Wireless Sensor Network Localization Based on Improved Particle Swarm Optimization

Wireless sensor networks, which can achieve the target position by acquiring and processing the sensor information, has gained a widely attention in recent years. According to the localization principles with PSSI, we propose a localization method based on improved particle swarm optimization algorithm, which includes the parameters estimation of wireless signal transmission environment, the calculation of distance between target nodes and anchor nodes, the improvement of particle swarm optimization by introducing the adaptive inertia weight based diversity feedback and the grouping mutation strategy. The experimental results show that the improvements can greatly accelerate the convergence speed and enhance the localization accuracy comparing other particle swarm optimization algorithms, and the total root mean square error of target localization is below 0.6m.