Improving Localization Precision in Wireless Sensor Networks Using Salp Swarm Algorithm

In wireless sensor networks (WSNs), localization technologies such as GPS are often impractical due to environmental constraints and high costs. This paper introduces a novel approach to localization using the salp swarm algorithm (SSA). SSA is adapted to enhance the accuracy of sensor localization in WSNs by efficiently exploring by leader salp and exploiting by follower salps the search space to find optimal positions for unknown sensors. The effectiveness of the proposed SSA-based localization method is evaluated through comprehensive simulations conducted in MATLAB. The performance of SSA is compared with three other algorithms: DV-HOP, particle swarm optimization (PSO), and grey wolf optimizer (GWO). Various parameters, including the number of unknown sensors, anchors, and communication radius, are analyzed to assess their impact on localization accuracy.Results demonstrate that the SSA algorithm consistently outperforms the other methods in terms of localization accuracy. SSA achieves superior results, especially in denser networks and with a larger number of anchors, showing significant reductions in localization error compared to DV-HOP, PSO, and GWO. The findings indicate that SSA is a robust and effective solution for precise localization in WSNs, offering promising prospects for future applications and further research in optimizing sensor network deployments.