Research on wireless sensor network location based on Improve Pigeon-inspired optimization

Abstract

Wireless sensor network (WSN) is a hot research field at present. As a key technology of WSN, localization algorithm plays an important role in improving node location accuracy and network efficiency. An improved Pigeon-inspired Optimization(IPIO) combined with a typical localization model is proposed to solve the problem of node localization accuracy in wireless sensor networks (WSN). First of all, a Pigeon-inspired Optimization based on pareto distance classification is proposed to optimize the fitness calculation method, and then the self-learning idea and speed formula are combined. Finally, the position correction factor is introduced into the late updating formula of pigeon group to further improve the positioning accuracy. The simulation results show that compared with the improved particle swarm optimization(PSO) and the cuckoo swarm(CS), the algorithm can effectively improve the location accuracy of nodes and reduce the cumulative error caused by successive positioning. It has a strong practicability.