Reinforcement learning driven self-evolving trust framework for secure and scalable internet of vehicles using blockchain
Abstract
The current high-speed pace of the Internet of Vehicles (IoV) has successfully allowed the construction of intelligent transportation frames; nevertheless, there are several grave issues, such as malicious data injection, dynamic trust variations, scale risks, and strict low-latency, which have been mentioned. Current methods of managing trust use mainly fixed or semi-dynamic reputation models, likely to fail in dynamically changing and adversarial settings of the IoV. In dealing with these issues, this article suggests a self-evolving framework of trust, combining blockchain technology, reinforcement learning (RL), and edge/fog computing to allow real-time, adaptive, and secure assessment of trust. The suggested system will utilise a multi-layered structure in which vehicular information is subject to pre-processing and behavioural evaluation to determine reliability in terms of data accuracy, abnormality detection, and falsity detection. A blockchain ledger keeps historical trust records with constant levels of transparency, impartiality, and withstands modification. The reinforcement learning-based trust decision engine is a dynamic assessment of incoming data, which is formed based on real-time behavioral features and historical trust data, and independent of the acceptance, verification, or rejection of the messages. A mechanism of continuous updating of the trust scores is based on a reward and a penalty system so that the system can adjust to changing network conditions and new patterns of attacks. The framework adopts a low-latency performance using edge and fog computing to compute locally, and privacy-preserving approaches, including anonymization and federated learning, to keep sensitive vehicular information safe. It is shown through a large body of simulations run on SUMO and NS-3 that the proposed framework is much more suited in terms of trust accuracy, attack detection rate, and communication latency to scale over a large area and set up the next generation IoV systems.
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Authors
Copyright (c) 2026 Poushali Das, Suity Roy, Ipsita Pathak, Sudipta Bhanja, Siddhartha Chatterjee (Author)
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Copyright (c) 2025 International Journal of Applied Resilience and Sustainability (IJARS) 
This work is licensed under a Creative Commons Attribution 4.0 International License.