Stochastic Fractal Search with Reinforcement Learning for dynamic routing in resource-constrained IoT networks

Abstract

The Internet of Things (IoT) enables pervasive connectivity and large-scale data exchange among heterogeneous devices, driving advancements in intelligent automation and decision-making. However, the resource-constrained nature of IoT nodes and the increasing data traffic in dense deployments demand energy-efficient and adaptive communication strategies. This paper presents a hybrid framework that integrates Stochastic Fractal Search (SFS) for energy-efficient, trust-aware clustering with Q-Learning-based Reinforcement Learning (RL) for dynamic multi-hop routing in IoT and edge-assisted environments. The SFS-based clustering mechanism optimizes cluster-head selection by minimizing intra-cluster energy consumption and ensuring balanced load distribution, while the RL-based routing module adaptively learns optimal forwarding paths based on residual energy, link distance, and trust metrics. A dual trust model, incorporating both energy trust and data trust, is employed to detect and isolate unreliable nodes, thereby enhancing network reliability and security. The proposed SFSRL framework effectively reduces redundant transmissions, balances energy utilization, and enhances scalability across heterogeneous IoT-edge infrastructures. Simulation results validate that SFSRL outperforms existing protocols such as TEC-SFS, RINA, GATERP, and MODRL in terms of energy efficiency, network lifetime, Packet Delivery Ratio (PDR), data rate, throughput, and end-to-end delay, establishing it as a robust and scalable solution for next-generation IoT applications.