Localization in 3D sensor networks using stochastic particle swarm optimization

Localization is one of the key technologies in wireless sensor networks, and the existing PSO-based localization methods are based on standard PSO, which cannot guarantee the global convergence. For the sensor network deployed in a three-dimensional region, this paper proposes a localization method using stochastic particle swarm optimization. After measuring the distances between sensor nodes, the sensor nodes estimate their locations using stochastic particle swarm optimization, which guarantees the global convergence of the results. The simulation results show that the localization error of the proposed method is almost 40% of that of multilateration, and it uses about 120 iterations to reach the optimizing value, which is 80 less than the standard particle swarm optimization.