Genetic Algorithm-determined Path Optimization for Energy-Efficient Multi-Hop Routing Scheme for Wireless Sensor Networks

Abstract

Wireless Sensor Networks basically constitute a critical infrastructure for pervasive monitoring, environmental sensing, and internet-of-things applications. Nevertheless, the inherent energy limitations of battery-powered sensor nodes remain a fundamental bottleneck constraining network longevity. Conventional routing strategies frequently ignore path-length heterogeneity, resulting in premature node depletion and unbalanced traffic loads. This paper proposes a Genetic Algorithm-determined Path Optimization (GAPO) scheme that systematically determines energy-efficient multi-hop communication routes for WSNs equipped with multiple sink nodes. The GAPO framework incorporates a novel fitness function derived from the cumulative Euclidean distance across candidate routes, augmented by elitism-guided selection, two-point crossover, and an adaptively tuned mutation operator. Comprehensive MATLAB-based simulations with 60 randomly deployed sensor nodes within a 150 m × 150 m deployment field confirm that GAPO achieves path-length reductions of up to 55% relative to direct node-to-sink transmission. Comparative evaluation against the Improved Threshold sensitive Energy Efficient sensor Network protocol (iTEEN) protocol further demonstrates that GAPO prolongs network lifetime by sustaining a higher number of active nodes per round across 2,000 operational rounds while maximising packet delivery to sink nodes. These outcomes affirm that GAPO represents a scalable and robust routing paradigm suitable for deployment in both small-scale and large-scale heterogeneous WSN environments.