Contribution of a bio-inspired meta-heuristic for optimization of localization in wireless sensor networks

Abstract

Localization in Wireless Sensor Networks (WSNs) is a fundamental task that directly inf luences network accuracy and performance. Although many localization techniques have been proposed, several limitations remain, while bio-inspired optimization algorithms have shown strong effectiveness in this domain. In this work, two localization algorithms are proposed to improve the positioning of unknown nodes. The first, SLnO-CLA, combines the Centroid Localization Algorithm (CLA) with the Sea Lion Optimization (SLnO) algorithm. SLnO is used to optimize and refine the initial position estimate provided by CLA , improving localization accuracy, and accelerating the convergence. The second algorithm, referred to as IMP-SLnO, is an enhanced version of SLnO that focuses on improving stability and overall performance through multiple enhancement mechanisms that increase diversity, strengthen exploration, and speed up convergence. The performance of SLnO-CLA is evaluated and compared with the classical CLA algorithm across 30 independent runs using identical network deployments. Results show that SLnO-CLA reduces the Mean Localization Error (MLE) by 93.7% compared to plain CLA. The performance of IMP-SLnO is further evaluated against baseline SLnO and several well-known bio-inspired algorithms including Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and SLnO-CLA. IMP-SLnO achieves an MLE of 0.2459 m, representing an 82.1% improvement over baseline SLnO, and outperforms all comparison algorithms across all reported metrics.