Location Awareness Accuracy Improvement for Full-Mesh IoT Networks for CAV and 5G applications

Location-aware devices are devices capable of determining their physical location, actively or passively, in relation to a known reference or references. They play a vital role within the areas of connected and autonomous vehicles (CAV) and 5G communication networks, as next-generation Internet of Things (IoT) devices are expected to have reliable, high-accuracy location detection capabilities. For example, the transition to autonomous vehicles will be underpinned in part by so-called "Vehicle-to-Vehicle" (V2C) communication, in which vehicles are able to wirelessly exchange information about their surroundings - their location, speed, etc. - with each other. The importance of location information in handheld and wearable IoT devices is well established and was recently reinforced by the importance of contact tracing apps as part of the national response to the COVID-19 pandemic.

While recent years have made strong advancements, there are still considerable challenges in the reliable performance of location-aware technology that must be overcome to enable successful deployment in complex and safety-critical real-world environments, particularly when devices are travelling at high speeds. This research project aims to perform research and development regarding agile artificial intelligence (AI) and machine learning (ML) algorithms for addressing issues such as mutual sensor interference and location awareness accuracy, particularly as it pertains to the reliability of self-driving and remotely monitored vehicles.

We can summarise the project in terms of three main objectives. Firstly, we will develop AI/ML algorithms for a full-mesh location-aware system, which considers factors such as antenna characteristics, propagation channels, and interference within complex real-world environments. We will proceed to embed these algorithms into bespoke location-aware IoT testbeds, capable of modelling a range of environments, and develop graphical tools for demonstration and testing purposes. We propose to use a network of commercially available 'Swarm' radio modules - a network of independent nodes capable of performing time-of-flight ranging to all connected nodes to determine real-time location using broadband UWB and CHIRP communication protocols. Finally, we will develop evaluation and validation methods for location awareness accuracy improvement for moving objects.

With respect to the EPSRC's research priorities, this project aligns closely with the research council's focus on 'Pervasive and ubiquitous computing'; developing context and location awareness is an important part of integrating IoT smart technologies into areas such as transport and urban infrastructure, and complements EPSRC's C2 and C3 Connected Outcomes to achieve transformational development in the Internet of Things and deliver intelligent and connected solutions in the transport sector. The development of innovative AI algorithms complements the focus on Artificial Intelligence technologies, as well as 'ICT networks and distributed systems', through our use of full-mesh 5G networks to facilitate location estimation, and our research into mitigating factors such as mutual sensor interference.

The project is in collaboration with the National Physical Laboratory (NPL), the UK's national measurement standards laboratory, and the research supports a government theme on CAV systems, focusing on the successful introduction of reliable driverless and driver-assisted vehicles. AI, sensor and control systems are revolutionising terrestrial, marine and aviation transport services, and automation is a government priority with the potential to disrupt models of vehicle ownership. This, combined with the transition to electric vehicles, is an important step towards achieving net-zero CO2 targets within the transport sector, and we believe our work has strong potential to increase the integration of automation-based technology in this area.

The primary outputs from the CDT will be cohorts of highly qualified, interdisciplinary postgraduates who are experts in a wide range of sensing activities. They will benefit from a world leading training experience that recognises sensor research as an academic discipline in its own right. The students will be taught in all aspects of Sensor Technologies, ranging from the physical and chemical principles of sensing, to sensor design, data capture and processing, all the way to applications and opportunities for commercialisation, with a strong focus in entrepreneurship, technology translation and responsible leadership. Students will learn in extensive team and cohort engaging activities, and have access to cutting-edge expertise and infrastructure. 90 academics from 15 different departments participate in the programme and more than 40 industrial partners are actively involved in delivering research and business leadership training, offering perspectives for impact and translation and opportunities for internships and secondments. End users associated with the CDT will benefit from the availability of outstanding, highly qualified and motivated PhD students, access to shared infrastructure, and a huge range of academic and industrial contacts.

Immediate beneficiaries of our CDT will be our core industrial consortium partners (MedImmune, Alphasense, Fluidic Analytics, ioLight, NokiaBell, Cambridge Display Technologies, Teraview, Zimmer and Peacock, Panaxium, Silicon Microgravity, etc., see various LoS) who incorporate our cross-leverage funding model into their corporate research strategies. Small companies and start-ups particularly benefit from the flexibility of the partnerships we can offer. We will engage through weekly industry seminars and monthly Sensor Cafés, where SME employees can interact directly with the CDT students and PIs, provide training in topical areas, and, in turn, gain themselves access to CDT infrastructure and training. Ideas can be rapidly tested through industrially focused miniprojects and promising leads developed into funded PhD programmes, for which leveraged funding is available through the CDT.

Government departments and large research initiatives are formally connected to the CDT, including the Department for the Environment, Food and Rural Affairs (DEFRA); the Cambridge Centre for Smart Infrastructure and Construction (CSIC); the Centre for Global Equality (CGE); the National Physics Laboratory (NPL); the British Antarctic Survey (BAS), who all push our CDT to generate impacts that are in the public interest and relevant for a healthy and sustainable future society. With their input, we will tackle projects on assisted living technologies for the ageing population, diagnostics of environmental toxins in the developing world, and sensor technologies that help replace the use of animals in research. Developing countries will benefit through our emphasis on open technologies / open innovation and our exploration of responsible, ethical, and transparent business models. In the UK, our CDT will engage directly with the public sector and national policy makers and regulators (DEFRA, and the National Health Service - NHS) and, with their input, students are trained on impact and technology translation, ethics, and regulatory frameworks.