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

Lead Research Organisation: University of Cambridge
Department Name: Chemical Engineering and Biotechnology


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.


10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023046/1 30/09/2019 30/03/2028
2394469 Studentship EP/S023046/1 30/09/2020 29/09/2024 Liam Mark Self