SEEDS · A holistic design of secure vehicular networks: communications, data caching and blockchain services

A holistic design of secure vehicular networks: communications, data caching and blockchain services

With the rapid development of future connected vehicles, Cyber Security has been gaining increasing importance to the connected systems that are fundamental to our society, health and welfare. It will be given priority to the upcoming areas, including autonomous systems and cyber-physical systems, etc. The SEEDS project sets an ambitious research agenda to fully exploit the potentials of V2X communications, data caching and blockchain services for revolutionizing the security and privacy of vehicular networks. As the conclusion of the key innovations of the project, 1) Firstly, we have applied mathematical modelling approaches and computer simulations to successfully evaluate our developed physical layer security schemes. Our proposed security scheme ensures that the performance of the considered system can be improved by adjusting the SIC decoding order at the MEC server, where the corresponding conditions are derived. Another major outcome of this project is from the multiple access (MA) perspective. In particular, the outcome of this project characterized the fundamental issues and information-theoretic capacity limits of power-domain NOMA, and then extended it to the multiple-input multiple-output (MIMO) scenario, including the foundational principles of near-field communications, and the sensing and communications (ISAC) systems. 2) Secondly, from the networking and data centric point of view, we introduced a robust and multifaceted innovation strategy for securing and optimizing Vehicle-to-Everything (V2X) networks. A major thrust of the work was the development of lightweight, blockchain-based systems tailored to the resource constraints of vehicular environments. In parallel, the project leveraged edge computing and artificial intelligence to enhance the responsiveness and resilience of V2X communications. Deep reinforcement learning techniques were used to create intelligent task offloading agents capable of optimizing computation, latency, and energy use in dynamic vehicular contexts. Edge-based intrusion detection systems were also implemented using distributed machine learning models, providing real-time protection against threats such as denial-of-service attacks, spoofing, and data tampering—while maintaining low latency and computational overhead. Another innovation was the development of advanced security and trust mechanisms within in-vehicle networks. 3) To further validate the project’s innovations, SEEDS built a suite of experimental testbeds. A 5G-V2X campus testbed was developed to test real-world integration of secure blockchain consensus mechanisms and to explore intelligent autonomous driving features.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз