Pigeon-Inspired Optimization for Node Location in Wireless Sensor Network

Wireless Sensor Network (WSN) refers to a network of devices that can communicate the information gathered from a monitored field through wireless links. As a critical technology of WSN, the localization algorithm plays a vital role in improving node location accuracy and network efficiency. A hybrid Pigeon Inspired Optimization (PIO) with a typical localization model is proposed to solve the problem of node localization in WSN. The self-learning idea of PIO and speed formula are combined to improve exploring and exploiting agents of PIO. Fitness function for optimization is mathematically modeled based on analysis Pareto distances. The simulation results compared with the other approaches in the literature, e.g., the improved particle swarm optimization (PSO) and the cuckoo search (CS) show that the proposed method effectively improves the location accuracy of nodes and reduces the cumulative error caused by success positioning nodes.