查看文章

The localization problem in wireless sensor networks (WSN) has recently received justifiable interest from researchers. Various optimization/learning algorithms are used to determine the accurate node localization. The main aim of this paper is to develop a novel Optimized Localization Learning Algorithm (OLLA) and analyze it with well-noticed localization-based learning algorithms, namely Approximate Point in Triangle (APIT), Localization Algorithm using Expected hop Progress (LAEP), Randomized Approximate Nearest Neighbors (RANN) and Standard-based Particle Swarm Optimization (SPSO). The performance is investigated based on indoor and outdoor scenarios, on specific parameters: absolute localization error, relative localization error, root mean square error, and probability distribution on various anchor nodes. Simulation results show that the proposed learning algorithm OLLA performs well in …