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.

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

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.

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 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