Enhancing secure IoT data sharing through dynamic Q-learning and blockchain at the edge

Abstract

Secure and efficient data sharing in Industrial Internet of Things (IIoT) is a continuous difficulty due to the limits of static proxy node selection, centralized designs, and the lack of agility in dynamic situations. Traditional systems often suffer from excessive latency, single points of failure, tight access control, and vulnerability to targeted attacks. To address these limitations, we offer BDEQ (Blockchain-based Dynamic Edge Q-learning), a novel framework combining blockchain smart contracts and deep Q-learning for real-time, trust-aware proxy node selection. Unlike static systems, BDEQ’s reinforcement learning agent dynamically selects appropriate edge nodes based on performance, resource availability, and trust criteria. This ensures secure access control, decentralized auditing, and resilience to security attacks. In a simulated gas-industry IIoT context, BDEQ lowered data access latency by 35% and boosted throughput by 28% over baseline approaches while giving greater resilience to attacks. These results validate BDEQ’s relevance to next-generation IIoT contexts needing adaptive, decentralized, and secure data sharing.