Adaptive Load Balancing in Industrial IoT Sensor Networks Using Dynamic Spanning Tree-Based Routing

Abstract

Spanning tree-based dynamic graph routing implements a topology-aware approach for facilitating adaptive load balancing in clustered Wireless Sensor Networks (WSNs). Traditional routing methods often encounter communication constraints and energy inefficiencies resulting from fixed route choices and unequal data distribution across cluster members. The proposed framework establishes a spanning tree structure that dynamically adjusts to changes in topology, energy levels, and node density. The main goal is to enhance route selection while guaranteeing equitable workload allocation across all clusters, thereby prolonging network lifespan and reducing packet latency. The approach combines dynamic edge reconfiguration with localized decision-making, enabling real-time modifications in routing pathways according to residual energy and communication traffic. Each cluster creates an energy-weighted spanning tree that is regularly updated via distributed coordination, improving scalability and fault tolerance. Experimental assessments under diverse network settings reveal enhanced throughput, decreased transmission overhead, and stable energy usage across all nodes, confirming its appropriateness for extensive WSN implementations.