Adaptive Trust Models for Wireless Sensor Networks Using Blockchain and Edge Intelligence

Abstract

Environmental sensing, smart city applications, industrial automation, and contemporary monitoring all depend on wireless sensor networks. However, there are serious trust and security issues because of their dispersed structure, resource limitations, and deployment in frequently dangerous locations. Conventional wireless sensor network trust systems rely on centralized authority and local reputation metrics, which have issues with scalability, adaptability, and resistance to complex attacks. In order to provide a reliable, scalable, and comprehensible trust management solution for wireless sensor networks, this study suggests an integrated architecture that blends adaptive trust modeling with blockchain-backed ledgering and edge intelligence. The suggested methodology records trust anchors, transaction summaries, and policy updates utilizing an immutable blockchain layer, periodic aggregation and adaptive fusion at edge devices using machine learning, and lightweight local trust estimators at sensor nodes. The system constantly modifies the trust weighting based on ambient inputs, node behavior, and context. We introduce the system architecture, formal trust update rules, a lightweight consensus and storage approach appropriate for limited contexts, and a security analysis that addresses common threats like collusion, on-off assaults, Sybil attacks, and fake data injection. When compared to baseline reputation systems, a simulation-based study shows improvements in the detection of misbehaving nodes, a decrease in false positives, and resilience against coordinated attacks. The method provides obvious routes to deployment in practical WSN applications while striking a compromise between enhanced network-level security and energy and communication overhead.