The National Edge AI Hub is pleased to announce an exciting new collaborative publication opportunity in partnership with the Institution of Engineering and Technology (IET).

Rajiv Ranjan, Dhaval Thakker, Baseer Ahmad, and Gagangeet Singh Aujla are currently editing an upcoming book focused on Edge AI, which aims to showcase cutting-edge research and innovation across the Hub’s partner universities. This initiative provides a valuable platform to highlight the breadth of expertise and impactful work being delivered across the National Edge AI Hub community.

As part of this effort, the Hub is seeking to include at least one chapter contribution from each partner institution, ensuring the book reflects the full diversity and strength of the network. The final publication will be positioned as a collective output from the entire Hub.

Contribute Your Ideas

To shape the direction of the book, a bottom-up approach is being adopted. Researchers and colleagues across partner institutions are invited to propose potential chapter contributions.

Submissions at this stage are intentionally light-touch. Contributors are asked to provide:

• A suggested paper title or topic
• Responses to a short form (only four questions)

Proposals will then be reviewed and selected based on emerging themes and overall fit within the book.

Get Involved

All colleagues within partner universities are encouraged to participate, and we welcome this call being shared widely within your institution.

Submission deadline: 5 June
Submission form: Link Here

This is an excellent opportunity to contribute to a high-profile publication and to help present a unified, impactful voice for the UK’s Edge AI research community.

The National Edge AI Hub invites UK academic institutions to collaborate on the development and delivery of events and workshops focused on all aspects of edge artificial intelligence. We are keen to work with colleagues across disciplines who are exploring or interested in edge AI, from foundational research through to real world applications.

The Hub can support those interested in hosting an event with practical and financial aspects of event delivery, including identifying speakers, event logistics, catering costs, and promotional activity. Our aim is to make it straightforward to convene high quality, inclusive events that foster collaboration, share emerging insights, and strengthen the UK edge AI community. We expect local engagement partners to make spaces available for free, to organise and facilitate the agenda and to ensure relevant stakeholders join the event.

We welcome proposals for workshops, seminars, training sessions, and interdisciplinary forums. If you are interested in developing a joint event or would like to discuss an idea, please get in touch via email: hub@edgeaihub.co.uk

The National EdgeAI Hub is excited to announced its 2026 pump priming call aiming to encourage research into Edge AI applications from non-technical perspectives. This initiative seeks to engage disciplines such as social sciences, medical sciences, and related fields to explore the societal, ethical, and practical implications of deploying AI at the edge. By supporting such projects, the Hub aims to broaden the impact of Edge AI beyond technical innovation, fostering insights into human, organisational, and domain contexts where these technologies can deliver meaningful benefits.

We are looking to fund projects lasting up to 12 months with £10k.

We strongly encourage applicants to develop proposals that foster interdisciplinary collaboration, bringing together expertise from diverse fields. Interdisciplinary approaches are essential for addressing the technical, societal, and ethical dimensions of Edge AI adoption and impact. In addition, we actively promote collaboration between academia and industry, as well as partnerships with public sector and civil society organisations. These collaborations help ensure that research is both innovative and practically relevant, enabling real-world application, knowledge exchange, and pathways to impact.

This call invites proposals to the three stands below.
Strand 1: Technology adoption and acceptance
Strand 2: Benefits realisation and technology assessment and impact
Strand 3: Responsible innovation

Applications must be submitted via email after the 1st of May 2026 and before the deadline 1st of June 2026.

For more information please visit the call’s page.

The National Edge AI Hub is pleased to share an early indication of an upcoming funding opportunity, anticipated to open in the second half of 2025. The Hub will support innovative, impactful projects that advance the design, deployment or adoption of Edge AI technologies across UK industry and the public sector. This call will focus on applied research and development activities that demonstrate clear potential for real-world use, economic value and societal benefit in relation to cybersecurity and data disturbances. Projects should align with the Hub’s mission to build capability and confidence in edge AI, and to enable UK organisations to develop trustworthy, efficient, and effective AI systems at the edge.

While we remain open to a broad range of application domains, we are particularly interested in proposals that target one or more of the following sectors:

• Energy
• Health
• Manufacturing
• Smart Cities
• Smart Transport

We welcome collaborations between academia, industry, and other relevant stakeholders. Projects must include direct cash contributions from industry partners, reflecting a shared commitment to delivering practical outcomes and long-term value. Funded project should also plan to contribute to a 2029 impact case study in collaboration with Hub partners. Match-funded proposals with clear financial investment from industry are highly encouraged. Further details, including eligibility criteria, funding scope and guidance on how to apply, will be released when the call formally opens later in 2025. In the meantime, we encourage interested parties to begin considering project ideas and potential partnerships.

For any queries about the fund, its thematic priorities, or its suitability for your business or organisation, please contact us at hub@edgeaihub.co.uk.

Our EdgeAI Hub is taking part at the AI UK 2025, the national showcase of data science and artificial intelligence (AI). Hosted by The Alan Turing Institute and returning for its fifth year, AI UK is an in-depth exploration of how data science and AI can be used to solve real-world challenges. Our diverse programme is thematically structured around the latest innovations from across the AI ecosystem. With a broad range of interactive content, covering the latest thinking on fundamental AI, digital twins, algorithmic bias, AI ethics – and much more.

Come and visit our stand on the 5th floor!

https://ai-uk.turing.ac.uk

Date: 19 May 2025
Location: Tromsø, Norway (co-located with CCGrid 2025)

The EAVSec Workshop invites researchers, practitioners, and industry professionals to submit original work exploring trust and security challenges in Electric and Autonomous Vehicle (EV/AV) ecosystems. As these technologies integrate with cloud systems, energy grids, and urban infrastructure, they face critical security, privacy, and resilience issues.

Submission Deadline: 7 February 2025

For more information about the workshop and to find more information about the submission process please visit EAVSec Workshop 2025 for details.

We are thrilled to announce that two of our latest research papers have been accepted to the prestigious IEEE Big Data 2024 conference!

Paper 1: LEAP: Lifelong Learning Edge-Cloud Adaptive Fused Framework for Mobility Prediction

Authors:
Newcastle University: Shamil Al-Ameen, Tejal Shah, and Rajiv Ranjan.
General Motors: Bharath Sudharsan.
University of Mosul: Roua Al-Taie.

This paper introduces LEAP, a groundbreaking framework that combines edge and cloud capabilities to enable lifelong learning for mobility prediction, paving the way for more intelligent, adaptive systems.

Paper 2: Poly Instance Recurrent Neural Network for Real-time Lifelong Learning at the Low-power Edge

Authors:
Newcastle University: Shamil Al-Ameen, Tomasz Szydlo, Tejal Shah, and Rajiv Ranjan.
General Motors: Bharath Sudharsan.
University of Limerick: Tejus Vijayakumar.

In this work, we present a novel neural network model designed to support real-time lifelong learning directly on low-power edge devices. This model is crucial for enabling efficient, always-on AI applications in resource-constrained environments.

Both projects were made possible by support from the National Edge AI Hub. The hub is dedicated to advancing AI safety and resilience in edge computing environments.

Looking forward to sharing these advancements with the IEEE Big Data 2024 community!

IoTSys 2024 was a prestigious conference that brought together global experts in IoT systems, AI, and emerging technologies. It provided a platform for exchanging ideas and showcasing the latest advancements in these fields. Our team, consisting of Joao Tadeu Pereira Gollnick, Rajiv Ranjan and Devki Nandan Jha worked closely to develop an innovative solution addressing DDoS attacks in edge computing environments. The evaluation was conducted on a real-edge testbed, simulating various attack scenarios. Receiving the Best Paper Award reflects the recognition of the quality and impact of our research in the rapidly evolving IoT landscape.

Abstract: Edge computing has evolved as a decentralised technology for processing data closer to its source, thus reducing latency and improving bandwidth utilisation. However, this shift also introduces new security challenges, particularly Distributed Denial of Service (DDoS) attacks. As there are numerous components, the effect of an attack on one component can be cascaded to other components which can severely disrupt the application execution. To handle that, this paper presents DEFID, a framework for DDoS attack detection and filtering in the edge computing environment. DEFID utilises Extended Berkeley Packet Filter (eBPF) to detect and filter the DDoS attack. Since eBPF allows efficient execution of custom monitoring and filtering programs executing directly within the Linux kernel, it provides a lightweight, high-performance and flexible framework for real-time network traffic analysis. The effectiveness of DEFID is analysed using three separate attack programs, Goldeneye, SlowLoris and Tor’s Hammer Attack in a test environment which shows that the proposed solution is able to accurately detect and filter various types of DDoS attack traffic with minimal overhead

For more infomation visit: https://crowdos.cn/AIoTSys/2024/