QLB-IoT: Intelligent and Trust-Aware Routing for IoT-Edge Networks with Blockchain-Assisted Q-Learning

With the exponential growth of Internet of Things (IoT) devices, edge computing has emerged as a vital paradigm for localized data processing, low-latency communication, and efficient resource utilization. However, routing in IoT-based edge networks remains challenging due to dynamic topologies, constrained energy resources, and growing security threats. To tackle these challenges, this paper proposes QLB-IoT, a novel framework that integrates Q-learning-based intelligent routing with blockchain-assisted trust management to optimize energy consumption while ensuring secure and adaptive data transmission. The proposed method enables IoT devices to autonomously learn energy-efficient routing paths based on real-time network parameters such as residual energy and link distance. The Q-learning mechanism minimizes route flapping and promotes adaptive decision-making. Simultaneously, blockchain and digital signatures establish a decentralized trust layer that authenticates devices and prevents attacks like blackhole and Sybil. Compared with Open Shortest Path First (OSPF), Q‑Routing, Deep Reinforcement Learning-based control framework for traffic engineering (DRL‑TE), and Intelligent Edge Network Routing (ENIR), exhibits an extended network lifetime to ≈ 1300 rounds (2.4× OSPF and 8% beyond ENIR), increases throughput by 18–30%, and reduces average latency by half, while minimizing packet loss by ≈ 40% and increasing the likelihood of quality of service (QoS) compliance by up to 10%. These improvements stem from a real-time exploration–exploitation mechanism that dynamically reroutes traffic away from energy-depleted nodes, and from the immutable ledger that prevents malicious route manipulation without centralized oversight. By combining adaptive, energy-aware routing with verifiable trust, QLB-IoT delivers a scalable and resilient solution for mission-critical IoT-edge deployments.