Projects

Funding Agency: EPSRC

Total Funding: £730,002 (£151,067 to Durham)

Duration: 01 Sep 2023 – 31 August 2026

PI: Dr. Yang Long, Co-I: Dr. Gagangeet Aujla

Partners: Newcastle University and Cardiff University

Industry Partners: NR Electric UK Limited, China Electric Power Research, Gainwell Technologies, Thales UK Limited, VSB Technical University of Os, General Motors Corporation, BorgWarner Ltd, PA Consulting Group, Aurrigo Ltd.

Description: This project aims to address the diversity and uncertainty of cyber security of autonomous vehicle. It is joined bid with Newcastle University and Cardiff University. Based on the research foundation of our core research team, autonomous vehicle system is simulated in a Software Defined Network (SDN) so as to capture the diversity and uncertainty of cyber attacks. Machine Learning (ML) is developed to drive the attack generation. Validation and evaluation of the damage and response needed is studied and inform real-world stakeholders from our industrial partners.

Funding Agency: EPSRC

Total Funding: £2.02M

Duration: 1 July 2023 – 30 Jun 2026

Hub PI: Professor Julie McCann, Vice-Dean (Research) for Imperial’s Faculty of Engineering

Durham PI: Prof. Hongjiang Sun, Durham Co-Is: Dr. Gagangeet Aujla, Dr. Anish Jindal and Dr. Wanqing Tu

Partners: Imperial College London (Lead), University of Glasgow, University of York, University of Leeds, Cranfield University

Weblink: https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/X040518/1

Description: The CHEDDAR: Communications Hub for Empowering Distributed clouD computing Applications and Research lies on this interface between communications fundamentals and an infrastructure that serves our many communities. Bringing together a spectrum of experts not only in communications but across quantum, neuro-symbolic AI, autonomy etc who have worked in application areas from Farms to Space via Smart Cities, and who understand security and privacy, CHEDDAR aims to build a community that will provide the step change in research and be agile to address new opportunities as they arise. CHEDDAR also aims to take a different and inclusive approach to this research which includes clear opportunities for community engagement and dialogue across the UK, and in researcher development providing both technical and soft-skills round inclusivity and responsible research. With a particular focus on security/privacy and sustainability and resilience this Hub aims to accelerate the national scale innovation process in connected computing research and make UK a lighthouse globally. This is necessary as the UK has demonstrated its ability to be world leading in topics such as AI, Autonomy etc., but many EPSRC and UKRI funded advances are set to unleash their £Bn’s of potential on the economy – it requires new communication interfaces to connect them and unleash these benefits, whilst mitigating the new risks that emerge. We aim to develop the pathways to enabling new communication network design and innovation, through connections with the other 2 hubs, with centres of doctoral training and innovation knowledge centres, institutes, government and standards bodies etc. and identify mechanisms for joint capability and facility sharing and cross-fertilize the co-design of connected computing capabilities.

Funding Agency: The Royal Society, Scheme: International Exchanges

Total Funding: £12,000

Duration: 2022-2024

PI: Dr Anish Jindal, Co-I: Dr Gagangeet Singh Aujla

Partner: Dr. Luca Foschini, University of Bologna, Italy

Description: This project aims to provide an IoT-based transportation system which will contribute to the good health and well-being of citizens as well as provide better overall traffic flow in densely populated cities which helps the environment by reducing the harmful carbon emissions.

Funding Agency: The Royal Society, Scheme: International Exchanges

Total Funding: £12,000

Duration: 2022-2024

PI: Dr Anish Jindal

Partner: Dr. Nurul Sarkar, Auckland University of Technology, New Zealand.

Description: This project aims to provide an Programmable, secure and resilient IoT communication networks for smart grid.

Funding Agency: EPSRC

Total Funding: £401,205 (EP/S033483/) and £191,339 (EP/S033483/2)

Duration: EP/S033483/1: 01/11/2019-19/09/2021, EP/S033483/2: 20/09/2021-31/10/2023

PI: Prof. Thomas Erlebach, Co-I: Dr Amitabh Trehan

PostDoc: Konstantinos Dogeas (since September 2022)

Visiting Researchers and Collaborators:

• Evripidis Bampis, Sorbonne Université, Paris

• Christoph Dürr, Sorbonne Université, Paris

• Nicole Megow, University of Bremen

• Jens Schlöter, University of Bremen

Ex-Staff: Michael Hoffmann, University of Leicester (co-PI, until June 2021) and Murilo Santos de Lima (post-doc, until April 2021)

Website: https://sites.google.com/view/thomas-erlebach/home/acute

Description:How much information should we collect before making a decision? This question underlies the research area of computing with explorable uncertainty. For example, assume that we want to build a network connecting a set of branch offices. For any two locations A and B of branch offices, we have an estimate of the cost for building a link between A and B based on the distance between them. The exact cost of building a link between A and B can be determined by further investigations, but these investigations take time and cost money. If we knew the exact link cost for every pair of locations, we could determine the cheapest way of building the network using a known algorithm for the “minimum spanning tree” problem. The approach of first determining for all pairs of locations the exact cost of building a link between them is not efficient, however: It will take a long time to determine all the exact link costs, and the costs for obtaining that information will be significant. It is therefore desirable to find efficient methods for selecting in a clever way the pairs of locations for which the link costs need to be determined, while still achieving the goal of being able to build a cheap network with the information gained. Algorithms for computing with explorable uncertainty solve such problems: They specify a strategy for selecting the pairs of locations for which exact information should be determined until sufficient information has been gained to determine the best possible network to be built. More generally, computing with explorable uncertainty deals with problems where part of the input is uncertain (known only approximately) but can be obtained at a cost using a query operation.

Previous work on computing with uncertainty has focused on the setting where queries are made one by one sequentially (which may take a long time) and where the goal is to make as few queries as possible while ensuring that sufficient information is obtained to solve the problem optimally. This leaves open the question of how the queries should be selected if a number of queries can be made at the same time in parallel, which is realistic in many applications (for example, in the application outlines above, the exact costs of building links between several pairs of branch office locations could be determined in parallel). Another direction that has not yet been sufficiently considered is the setting where the goal is to optimize a combination of the query cost and the cost of the solution determine in the end. The project aims to take research in computing with explorable uncertainty to the next level by addressing these open questions and developing new algorithms that work provably well in the described scenarios. Methods developed in the project can potentially be useful to any decision-making scenarios where additional information about the input data of a problem is available in principle and can be obtained at a cost.