Research on Dynamic Routing Method Based on Communication Quality Optimization Model and Improved Grey Wolf Algorithm

Wireless sensor networks (WSNs) require efficient dynamic routing to adapt to changing topologies and resource constraints . This study aims to optimize dynamic routing by addressing the limitations of existing heuristic algorithms (e.g., slow convergence, local optima) in dynamic environments like industrial Internet of Things (IoT) . Existing methods (e.g., ACO, GA) struggle with node mobility and link fluctuations, while continuous-based Grey Wolf Optimization (GWO) is unsuitable for discrete routing decisions. We propose a dynamic routing method using a Communication Quality Optimization Model (integrating path delay, energy consumption, bandwidth, and link stability) and an Improved Grey Wolf Algorithm (IGWO) featuring discrete position encoding, a dynamic convergence factor, and chaotic perturbation (Introduction of Your Method). This adaptation enables efficient discrete optimization, enhances exploration-exploitation balance, and avoids premature convergence. Simulation results demonstrate 15.2% higher network coverage, 28.7% lower packet loss, and ~40% faster convergence compared to benchmarks . The method provides an effective solution for WSN deployment in 5G, Industrial Internet of Things (IIoT), and smart grids, significantly improving communication reliability and resource efficiency .