Abstract: Localization is essential in Wireless Sensor Networks (WSNs) for communication protocols such as geographic routing and applications including person tracking, battlefield monitoring, and environmental surveillance. This paper proposes an improved localization method called SGOL (Social Group Optimization for Node Localization), which combines the Social Group Optimization (SGO) algorithm with K-means clustering to minimize the error between estimated and real positions of unknown sensors. To address the collinearity problem of anchor nodes, K-means clustering selects geometrically well-distributed anchor nodes. A weighted fitness function assigns different precision values to original anchor nodes versus estimated ones obtained through iterative localization stages. A multi-stage iterative process enables isolated nodes (those with fewer than three visible anchors) to be localized using previously localized nodes as reference points. Extensive simulations over 50 independent runs with 100 nodes, 15 anchors, and 5% noise show that SGOL achieves an average localization error of 0.870 meters, representing improvements of 6.5%, 11.2%, 31.1%, and 75.1% compared to Jaya NL-WSN [1], PSO [2], FOA-L [3], and CSO [4], respectively. SGOL also achieves a higher localization rate than all the compared algorithms due to its multi-stage architecture. Notably, K-means clustering becomes increasingly effective as network size grows, contributing up to 19% error reduction in large-scale deployments.

Key words: wireless sensor networks localization, swarm intelligence, social group optimization, k-means clustering, range-based localization, iterative localization