data is crowdsourcing. Our researchers have looked into the optimisation of crowdsourced data – for example the design of algorithms to advise people of the best time or place to collect pollution data. This could be to get a good spread of readings in terms of timing and geographical location, or to validate ‘noisy’ data by getting multiple readings at the same place and time. This work has the potential to inform measures to reduce the impact of pollution, for example by diverting traffic to prevent pollution built-up or alerting people to avoid areas of high pollution.

Putting users in control

From smart doorbells and heating systems to virtual assistants, the use of home IoT technology is growing fast. Generally the terms of use mean users have to accept that the data these devices generate will be shared with service providers and perhaps sold on to other entities; however, this raises issue of data privacy and consent. To address this, our researchers are looking at access control –that is, the ability of IoT device owners to specify what subsets of data other entities can access and how they can process it. The aim is to give users more fine-grained control over their data so they can make informed choices about what and how much they share. For example, someone may be comfortable with sharing data from individual devices but would prefer data from different devices not to be aggregated, as this would reveal more about their lifestyle and activities.

Research in this area is tackling technical challenges such as how decentralised infrastructures can help to enforce access control by keeping as much data as possible at the edge of a network rather than combining it in a centralised store, the use of metadata to enable more

fine-grained control, how data management models can enable more meaningful consent about IoT data use, and ways to ensure users’ consent preferences are honoured.

There is also a sociotechnical aspect, such as enabling people to have the digital literacy to participate in the wider dialogue about privacy and control, and designing user interfaces that allow people to make informed decisions about data consent in a manageable way.

Another branch of research is finding ways to overcome the operational and cultural barriers that prevent businesses from sharing mutually beneficial data, through risk assessments that build trust between stakeholders.

Work at Southampton on the sociotechnical aspects of data infrastructures and access control is contributing to wider debate in this area. For example, our researchers contributed their expertise to a report on the European digital infrastructure and data sovereignty 4 , using different scenarios to demonstrate to policy makers what would happen if data protection and infrastructure controls were tightened or loosened.

However, traditional battery power is not the ideal solution, given the impracticality and expense of replacing or recharging spent batteries in billions of devices, and the environmental impact of disposing of them. Since undertaking the first EPSRC-funded energy harvesting project in 1999, Southampton has a longstanding track record of innovation in the field of energy harvesting technologies. These are the components within IoT devices that locally generate electrical power from ambient sources such as light, vibration and temperature differences. For example, vibration-powered sensors developed by our researchers have been successfully commercialised

5 http://eh-network.org/

through spin-out company Perpetuum. We are continuing to investigate new ways to power the IoT, to make devices as energy efficient as possible and to balance the trade-offs between functionality and a low ‘power budget’.

Recognised internationally as a centre of excellence in this field, Electronics and Computer Science at Southampton hosts the UK’s Energy Harvesting Network5 established in 2010, which aims to encourage collaboration and disseminate the latest advances.

3 https://ccais.soton.ac.uk

4 https://www.eitdigital.eu/newsroom/news/archive/article/new-report-on-european-digital-infrastructure-and-data-sovereignty/

Whether sited in remote locations for environmental monitoring or attached to the moving parts of machinery, most IoT devices need to be locally powered – i.e. not plugged in – in order to fulfil their function.

System-level and energy-driven design

One area of research takes a system-level approach to IoT device design, aiming to co-design the energy harvesting and sensor components for maximum efficiency and reliability, tailored to the required application. Work is also ongoing to build intelligence into devices to enable them to adapt their performance to the available energy supply –known as energy neutral behaviour.

Our researchers have used this holistic approach to develop wireless energy harvesting sensor systems to monitor the health of engines and components in aircraft, marine vessels and other vehicles. One example is a credit-card sized, self-powered smart sensor designed to be integrated into a helicopter blade. The device uses a screen-printed piezoelectric generator to harvest energy from the blade’s vibrations, integrated with energy management, sensing and data transmission components, to sense and send data about the condition of the blade. In a similar vein, researchers are looking at how to harness the heat or motion inside jet engines to power a real-time monitoring system for their bearings.

application requirements and designs them in tandem. This approach is particularly useful for problems where the energy harvesting method dovetails with the application.

One example is a step counter that harvests energy from the insole of a shoe. Each step generates a small spike of power which indicates that a step has occurred while also powering the processing and communication of step data. The sensor dies (its power runs out) between each step and there is no need for energy storage. Unlike traditional step counters that are powered continuously to monitor motion and process data to detect steps, the power itself is used as a way of indicating that the step has occurred.

Another example is a bike computer that generates energy each time a magnet on the wheel passes a device mounted on the fork – the energy spike can be used to extrapolate information about speed and distance as well as powering the device, removing the need for any energy storage.

Energy harvesting innovation and optimisation

As well as looking at whole-system approaches, our researchers are working on enhancing existing energy harvesting technologies and developing new ones, building on a long heritage of innovation in this field.

Other longstanding areas of development include piezoelectric energy harvesting, which turns mechanical pressure into power, electromagnetic energy harvesting, which generates power using vibration or motion (the technology exploited by spin-out company Perpetuum), and thermoelectric energy harvesting, which uses temperature differentials.

We are also developing new areas, such as temperature-driven phase change generators, that exploit the feature that gases have bigger volume than liquids for a given mass. By turning a liquid into a gas and back again using daily temperature variations, the resulting change in pressure can power an energy harvester. Such systems offer the potential to be more robust than solar power for some environmental IoT applications.

Work is also ongoing in the field of wireless power transfer (WPT), through ambient radio waves such as mobile phone signals, or direct energy broadcast. While commercial systems exist for energy broadcast, there is lots of potential for improvement – for example looking at the design of antennae to make the transmission more directional. Another area of our WPT research is inductive coupling, which involves the transfer of power via coils that can be printed onto clothing or equipment. By tuning the coils and operating them at a specific resonant frequency, it’s possible to increase the range over which power can be transmitted. One application for this work is in

Powering the IoT

industry, where sensors in hard hats are being used to improve safety and efficiency on construction sites.

Pioneers of vibration energy harvesting

Energy harvesting has been a focus at Southampton since the 1990s, when our researchers developed novel microgenerators able to convert the vibrations from industrial machinery into energy for wireless sensors. This technology was commercialised through the formation of spin-out company Perpetuum in 2004, which produces smart sensors deployed in the rail industry for real-time condition monitoring of train axle bearings, wheels and track. Perpetuum’s systems enable rail companies to plan maintenance according to need, reducing costs without compromising the safety and reliability of their fleet. The company has been hugely successful, expanding to employ more than 80 engineers, and was recently acquired by Hitachi Rail.

Flexible and wearable IoT devices While there are numerous wearable IoT devices already on the market, such as activity trackers and smart watches, research at Southampton is taking wearable tech to the next level in the form of smart clothing. Work is underway on energy harvesting techniques that can be integrated into textiles for kinetic, solar and wireless power transfer – for example, developing fabric with inbuilt photovoltaic properties rather than using traditional solar cells.

When it comes to powering wearable IoT, another challenge is power storage. Conventional batteries are too rigid to incorporate into textile-based devices, so work is underway on new flexible batteries and supercapacitors that can be integrated into fabric.

Innovation in this area is opening the door to a range of new technologies – activity sensors to help monitor the wellbeing of older people and wearable pollution sensors to provide data for smart city systems are just two applications being investigated by our researchers.

Another example of system-level design is a project undertaken with a local ferry company, during which our researchers developed and deployed condition monitoring sensors powered by the vibration of the engine. This resulted in the first real-world demonstration of a tunable energy harvesting sensor system that could adapt to different speeds rather than working at a fixed frequency.

Energy-driven and intermittent computing

Researchers are undertaking leading investigation in the field of energy-driven design, which looks at the energy resources and

The ambient energy sources used to power IoT devices, such as light or motion, tend to be dynamic and therefore don’t provide a constant energy supply. Our researchers are investigating ways to solve some of the problems this poses, particularly when the device size and cost has been minimised by removing batteries or other forms of energy storage. For example, a device may be in the middle of processing some data when its power cuts out. How can its position be saved so that it can continue where it left off when the power returns? And what’s the best way to manage communication between intermittently powered devices, where the receiving device might not be powered at the time data is transmitted?

Innovations in electromagnetic energy harvesting include the development of a novel airflow energy harvester, which generates power from the oscillation created by moving air. The idea is that airflow-powered sensor systems can be placed in air conditioning, ventilation or heating vents, using power generated from the airflow in the vents to sense and transmit data about temperature, humidity and the presence of gas, and thereby contributing to safer and more efficient buildings management.

Thermoelectric energy harvesting projects have included putting sensors on radiators for smart home energy management, in aircraft engines for condition monitoring and in agricultural sensors, and our researchers are continuing to develop and optimise thermoelectric materials and devices. Harvesting thermoelectric energy becomes more challenging where heat differentials are small – for example, in wearable devices the difference between body temperature and the ambient temperature – and this is another area under exploration.

“A vision for the future could be that when manufacturers design new electronic devices, ‘without batteries’ could become the default.”

Professor Geoff Merrett Head of Centre for IoT and Pervasive Systems

SECURITY, SAFETY AND PRIVACY

With new technologies come new risks and threats, and the IoT is no exception.

These can take various forms, from device takeovers to the introduction of malicious software into an IoT network via its hardware. IoT devices and systems are vulnerable to these types of attacks for a number of reasons. Device power constraints mean there is limited capacity to apply the types of security measures you would find on a computer, for example. IoT devices are often located in the environment, giving systems an increased ‘attack surface’. And when it comes to developing IoT devices for the mass market, manufacturers tend to focus on getting products to market cheaply and quickly, with security a lesser priority. The ubiquity of IoT systems also raises questions about the huge quantities of data they produce – how the reliability of data can be assured, and how consent and data privacy can be managed.

Security, Safety and Privacy

Dr Basel Halak Associate Professor in Cyber Security

Southampton is a leading hub for cyber security research in the UK, recognised by the National Cyber Security Centre as an Academic Centre of Excellence for Cyber Security Research6 Our researchers are contributing to new developments in this area, focusing on the specific challenges posed by distributed, cyber-physical systems.

Energy-efficient device security

At the hardware level, one stream of research is developing energy-efficient authentication and security protocols that take the low ‘power budget’ of IoT devices into account. These enable systems to detect whether an adversary has infiltrated the network and deployed a malicious device.

The protocols use a type of hardware security primitive known as physically unclonable functions. This harnesses the tiny variations inherent in every IoT device, which are created by variability in the integrated circuit manufacturing process and can be detected by observing the device’s behaviour in the network traffic. These variations create a unique fingerprint for each device, which the protocol identifies and authenticates. The system can then spot anything incongruent by observing the network traffic, applying machine learning to identify any unexpected behaviour. And because the fingerprint is generated from the underlying hardware, it is much harder for an attacker create a forged device to mimic it.

Protection against physical attacks

As the interface between the physical and cyber world, it’s necessary for IoT devices to be located in the environment, making them susceptible to physical or invasive attacks. Supported by Arm and the Royal Academy of Engineering, our researchers are developing new techniques to remotely detect whether a device has been tampered with, with a focus on safety-critical or sensitive applications such as power infrastructure and defence.

Based on data from physical experiments, they are using machine learning to classify these types of attacks and differentiate them from non-malicious environmental changes that may affect a device, so that the system can respond accordingly.

Identifying malicious software

Like commercial software, malware evolves over time – as security controls are improved, malware developers will release new versions of their malware to attempt to bypass them. The different versions, similar but with variants, are seen as part of a malware ‘family’, which evolves and grows over time. From the malware analyst’s point of view, it’s important to work out whether a new malware is part of an existing family, as this offers a better understanding of how to deal with it.

To provide this information, our researchers are developing techniques to understand whether new android malware is part of an existing family. Using existing malware datasets, the malware is executed on a device and metrics are collected, such as how much battery, memory and processing capacity is used.

By applying machine learning the researchers can identify whether this pattern of resource usage resembles those of existing malware families. Ultimately this information could be used to build protection into devices more quickly and give forensic analysts more knowledge to deal with infected devices.

Identifying and tackling propagation attacks

IoT systems are vulnerable to spatial-temporal propagation – where, by accessing one device, a hacker can set off a cascade of failure across a network of devices over time. What seems like a minor attack, if not caught quickly, can introduce major problems.

Our researchers are working on techniques to define, identify and defend against these types of attacks, looking at a range of applications including smart energy meters, vehicle-tovehicle communications and sensors in offshore oil systems.

Privacy and data consent

IoT devices generate vast amounts of data, and our researchers are investigating questions around data ownership, use and privacy, and human interaction with the data system. For example, how do you balance the trade-off between making personal data more private by adding ‘noise’ or aggregating and summarising it, while retaining its usefulness for its intended application?

One project is looking at privacy in relation to a future ‘internet of vehicles’ (IoV), where connected vehicles will generate data about people’s location data, other road users and devices observed by the vehicle, and communication between vehicles. The research is exploring the balance between collecting the minimum personal data while still enabling the IoV to operate efficiently and safely, how more transparency about data use could incentivise people’s participation where personal data is needed, and the design of automated interactions to negotiate data use agreements, giving people more control over what they share.

Automated negotiation was the focus for another study which tackled issues around meaningful consent for the digital economy. Our researchers looked at how an intelligent agent that understood a user’s preferences could negotiate on their behalf about finegrained data control, balancing user privacy with service providers’ need to gather data for their service, and allowing the wider system to work at an economic level. Other streams of research in the field of data privacy include work on decentralised data architectures to reduce the security and privacy risks associated with central data stores.

Trustworthy

IoT systems Southampton is a partner in the PETRAS IoT Hub, a programme funded by UK Research and Innovation which is investigating different aspects of IoT safety and security. Our researchers are working alongside Web Science colleagues on projects examining at the reliability of IoT system data, how secure data sharing can be enabled, and users’ perception of the trustworthiness of the system.

Other projects on the topic of trust and the IoT include designing mechanisms to promote trust among industry stakeholders to enable mutually beneficial data sharing and data provenance techniques to establish the trustworthiness of IoT data.

The increasing use of smart systems in our homes, buildings, factories and cities raises questions about safety and privacy, as well as creating new opportunities for hackers and cybercriminals.

“We hear stories about baby monitors being hacked, for example, because when companies are developing new devices they prioritise price over security. That’s changing, with institutions like the IoT Security Foundation releasing guidelines for companies to follow. But generally many IoT applications are consumer driven, so one challenge is to make devices that are secure but also affordable.”

COMMUNICATIONS AND NETWORKING

The ability to communicate – typically wirelessly – is a fundamental function of IoT devices. Southampton has been at the forefront of wireless communications, signal processing and service management research for more than three decades, feeding into the standards and protocols for each generation of mobile technology.

With 5G (and eventually 6G) opening up more capacity for massive machine-type communications, the Centre’s researchers are developing techniques for more efficient transmission to enable the mobile network to accommodate the ever-growing number of IoT devices. They are also looking at how best to optimise communication within networks of low-power or intermittently powered IoT devices.

As with other aspects of IoT technology, the challenges around communication and networking vary depending on the application. For some IoT systems – for example those used in smart vehicles – high-speed communication is essential and the transmission of large volumes of data, such as streamed video, might be necessary. Other devices, such as environmental sensors, might only need to transmit a small amount of data each day, but may only have a very small power resource with which to do it. Much of our research focuses on this tension between the amount of energy a device is available for communication, the quantity of data it needs to transmit, and how far and how fast the data needs to go.

“One challenge is how to make efficient use of the spectrum resources, because the spectrum allocated for supporting this type of wireless communication is very limited. Supposing we have a billion IoT devices, all of them wireless, on a bandwidth of 10MHz – how do we use that 10MHz of bandwidth to support the devices? That’s the challenge we’re working on.”

Professor Lie-Liang Yang Professor in Next Generation Wireless

Optimising wireless communications for the IoT

We have research streams looking at how the technologies within 5G and beyond can best support the IoT’s growing communication and networking needs. For example, we’re part of a joint project with the University of Surrey and industry partners on New Air Interface Techniques for Future Massive Machine-Type Communications. This project, which funded by the Engineering and Physical Sciences Research Council (EPSRC), is developing new techniques supporting efficient wireless data transmission in the wireless networks with ultra-densely distributed devices.

Transmitting data wirelessly involves converting it into radio waves that travel through the air –this stage is the air interface. However, the air interface processes signals over very crowded and expensive radio frequencies, and too much data traffic across these limited frequencies can lead to interference. Our researchers are developing new techniques for sending and receiving signals, and novel designs of the signals themselves, to make the most efficient use of the radio frequency spectrum.

Our researchers were also key players in FLAME7, a four-year EU-funded programme that tested the adoption of 5G in the media sector, designing network architecture to meet the exponentially increasing demands on mobile broadband access. The programme conducted 50 trials with partner companies across Barcelona, London and Bristol, where software-defined 5G infrastructures were deployed as service delivery platforms, enabling localised content delivery with lower latency and higher efficiency.

Another area of investigation is grant-free multiple access (GFMA). In the current cellular system, each time someone uses their phone it has to request access to the network from the base station – known as grant multiple access. But in the context of millions of IoT devices, requiring a ‘handshake’ for each connection would overwhelm the base station, result in heavy overhead, long delay and resource wastage. For low-power IoT devices, it would also use up limited energy and computational resource within the devices themselves.

7 https://www.ict-flame.eu/index.html

“This project was all about managing cloud native service delivery into the edge of networks. As part of the Internet Engineering Task Force (IETF)

‘Computing in the Network’ Research Group, we’re exploring novel use cases pushing software defined connectivity at layer 2 further into device.”

Professor Michael Boniface Director of IT Innovation Centre

A GFMA system removes this problem by enabling devices to send their information without requesting access. However, it adds complexity at the base station, which is no longer in control of what type of information is coming in and from which IoT devices. Our researchers are helping to overcome this challenge by designing high-efficiency GFMA schemes and the algorithms that will enable base stations to understand and effectively manage the network traffic.

Communication in motion

As IoT devices become integral to vehicles and transport systems, the Centre’s researchers are exploring new ways to ensure effective wireless communication when travelling at high speed. For example, how can IoT devices on a plane or train communicate with a fixed station on the ground, and how will the autonomous vehicles of the future exchange information while travelling on the motorway? In these contexts, the speed of travel causes problems such as the Doppler frequency shift and the wireless channel changes very quickly, creating rapid changes in the quality of the transmission. Our researchers are exploring spectrum sharing schemes and multiple access schemes to overcome this.

They are also looking at the optimum routing protocols for networks of smart vehicles, where it’s useful to share information such as accident and traffic information, or stream entertainment for passengers. This can be done via fixed base stations along the route, or the vehicles can transmit information between themselves. Using theoretical design and computer simulation methods, our researchers have found that a hybrid technique that uses vehicles and the roadside infrastructure is the most efficient approach.

Using smart devices to extend the communications infrastructure

Smart IoT devices can themselves be used to enable communications in circumstances where the existing infrastructure is unable to meet demand – for example a large crowd in a football stadium. In scenarios like these, teams of drones can serve as temporary base stations and our researchers are using optimisation and machine learning techniques to analyse networks and understand how drones can be used most effectively.

“This research aims to understand the communication links between the drones and the users on the ground in order to find out the number of drones needed to cover a specific area, how best to position them and whether it’s more effective for the drones to cooperate to provide maximum coverage, or for each drone to service a particular group of users.”

Dr Mohammed El-Hajjar Associate Professor in Next Generation Wireless

Smart drones can also provide additional communication resource by caching soughtafter information to avoid overwhelming base station resources. This could prove useful in scenarios where there are large crowds and the network isn’t able to cope with demand, or for getting information to people in disaster situations where the communications infrastructure has been damaged.

The drones would identify the most searchedfor topics and cache relevant information, sending it to users on request and therefore reducing the number of contacts with the base station. Our researchers have explored ways to identify and cache the information and how best to optimise the deployment of the drones in different scenarios. This included looking at drone numbers and location, and whether it was more effective for each drone to cache one topic or for every drone to cache several topics.

Communication within intermittently powered and batterypowered networks

An IoT device that is plugged into a power source can communicate through a wifi network, but battery or self-powered devices, which make up a large proportion of IoT devices, don’t have the necessary ‘energy budget’ to use wifi. That’s where the use of low-power networking technologies comes in. Typically in these networks, data travels from the devices to a central hub, where it is collected and sent on to its final destination via the internet.

A branch of the Centre’s work is investigating how the individual devices can best transmit or ‘hop’ data across the network to achieve this end goal. In particular, our researchers are looking at how to select the most efficient route for data in networks where the devices are

powered by energy harvesting. This is a challenge because, in the interests of energy efficiency, the devices are only switched on when they need to perform a particular task.

Where devices are battery powered, the challenge lies in optimising the battery life, balancing the energy needed for communication against the requirements of other essential functions. Our researchers have looked at how to route data in different ways at different times to maximise the battery life of all the devices in a network. At central hub or base station level, they have also used techniques such as genetic algorithms and machine learning to optimise battery life across a network.

Testing compatibility and standardisation for environmental monitoring

Deploying IoT capability in remote locations and harsh conditions creates a specific set of communications challenges, and our environmental monitoring specialists have tested different types of networks and channels, as well as looking at the standardisation that is needed for the IoT to realise its full potential. Their work on glacier monitoring has taken advantage of long-range radio and the low-power internet (the same technology used to transmit data from domestic smart meters, for example) and direct long-distance communication between devices across the web and low-power web.

Communications for the molecular IoT

Looking to the future, the molecular IoT is an area full of exciting possibilities – for example, it could see the deployment of self-organised networks of nano-scale devices inside the human body to diagnose health conditions, or in the environment to monitor soil or water

“My interest is in communication across intermittently powered networks – how you pick the route that’s most likely to get your data from the source to its destination when neither the sender or the receiver are continually switched on.”

Professor Geoff Merrett Head of Centre for IoT and Pervasive Systems

quality. Our researchers are investigating ways to support molecular communication in nano-networks, and how to overcome issues relating to the efficient propagation and detection of molecular signals in different physical environments.

“For environmental work it’s all about lower-frequency, longer-range communications, and transmitting tiny amounts of data rather than downloading web pages or sending big files. The communications are slower over a longer range and must be robust against the elements. Ideally, they should also use web protocols that offer a standardised way of setting up and configuring communications networks, rather than requiring bespoke programmes.”

Professor Kirk Martinez Professor in Web and Internet Science

NANOTECHNOLOGY AND SYSTEM-ON-CHIP

IoT devices rely on a combination of components that enable them to sense their surroundings and to store, process and transmit data. The design and configuration of these components involves numerous challenges – they must be very small, but also have the processing capacity needed for their task, and the ability to generate and use power efficiently. To be viable for mass production, they also need to be fabricated using low-cost materials that have minimal environmental impact.

Our researchers are at the forefront of fundamental research that is advancing these underpinning technologies at the micro and nanoscale. Using thin film deposition, nanowire electronics and other nanofabrication techniques, they are working on new materials and designs for sensing, processing, memory and power generation components, and optimising the integration of these components into multi-layered on-chip systems. Their work takes advantage of Southampton’s exceptional cleanroom complex, which includes state-of-the-art microfabrication and nanofabrication facilities.

Nanoscale sensing technology

One element of this research focuses on the development of sensor technology using zinc oxide nanowires. A class of metal oxide semiconductor, zinc oxide is a cheap, plentiful material with low toxicity and low environmental impact, making it an ideal material for IoT components. Ionic in nature, it reacts readily to positive or negative charges; by analysing these reactions it’s possible to identify

particular micro or nanoscale targets – for example the presence of a particular type of pollution particle, or abnormalities in DNA that could indicate potential disease.

Our researchers are continuing to scale down the technology further, to detect micro ribonucleic acids (miRNAs), the building blocks of DNA. The initial aim is to develop this technique for use in disease diagnosis, drug trials and the design of tailored drug treatments. However, it’s a technology with many other IoT possibilities, such as distributed sensor networks for air, soil or water pollution, as well as smart biomedical applications.

On-Chip Power Management

Another stream of this research involves the design and integration of power management circuits into chip systems. Sensor technologies are power hungry, and the amount of power available to a sensor system limits how much data it can collect, process and transmit. Typically, current sensors can only work intermittently to sense, process and transmit

their data, after which their power is depleted and they need time to recharge. This poses a problem if more frequent monitoring is needed. Our researchers are developing systems to maximise the energy efficiency of IoT devices using zinc oxide nanowire technology.

Powering on-chip systems

When it comes to powering on-chip IoT systems, nanoscale thermoelectric generation is a good fit as it produces energy in the microwatt to milliwatt range that these devices require. It can also be a useful solution in scenarios where solar or vibration energy harvesting isn’t possible.

It uses semiconductor materials that react to temperature differentials – when they get hotter on one side, carriers diffuse from the hotter to the colder side of the material, creating a current. There are two types of thermoelectric material, n-type and p-type, which create negative and positive charges respectively. When an n-type doped material is connected electrically in series and thermally in parallel across a temperature

differential to a p-type doped material, current flows between the two. Across multiple pairings, this process generates the milliwatts needed to power IoT on-chip systems.

Our researchers are working on the development of thermoelectric materials that are abundant and cheap to manufacture, and looking at how to optimise these materials for maximum energy generation by varying doping concentrations and nanostructures. They are also designing, modelling and fabricating thin-film micro-generators to harvest the thermal energy, and looking at how best to integrate these components – for example, to incorporate many n-type and p-type pairs into a chip system for high-power performance.

Different environments have different thermal conditions, so another aspect of this research is device design optimisation, using AI technologies to accurately predict the performance of on-chip systems under different conditions. This will enable designs to be tailored to maximise device efficiency and performance in specific settings.

Memory optimisation for edge computing

One function of an IoT device is the transmission of data – traditionally to a central point – for processing. This takes a lot of power, so there has been a move towards edge computing – where some of the analysis and decision making happens at or near the device level – to minimise the amount of data that needs to be communicated. To do this, it’s essential to integrate memory into the system, so another area of research is the development of novel nanoscale memory devices that can be integrated into embedded systems.

Integrating components into on-chip systems

The development of novel components leads to another challenge – how best to integrate them into a single on-chip system. Research is underway to identify the best materials and the most effective three-dimensional configuration of sensors, microprocessors and transmitters in multilayered devices.

“The aim is to design an integrated micro power grid, using zinc oxide transistor technology, that regulates the energy needed for the different chip functions and shuts off power in parts of the system that are not in use.”

Professor Harold Chong Professor in Sustainable Electronic Technologies

“Thermoelectric energy methods have been used for many years in niche applications such as car waste heat recovery. What’s new is doing it at the nanoscale, which can significantly improve the performance of thermoelectric materials.”

Dr Ruomeng

Huang Lecturer in Sustainable Electronic Technologies

Nanotechnology and System-on-Chip

C-IoT Research Areas

INFORMATION PROCESSING AND MACHINE LEARNING

Information processing and machine intelligence techniques have an essential role to play in making sense of the enormous quantities of data generated by the IoT. Our researchers are finding new ways to extract useful information from this data pool and developing intelligent software to enable IoT systems to learn, identify patterns and make smarter decisions for a range of applications.

Information Processing and Machine Learning

Encompassing both IoT software and hardware, their work is considering a range of challenges and trade-offs. For example, how can resource-hungry information processing and artificial intelligence (AI) techniques be optimised for low-power devices? At device level, what’s the optimal trade-off between the speed and efficiency of intelligent processes and their accuracy, and how can hardware design contribute to better results? And how can the privacy and security benefits of distributed systems be realised while enabling the holistic view of data that machine learning and AI require?

A long-established discipline at Southampton, machine intelligence has been a focus of research activity here for more than 20 years. Our work in this area crosses the spectrum of computer science and electronics and our researchers collaborate with industry and academic partners around the world, and with Southampton’s Centre for AI and Machine Intelligence8

Putting more intelligence at the edge

Currently almost all machine learning and AI happens on cloud-based servers. For example if you ask your virtual assistant a question, the device itself will detect your voice and wake up, but after that the audio is recorded and sent to the cloud for interpretation and to generate a response.

This approach provides access to plenty of computational resource but raises issues about privacy – data might be intercepted during communication or the central store of data compromised. There are also issues around the power cost of communicating data and latency of the response. For these reasons, it would be useful to do some AI at the edge – that is, on or close to the device level rather than centrally. However, most IoT devices don’t have the processing power to run complex AI algorithms.

Our researchers are therefore investigating ways to bring intelligence to the edge in a way that is both quick and energy efficient. Southampton is a partner in the International Centre for Spatial Computational Learning9 a programme funded by the Engineering and Physical Sciences Research Council (EPSRC). Collaborating with colleagues at Imperial College London and the universities of Toronto, California Los Angeles and Sydney, our researchers are exploring ways to adapt and scale machine learning algorithms so that they either use less energy or return a response more quickly, while making more efficient use of the power resource of existing hardware.

For applications where less precision can be tolerated, this might mean a trade-off between accuracy and speed or power use. For example, it’s essential for the AI that runs a smart vehicle’s safety-critical systems to be both fast and accurate, and these systems may need additional power at certain times – for example, for processing video when travelling at higher speeds. However, it’s possible for the vehicle’s less critical AI elements, such as the infotainment system, to work at a lower power and output without any adverse effects.

8 https://www.cmi.ecs.soton.ac.uk

9 https://spatialml.net/

“At the moment everyone’s trying to make AI as clever as possible, designing algorithms with the highest degree of accuracy they can achieve. But as things move to the edge I think we’ll see a trend towards trading off performance and power use.”

Professor Geoff Merrett

Head of Centre for IoT and Pervasive Systems

“Modern AI techniques are very power hungry, using massive server farms to churn through vast amounts of data. Our aim is to develop learning models using purpose-built hardware that can be embedded into smallfootprint, low-power devices.”

Researchers within Southampton’s Centre for Doctoral Training for Machine Intelligence for Nano-electronic Devices and Systems10 (MINDS CDT) are also looking at how to run machine learning on very low-cost, low-power devices. Cutting across both computer science and electronics, their work includes the design of novel hardware that is specifically designed for certain algorithms. They are also exploring how to enable machine learning across distributed networks, whether through a ‘super node’ where all the network’s computation happens, or peer-to-peer communication between the nodes in a network.

Optimising machine learning for health and care

One area that will benefit from more efficient machine learning methods is human activity recognition (HAR) based on sensor data, where the optimisation of HAR algorithms will help to realise the potential of innovations in personalised health and continuous health monitoring.

HAR data is expensive to collect and tends to be available only in small datasets. Our researchers have tested new ways to interpret it without using recurrent neural networks, which are the current state of the art but which require lots of data and are difficult to train. The have also found that transfer learning (where software learns a model using one dataset and is then able to apply it to different datasets) using a technique called cascade learning outperforms

“We’ve come up with an interesting model that uses Bluetooth as a proxy of the networks of human interaction over which infections spread. This novel modelling approach incorporates both digital and manual contact tracing and looks at how this might affect the R number.”

Dr Kate Farrahi Lecturer in Vision,

the commonly used end-to-end deep learning approach in terms of computational and memory resource.

This stream of work aims to contribute to the development of smaller, embedded deep learning models that can work quickly using less data than current methods, with multiple potential applications in the health and social care field.

Also in the sphere of healthcare, our researchers are using information processing and machine learning techniques to predict and prevent potential health problems. By analysing existing health datasets for trends and patterns, they have designed algorithms that can be applied to real-time data streams from remote patient monitoring systems, giving doctors additional information with which to make treatment decisions. Working with clinicians, they have designed systems with the potential to predict and prevent heart attacks and epileptic seizures, and they are looking to apply the same principles to a system that will predict the likelihood of neurological disorders in babies and young children in high-risk groups.

Modelling COVID-19 contact tracing

One aspect of our researchers’ work on machine learning came to the fore at the beginning of the COVID-19 pandemic, when a 2014 study that modelled a Bluetooth contact tracing system using mobile phone data suddenly took on new relevance.

The Singapore government got in touch to find out more about the study and subsequently launched a Bluetooth-based system, and the paper attracted significant attention from other government agencies and the media.

Since then, our researchers have updated state-of-the-art COVID-19 models to incorporate contact tracing in order to gain a realistic understanding of how factors such as user uptake can affect infection numbers.

Using AI in dynamic transport scenarios

Connected and autonomous vehicles are set to revolutionise travel and transport in the future, making journeys more convenient and efficient through platooning (vehicles travelling as a group) and smart ride-sharing schemes, for example. The concept of an internet of connected vehicles is underpinned by AI and our researchers are tackling some of the inherent challenges. For example, automated car users will be required to share data from their vehicle with others in order to join a platoon, so our researchers are designing intelligent agents to help people negotiate data use agreements to preserve their privacy. They are also modelling and developing AI algorithms for complex and dynamic scenarios, such as platoon formation, routing decisions and intersection control.

Another stream of this research is investigating ways to incentivise people to use an automated transport system in a way that is compatible with its aims, rather than ‘gaming’ it for their own ends – for example by offering financial incentives to share a ride or take a longer route if this benefits the efficiency of the system as a whole.

This work is being undertaken as part of AutoTrust11, a programme funded by the EPSRC.

“Once you have autonomous, connected vehicles that can communicate with each other, you can make more efficient use of the road network. This involves lots of aspects of AI, such as using limited data to infer what other road users might do or where congestion might occur, or optimising communal ridesharing routes based on levels of traffic and user preferences.”

Professor

of

11 https://autotrust.org.uk/

Learning and Control

Professor Tim Norman Head of School of Electronics and Computer Sciences

Enrico Gerding Head

the Agents, Interaction and Complexity Research Group

HEALTH AND WELL-BEING

From personal activity trackers to remote monitoring of long-term health conditions, IoT devices have become ubiquitous in the health, fitness and wellbeing space. Our researchers are working across the IoT spectrum, from sensor design through to data processing and machine learning, to develop new ways to harness the power of smart, connected devices for good health and patient-centred care.

Professor Michael Boniface Director of the IT Innovation Centre

Health and Well-being

We work in collaboration with clinicians from University Hospital Southampton NHS Foundation Trust and numerous other partners in the health sector, and with researchers from the University’s Centre for Health Technologies12

Shaping the future of healthcare

The use of IoT technology is blurring healthcare boundaries and enabling innovations such as virtual wards. Our researchers were instrumental in establishing COVID-19 virtual wards; their work on the home monitoring of patients’ oxygen levels and other vital signs fed into national policy during 2020.

Our researchers have also shared their expertise to inform the design of hospital infrastructure to take into account a combination of digital, virtual and physical environments, so that the hospitals of the future are equipped to take advantage of new digital approaches.

There is huge potential for data gathered using IoT monitoring to enhance public health datasets and therefore inform health research, funding and policy. In current datasets the links between medical data and the social determinants of health, such as lifestyle factors and activity levels, are minimal. A more complete understanding of public health requires the routine capture of these social determinants, and digital health and IoT systems will have a fundamental role to play in this. At Southampton researchers are looking at datasets on multimorbidities – where people have several health conditions at once – and how these datasets can be augmented from other sources to understand the contributory factors to ill-health. 12 https://www.cht.ecs.soton.ac.uk

Remote health monitoring systems

Being able to gather data about patients’ health as they go about their daily lives can be enormously useful for clinicians. As well as providing more robust data to inform clinical decisions, it can facilitate a more personalised and efficient approach to care, enabling doctors to call people in for appointments only when their health data flags a need.

Our researchers are working with health professionals across numerous specialties to design such systems. For example, they are liaising with surgeons who carry out knee, ankle and shoulder operations to explore remote monitoring that will give them a full picture of their patients’ condition before and after operations, rather than relying on physical checks and self-reporting by patients at appointments. Similar work is being carried out with diabetes doctors on remote blood glucose monitoring that provides data about their patients’ day-to-day health.

Stroke rehabilitation is another area where remote monitoring can be useful. One common result of a stroke is loss of limb strength or function, for which a physiotherapist may prescribe remedial exercises for the patient to do at home. However, often patients don’t do the exercises, or don’t do them for the recommended length of time or in a way that is most effective.

“Devices such as oximeters are readily available; the challenge is integrating them into a reliable remote system. This involves overcoming interoperability issues, looking at how devices are recognised and managed within a system, and how to evaluate the quality and dependability of the data they provide.”

C-IoT Application Domains

To give physiotherapists a fuller picture, our researchers have developed a home-based monitoring system, using motion sensors rather than cameras to preserve patients’ privacy, to generate data about how often patients are doing exercises and the quality of the movements. This can be fed back to the therapist in real time so they can understand whether adherence to an exercise regime is a factor affecting patients’ recovery.

Using data analysis and machine learning to predict health issues

As well as designing remote monitoring systems, our researchers are developing new techniques to analyse and apply machine learning to the data these systems create, with the aim of predicting and preventing potential health issues. This strand of research uses existing health datasets to identify patterns against which real-time data can be analysed.

For example, in collaboration with cardiology consultants at Southampton General Hospital, researchers have developed a remote heart monitoring system which analyses electrocardiogram (ECG) data to help predict and prevent heart attacks. ECG data is gathered and transmitted via sensors worn on the patient’s body, and automated software analyses the data in real time using machine learning. This gives the doctor greater insights into the patient’s condition and enables them to make informed treatment decisions. Previously, the doctor might only get a snapshot of the patient’s heart health each time they visited the hospital.

Similarly, research has been undertaken in partnership with neurologists looking at the remote monitoring and processing of brain signal data. This work has been applied to the prediction of epileptic seizures, which in future could enable patients to take preventative action to avoid a seizure occurring, and to predict long-term neurodevelopmental conditions in young children.

To continue this work, a further study is planned which will involve recruiting children who may be at higher risk of developing a neurological disorder, for example due to complications at birth. The plan is to monitor their brain activity during early infancy until school age, during which time any disorders may be detected and diagnosed. The data can then be analysed

“From a medical consultant’s point of view, they don’t see a patient again until four or six week after an operation. They ask how it’s going, and the patient says ‘fine’ – but what does that mean? Remote monitoring would provide the data to tell clinicians them how far a patient is walking each day, or whether they have been following instructions to rest or do rehab exercises, to help inform their treatment.”

Professor Neil White Professor in Smart Electronic Materials and Systems

retrospectively to identify factors and trends, to underpin models for future predictive remote monitoring of children with similar vulnerabilities.

New sensor technologies for healthcare and fitness

Our researchers are working on the development of novel sensors to detect vital signs and movement. One example is a sensor to measure respiratory rate. In a hospital ward, typically a patient’s breathing rate is monitored every few hours by a nurse counting how many times they breathe in and out in a minute. While these spot checks are useful, they don’t provide a picture of trends in real time. This can lead to the early signs of rapid-onset conditions being missed – one example is sepsis, where an increase in respiratory rate is an early indicator. Continuous monitoring, whether at home or in hospital, enables earlier, potentially lifesaving intervention.

Our researchers have developed a screen-printed motion sensor that can be attached to a garment to continually monitor a person’s respiratory rate – either in hospital or at home – transmitting data to clinicians and enabling them to respond to any significant changes. The sensor has been successfully tested on patients at the hospital.

In the field of physical fitness and performance, researchers have developed an adhering smart fabric that can be attached to the skin that monitors the body’s movement. The idea is to help athletes and their coaches to better understand and evaluate the execution of

sports moves to improve technique and avoid injury. The sensor integrates piezoresistive graphene nanosheet thin films with kinesiology tape, which mimics the elasticity of skin, to capture detailed information about muscle contraction and joint angles in rapid motion. The fabric is extremely thin, cheap to produce, and has the potential for use in remote monitoring for sports training and other fields, such as rehabilitation after surgery or stroke.

Exploring new design approaches

While IoT-enabled systems have obvious benefits in the field of health and wellbeing, the ubiquity of personal fitness, sleep, diet and other wellbeing apps is raising new questions among researchers in the field of human and systems interaction. They are investigating how apps and smart devices can help people build the knowledge, skills and practice they need to take control of their own health, rather than encouraging them to outsource their wellbeing to external services on an ongoing basis.

“In terms of IoT devices, there has been great work so far on the ‘we’ll take care of you’ side of things, but there is lots more to be done to help people take care of themselves and build up their resilience. We are exploring design principles and testing technologies that move from helping people to build habits, which can be useful but are inherently brittle, to heuristics, which offer a template for making choices that can be adapted to any context.”

Professor m.c. schraefel Professor of Computer Science and Human Performance

“A patient who is worried about their symptoms can call the doctor, and the doctor might ask them to walk up and down the stairs – the doctor can then see the ECG data in real time and use it to decide on the best course of action for the patient.”

Professor Koushik Maharatna Professor in Biomedical Electronics

ENVIRONMENTAL MONITORING

The environmental challenges facing our planet have never been greater, with the impact of climate change, pollution and habitat loss becoming increasingly apparent around the world. By enabling us to gather more environmental data, including data from previously inaccessible locations, IoT technology can help us to better understand the world around us and inform effective action.

Every year billions of data points are taken around the planet, whether by small sensor devices, meteorological stations or satellite systems. If you can make any part of it cheaper, more connected, more autonomous or easier to configure and maintain, you save thousands of hours of researcher time.

Often used in harsh or remote locations, environmental IoT systems need to be robust enough to withstand extreme conditions and easy to deploy and maintain – ideally removing the need to wade through a river or climb a mountain to change batteries or reset devices. Our researchers are developing and deploying IoT systems tailored to these specific demands.

Understanding glacier dynamics

To anticipate and manage the effects of climate change, it’s crucial to understand the effect of global warming on glaciers and the resulting sea level rise. However, the behaviour of glaciers is not well understood because it’s so difficult to model what’s happening beneath hundreds of metres of ice.

13 https://glacsweb.org/

For almost two decades our researchers have been generating new knowledge about glacier dynamics13, most recently at project sites in Iceland where they were the first to put custombuilt, internet-connected sensors underneath glaciers to measure temperature, stress, weather and subglacial movement in real time.

This involved designing a whole system tailored to glacier conditions, from new sensing technologies and communications protocols through to fabricating unique, super-robust sensor capsules. Developments included building in autonomy to enable the sensors to gauge the available power and communication channels and adapt their activity levels accordingly. A range of wireless communications technologies, including low-power radio links and mobile phone networks, were deployed to transmit data from the sensors to a base station on the ice, then to the internet via a nearby reference station.

Reflecting the multidisciplinary nature of IoT research, the team included expertise across computer science, electronics and glaciology.

Measuring ice flow

More recently the research team has been measuring glacial ice flow using the latest generation of low-cost real-time kinematic (RTK) dGPS units. Powered by solar energy, the system automatically provides location measurements from synchronised dGPS units, which wait for a static RTK fix and then sleep between sessions.

The readings are sent once a day to a web server via the Iridium satellite network, allowing the system to operate anywhere in the world. The fixes are accurate, quick to acquire and generate small amounts of data for transmission, reducing the power requirements by around a 50th compared to traditional systems and opening up the possibility of year-round live monitoring.

Testing IoT interoperability in the Scottish Highlands

Another project took researchers to the Cairngorms in Scotland to set up remote monitoring of hydrological, peatland and frozen ground processes14 The underlying aim was to test the standardisation and interoperability of the different elements of IoT environmental monitoring systems.

Typically, networked environmental sensing systems are difficult to set up because of incompatibility between the different components. The aim of this project was to see whether using standards and Internet Protocols would support an interoperable, internetconnected IoT system. The results showed the effectiveness of this approach, with benefits in terms of scalability, integration, management and robustness. The proof-of-concept project has laid the foundations for new IoT sensing research, as well as showing its potential for use in the Earth sciences.

Sensing salt levels in soil

Another area where IoT systems have huge potential is soil condition monitoring. Our researchers have developed sensors to detect the levels of chloride (salt) in soil, working in partnership with the University of Western Australia. In Australia, deforestation and increased demands on water supplies have altered the water table, allowing salt to rise upwards through the ground. In the long term, soil salination poses a threat to food security globally, as too much salt renders agricultural land unusable.

The sensors draw on a longstanding research interest at Southampton in the field of active inks that can detect or respond to things in their environment. Unlike traditional circuit board manufacturing, which uses a subtractive process that strips off copper to leave behind the desired circuitry, these inks can be screen printed directly onto a surface. It’s a flexible and relatively simple process; any material that is printable can be used, and it can be printed in any shape and layered with other materials to suit the required function.

14 https://mountainsensing.org/

“We’re looking for the user friendliness and autonomous reliability that you would expect if you were using an IoT system at home. Hardworking researchers out there in the rain, snow, or standing next to a volcano, want things that are easy to use and that just work.”

Professor Kirk Martinez Professor in Web and Internet Science

“Every year we’d add more smart technology to the systems, so they could self-manage and last as long as possible. We were able to run them continuously under the ice for two years, which no-one else has managed since.”

Professor Kirk Martinez Professor in Web and Internet Science

A substrate of ceramic is printed with silver as the conducting material, which is isolated with a layer of glass, followed by a printed layer of the active sensing material. In the case of chloride detection, the active material is silver chloride – which reacts with chloride in the environment – mixed with glass and liquid to form an ink.

Using this process the researchers fabricated sensing elements which can be inserted to the soil. The system uses two electrodes which effectively make half a battery; the environmental chloride provides the electrolyte, so if there is chloride present a voltage is created. Measuring the voltage between the two electrodes indicates how much chloride is in the soil.

The researchers are planning to extend this work to the sensing of phosphates used in fertiliser, which increase crop yields but harm the environment when they seep into the surrounding watercourses.

Powering environmental sensors

Another strand of research is looking at a new way of powering IoT devices for environmental monitoring. Environmental sensors are generally solar powered, but over time dust or plant growth can block out the light, reducing the amount of energy they can generate. Our researchers are working on an alternative that uses temperature changes at sunrise and sunset to generate energy.

This innovative technology turns the temperature change into a pressure change; as the air in a sealed cylinder warms up it expands, which increases the pressure. The pressure is used to move a piston and drive a generator. By changing the type of gas inside the cylinder the device can be tuned to work over a particular temperature range. Because the energy is generated only at dawn and dusk, it is harvested and stored for use when the sensor is active.

it will generate enough energy to run lowpower sensors and communication systems.”

SMARTER HOMES, CITIES AND INFRASTRUCTURE

At home, virtual assistants respond to our commands, smart meters automatically transmit readings to energy companies, and intelligent thermostats learn our preferred central heating settings. In the city, real-time transport information, cycle-hire schemes and checkoutfree shops are all enabled by the IoT, and sensor technologies are monitoring essential infrastructure to ensure it is safe and secure.

The possibilities for future uses of IoT in our homes and cities are limitless, and our researchers are investigating ways to make new applications a reality. As well as improving our everyday lives, IoT-based solutions have the potential to address major societal challenges, from caring for an ageing population to meeting climate change goals through greener transport and more efficient energy use.

“This sensor is well matched to agricultural needs, enabling farmers to detect whether salt levels are changing in real time. In the future it could also be applied to other environmental uses – for example monitoring the effects of salt being put back into the sea from the world’s many desalination plants.”

Professor Nick Harris

Head of Smart Electronic Materials and Systems

“This technique has exciting possibilities and hasn’t been exploited before. It’s useful for environmental monitoring because it can be used almost anywhere. As long as you’ve got a temperature change of a few degrees

Professor Nick Harris Head of Smart Electronic Materials and Systems

Our homes, communities and the essential infrastructure we all rely on are getting smarter and more connected, with the IoT underpinning a network of ‘things’ that can make our lives easier, greener and safer.

Self powered IoT environment monitoring node

Example of a typical salt environment. A freshwater river in Australia shows salt encrustment on the bank.

C-IoT Application Domains

Smarter Homes, Cities and Infrastructure

Developing underpinning technologies for smart cities

One project that has developed IoT systems for smart city applications is Wearable and Autonomous Computing for Future Smart Cities, a five-year project funded by the Engineering and Physical Sciences Research Council (EPSRC). It encompasses all the layers of the IoT stack, from developing sensor and energy harvesting devices through to building effective connected systems and designing intelligent software for data management and autonomous decision making. It is also looking at how humans and smart systems interact and how to advise or incentivise people to take actions that will benefit the system as a whole.

As part of the project the team designed and deployed bicycle-mounted pollution sensors to gather data about Southampton’s air quality. This type of system has benefits over fixed sensor systems, with the potential to deploy many more sensors and to incentivise people to take specific routes for greater geographical coverage.

The data could be used in real time to divert traffic to reduce pollution levels, or to produce routing recommendations for cyclists to help them avoid areas of high pollution. It could also empower citizens to lobby local decision makers to put plans in place to improve air quality. Another stream of this research saw the development of an intelligent waste bin that can sense the weight, temperature and gas emissions of its contents and identify what has been discarded. This technology could be used to gather data on what gets thrown away, encourage people to reduce food waste and prompt the sorting of recyclable materials.

Home-based activity monitoring

Another application of Southampton’s pioneering work on wearable, flexible IoT sensors is activity monitoring to ensure the wellbeing of older people or those with long-term health conditions, while enabling them to live as independently as possible. This involves interpreting data from sensors worn on the body to identify a person’s everyday activities, allowing carers or clinicians to check whether they are eating regularly, moving around in the usual way or taking medication, without the use of intrusive cameras.

Our researchers developed wearable sensors for activity monitoring as part of a large EPSRC-funded project called SPHERE (Sensor Platform for HEalthcare in a Residential Environment). Since then, they have continued to improve the integration of sensors so that they sit invisibly within clothing, achieving the same functionality as wristband sensors.

Smarter, greener transport

The IoT will enable completely new forms of transportation, including connected vehicles that work together for optimum efficiency, and electric autonomous vehicles (AVs) that will make public transport greener and more user friendly. The AV transport systems of the future could be much more flexible, with shuttle bus or ridesharing schemes that can adapt their routes and timetables dynamically according to demand.

Our researchers are tackling some of the inherent challenges associated with autonomous and connected vehicles. Projects include the design of intelligent control systems for teams of connected AVs to enable them to travel with minimum energy consumption while maintaining a safe distance, and designing intelligent agents to help smart car drivers negotiate data use agreements when information is shared between vehicles. Our researchers are also designing AI algorithms to make the best use of the road network, through approaches such as platooning (vehicles travelling as a group) and intersection control, efficient route planning to keep traffic flowing, and the optimisation of ride-sharing schemes.

Another aspect of AV research is the design of smart algorithms that enable automated ride-sharing or smart bus services to operate in the most efficient way. The algorithms take into account people’s individual needs – for example whether they need to arrive at their destination at a specific time or can be more flexible – and can

make decisions about which vehicles pick up which people and the routes they take. Work has also been undertaken on how best to incorporate the needs of disabled or older people into smart transport systems.

An important aspect of this research is exploring how people can be incentivised to report their transport needs truthfully in order to optimise the efficiency of the system overall. Southampton is leading the way in the use of game theory – a way of modelling the behaviour of self-interested users, or agents (which could be software or people) within a system – to design systems that incorporate these types of incentives. For example, in an automated taxi service, someone might accept the slight inconvenience of picking up another passenger who wants to go in the same direction in return for a financial discount.

Work is also in the pipeline to examine how to reduce congestion by incentivising people to take alternative routes by offering small financial rewards. The idea is to design a tool that would show different routes and calculate the incentives, enabling people to opt for a reward for taking a longer but less busy route.

Further investigation of incentive mechanisms to optimise smart transportation systems is being undertaken as part of Citizen-Centric AI

“There are lots of smart home-based monitoring systems around, but they tend to stop at the person’s front door. In future they could be extended beyond the home to the smart city environment, using sensors embedded into fabrics so all the person has to do is get dressed.”

Professor Steve Beeby Professor in Smart Electronic Materials and Systems

“Once

system and reduce congestion. But it’s a very dynamic IoT network and involves a lot of communications between vehicles. My research includes looking at the formation of platoons at intersections and whether algorithms that work across multiple intersections could remove the need for traffic lights.”

Professor Enrico Gerding Head of Agents, Interaction and Complexity

Systems15 This is a five-year project funded by UK Research and Innovation through a Turing AI Acceleration Fellowship – a prestigious award made as part of the UK government’s AI strategy.

Monitoring the health of critical infrastructure

Southampton has a long history of designing IoT systems to monitor the health of the transport infrastructure. Through the successful spin-out company Perpetuum, for example, sensor and energy harvesting technology designed by our researchers is now deployed in the rail industry for real-time monitoring of train axle bearings, wheels and track. This enables rail companies to plan maintenance according to need, ensuring safety and reliability across the network and reducing costs.

Another focus for infrastructure monitoring research is the electricity grid. With the increasing electrification of transport and the integration of more renewable energies into the grid, getting system-wide data about the condition and capacity of the electricity infrastructure is becoming even more important. However, the grid relies on high-voltage underground cables, and typically once they are laid it’s difficult to find out whether they are working efficiently or at risk of failing. Our researchers are developing smart materials sensors for condition monitoring which could make this possible and help to ensure a more stable and efficient power network.

The deployment of smart sensors along high-voltage cables would enable data to be gathered on their temperature and environmental conditions, which would in turn indicate whether the cable was being underused or overloaded, and whether there were any faults or problems. The sensors themselves are self-powered, generating energy from the temperature gradient created by the heat of the cable. The ultimate aim is to deploy a network of sensors that can share data between themselves, make decisions and communicate to a centralised control system to help the network run more efficiently and reliably.

“The IoT is exciting because if we can put inexpensive sensors that can talk to each other across the power network, it will give us new insights where previously we have only been able to make assumptions – for example whether too much energy is being pushed through highvoltage cables creating a hotspot that could lead to failure, or whether a cable has capacity to carry more power.”

Dr Thomas Andritsch Associate Professor in Electrical Power Engineering

Managing the demand for power

As EVs become the norm, the need to charge batteries will put unprecedented pressure on the power network. Our researchers have designed algorithms that take users’ needs into account to schedule charging in a way that avoids grid overload.

However, as with smart transport, this type of system relies on people being truthful about their needs – for example whether they have to charge their car in time for a daily commute, or whether their schedule is more flexible. Once again our researchers are using game theory to look at how incentives can be used to encourage people to be honest about their needs, by offering financial incentives if they can be more flexible about when they charge their car.

This work is progressing further as part of the Citizen-Centric AI Systems project, which includes work with industry partners, including EA Technology, Siemens Mobility and Jaguar Land Rover, to test new mechanisms in real EV charging systems.

“The aim is that our research will feed into the systems of the future as EVs become more widely used. Working with industry partners means we can make sure it’s possible to deploy it in real systems.”

Professor Sebastian Stein Professor in Agents, Interaction and Complexity

15 https://ccais.soton.ac.uk

“By learning about the application requirements, we can better focus the development of the underlying technologies. In the Wearable and Autonomous Computing for Future Smart Cities project these included intermittent computing, wireless power transfer and data processing.”

Dr Alex Weddell

Lecturer in Smart Electronic Materials and Systems

you have autonomous, connected vehicles that can communicate with each other, you can make more efficient use of the road

C-IoT Application Domains

Smarter Homes, Cities and Infrastructure

Another stream of research is looking at ways to use EV batteries to smooth out fluctuations in electricity supply and demand. When EVs are parked and plugged in they can be used as a huge storage device that can be discharged as well as charged. Drawing on this power could help resolve one of the challenges of using renewable energies – the unpredictable nature of wind and solar supply. Our researchers have examined the possibilities of vehicle-to-grid integration using IoT technology and the design of automated algorithms that can trade with the grid on behalf of consumers.

Making smart energy systems more secure

An emerging trend enabled by smarter energy grids is the evolution of transactive energy, where ‘prosumers’ – consumers who also produce energy, perhaps through solar panels on their roof – sell energy directly to other consumers. The IoT provides the technology to gather information and automate trading decisions, but can also open up new security issues, such as the possibility of forged transactions.

Using blockchain technology our researchers have designed and prototyped an infrastructure for secure and reliable peer-to-peer energy trading. The research was part of BlockIT (Blockchain-empowered Infrastructure for IoT), a project funded by the EPSRC.

Supporting smart city systems

As uptake of smart city systems grows, more and more IoT devices will be deployed, creating huge amounts of data. However, typically the devices themselves won’t have the capacity to process and analyse the data they produce. Issues such as latency and the limits of wireless bandwidth capacity means sending data to the cloud for processing may not always be the ideal solution.

Instead, our researchers are looking into the use of ‘fog’ computing – a layer of infrastructure closer to the ‘edge’ (where the IoT devices are). The fog layer, while not as powerful as the cloud, could take on some of the processing to improve speed and free up bandwidth.

As there is currently no pervasive infrastructure of fog devices, the research is focusing on the use of blockchain to establish an independent and transparent entity to support a federated system through which fog service providers can operate.

As well as being secure and independent, blockchain offers some built-in intelligence, enabling it to allocate fog resources in the most efficient way to the applications that require them.

In addition, algorithms designed by our researchers to manage and process data in dynamic scenarios could contribute to the smooth running of smart city IoT systems. The AI and machine learning techniques were developed as part of Distributed Analytics and Information Science International Technology Alliance (DAIS ITA)16, a joint UK/US governmentfunded project focusing on the use of IoT in fast-changing and resource-constrained

environments. The algorithms enable systems to autonomously distribute computational tasks according to system priorities and available resource – for example, diverting capacity to the traffic management system in the event of an incident on the road, and reducing it for less critical applications.

Addressing privacy and security issues

Whether collecting information about people’s daily lives or controlling essential infrastructure, the increasing use of IoT devices creates new privacy and security issues.

Our researchers are developing new ways to put users in control of personal data generated by IoT devices so they can specify which aspects of their data other entities can access and how it can be processed.

In the realm of IoT security, we are working to outsmart hackers by defining, identifying and defending against spatial-temporal propagation attacks – where an adversary accesses one IoT device in order to initiate a cascade of failure across a network over time. This work encompasses a range of applications including smart energy meters, vehicle-to-vehicle communications and sensors in offshore oil systems.

Other developments in IoT security include new techniques to remotely detect whether a device has been tampered with, and ways to identify and patch potential vulnerabilities in industry control systems.

INDUSTRY 4.0

Industry 4.0 offers the potential for smarter, more efficient processes, not only in single factory sites but across supply chains. It also encompasses innovations such as the ‘digital twin’ – a digital replica of an industry process, informed by real-time data, that allows virtual fine-tuning and the diagnosis of faults before they become critical.

In collaboration with industry partners, our researchers are working across the IoT stack – from the hardware within industrial IoT devices through to automated decision making and user acceptance – to realise the full potential of the IoT to improve efficiency, safety and reliability across a range of industry settings.

Improving safety and reliability in the aerospace industry

Advances in energy harvesting and sensor system technologies at Southampton are offering new solutions for aerospace, automotive and other vehicle engineering sectors, where monitoring the condition of engines and other components is crucial.

For example, Southampton is a partner in the EU-funded I2BS project17, working with Safran Aircraft engines and Schaeffler Aerospace to develop a new way to monitor the health of jet engine bearings. As part of this programme, our researchers are examining ways to harvest energy from within the jet

17 https://cordis.europa.eu/project/id/717174

“The data in blockchain is practically tamper-proof, so provides a strong guarantee of the integrity of the transaction data that is stored. In addition, blockchain is highly available; with thousands of nodes all storing the same data, an adversary is unlikely to be able to crash the whole system.”

Dr Leonardo Aniello

Lecturer in Cyber Security

IoT technologies are at the heart of Industry 4.0 – the fourth industrial revolution – opening up new possibilities by bringing embedded sensing, connectivity and automation into manufacturing and other industrial processes.

engine, using temperature differentials or the rotation of the shaft itself, to generate small amounts of power for a condition monitoring sensor. The sensors themselves gather data about vibration, temperature and the amount of stress on the bearing, enabling faults to be detected at an early stage to avoid failure.

Our researchers are also developing vibrationpowered energy harvesting systems to monitor other machines and components, including helicopter blades and marine engines.

While the automation and intelligence enabled by IoT devices brings huge benefits for industry processes, it also opens up the risk of cyber and physical attacks, and control systems are one potential point of vulnerability.

Control systems involve communication between computers and machines via a network. It’s possible for an adversary to introduce false inputs to this network, ‘tricking’ the computer into giving a command to the machinery. For example, a false input might cause the computer to open and close a valve more often than is needed, leading to the failure of machinery. In safety-critical industries and infrastructure, such an attack could prove catastrophic.

Building trust within collaborative industry networks

Smart industry IoT systems can enable businesses to work collaboratively, providing greater insight and efficiency across production and supply chains. However, this requires data to be shared between different stakeholders, making data security a concern. Each time information passes a boundary from one data owner to another, the risk of a data breach increases, and this may be an operational risk that businesses are not willing to take.

As well as developing the technical model, the researchers have been investigating the associated operational issues. For example, businesses may be reluctant to share sensitive information about their own systems, so one challenge is to find the right balance – that is, the level of information that is enough to engender trust but that won’t deter companies from participating because of confidentiality concerns.

This work has been developed across several projects in collaboration with industry partners, including the European Connected Factory Platform for Agile Manufacturing18 and the ZDMP: Zero Defect Manufacturing Platform.19

Supporting efficiency and safety in the construction industry

Developing smarter, more secure control systems

Automated industry processes rely on control systems – the computerised systems that control the physical tasks carried out on a production line, for example. The control system uses incoming real-time data gathered by IoT sensors across different parts of the line to monitor and manage activity. One strand of work at Southampton involves the development of mathematical models to optimise the way control systems make decisions based on this data. The algorithms enable the control system to operate more dynamically in response to changing conditions and adjust the production process accordingly, to use resources in the most efficient way and improve productivity.

Southampton researchers have developed a technique called symbolic execution to automatically examine the code underpinning the control system. This method enables the researchers to identify vulnerabilities that could enable this type of attack and suggest additional controls to patch these vulnerabilities.

Improving security across the supply chain

Another aspect of research into IoT system security focuses on the hardware supply chain. For IoT and other devices, most electronics manufacturers procure integrated circuits (ICs) from a network of suppliers, which means they don’t have full oversight of the IC production history. This opens up the risk of manufacturers inadvertently using counterfeit components that don’t meet the design specification, or that contain malware that enables a device to be hacked or attacked.

Our researchers are looking at ways to mitigate this risk, using blockchain and physically unclonable functions to authenticate ICs and track their movement through each stage of manufacture and assembly, so that any tampering can be detected.

To address this, our researchers have developed a risk analysis tool, the System Security Modeller, to analyse the security risks at each transition point and suggest control strategies to mitigate them. Sharing this information among stakeholders builds confidence that the flow of data will not expose any of the collaborative network to cybersecurity risks.

IoT systems are not only being used on production lines – innovations in wearable IoT mean companies can collect data via their workforce to inform business operations and ensure health and safety compliance. Our researchers are helping to improve this type of technology. One example is a project undertaken with Mafic, an SME that produces a system called Safeguard. Safeguard uses intelligent, connected technologies to give construction companies real-time insight into efficiency and safety on their sites. Sensor devices are built into construction workers’ hard hats; these collect and transmit data about workers’ movements, location and environment, which is processed using machine learning techniques to provide information on a range of factors, from levels of noise and dust to the tasks they are undertaking.

Our researchers have helped Mafic to optimise the device, delivering significant improvement in Safeguard’s capabilities. They initially worked on the development of the machine learning algorithms that underpin the technology. More recently, they have been working on two powerrelated challenges to improve the product’s usability – reducing the device’s power consumption and addressing issues around recharging. They applied state-of-the-art low-power techniques to reduce the sensor devices’ power consumption by an order of magnitude, thus extending the battery life.

18 https://www.efpf.org/

19 https://www.zdmp.eu/

They also developed new wireless power transfer techniques for more convenient recharging. Requiring workers to plug their hats in to recharge at the end of their shift was not desirable, but traditional wireless transfer techniques, such as those for mobile phones, require the device to be within 10mm of the power source, which was also not practical for this application. The researchers developed an alternative technique to extend the charging range.

Promoting IoT acceptance on the shop floor

While IoT technologies can bring huge benefits to industry, increased automation inevitably brings about changes for the workforce. One example is a use case where AI-based visual analytics automatically detect whether workers are wearing face masks in spray paint booths, only switching on the necessary equipment when a mask is detected. While this may be attractive to a company in terms of greater safety and efficiency, it requires staff to accept decisions that are not made or mediated by humans – something they may be reluctant to do.

Our researchers are seeking to determine the optimum level of IoT system information that workers need to help them understand the system and build trust and reassurance around its reliability. By providing companies with resources to help them bring their staff on board, the aim is to encourage the uptake of new technologies.

“With the jet engine bearings monitoring research we’re coming up with innovations in energy harvesting and system integration, and pushing the limits on commercially available high-temperature sensor devices.”

Dr Alex Weddell

Lecturer in Smart Electronic Materials and Systems

“We have also applied mathematical control system methods to the reduction of energy consumption in industry processes without sacrificing performance.”

Dr Bing Chu

Associate Professor in Vision, Learning and Control

“Supply chain security is important because mass-produced IoT devices are designed to be low cost and are therefore relatively unsophisticated, so they are they are particularly vulnerable to counterfeiting or malicious attacks via the IC supply chain.”

Dr Basel Halak

Associate Professor in Cyber Security

“Without trust there is no data sharing, and without data sharing there is no IoT stack. To optimise the impact of multi-stakeholder IoT systems, you need all the stakeholders to share a degree of trust, and we propose risk analysis as a way of increasing that trust.”

Dr Stefano Modafferi

Principal Research/Enterprise Fellow

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