A Localization Algorithm Based on Coevolutionary Noise-Suppressing Newton Method for Wireless Sensor Network

A robust and high-precision localization algorithm is crucial for the valid running of wireless sensor networks. However, the conventional localization algorithms hardly overcome the shortcoming of low accuracy. Concerning this problem, a novel coevolutionary noise-suppressing Newton method (CNSNM) is proposed, which combines the advantages of the noise-suppressing Newton method (NSNM) and particle swarm optimization (PSO) algorithm to enhance its accuracy and robustness. Specifically, the PSO algorithm conducts a global search using all beacon nodes, and the NSNM carries out local searches with different beacon node groupings, respectively. When the two algorithms get their solutions, NSNM utilizes the solution of the PSO algorithm to correct its search starting point and distance errors. Simultaneously, the PSO algorithm uses the best solution of NSNM to correct its distance errors, and then the next search begins until the termination conditions are satisfied. Simulation results show that the proposed method has the advantages of high accuracy and noise-suppressing performance. Further, since the CNSNM does not require the sensor to be equipped with specific measuring components, does not depend on GPS signals, and has low-computational burden, it possesses potential in the generalized application of real-world scenarios.