A Novel Localization Algorithm Based on Invasive Weed Optimization in Wireless Sensor Networks

Abstract

Localization is one of the most critical issues in wireless sensor networks (WSNs). An important research direction within localization is to develop schemes by using optimization methods. In this paper, invasive weed optimization (IWO) algorithm is used for the field of WSNs localization. Furthermore, two measures are proposed to improve the performance of algorithm. Firstly, the idea of proactive estimation is put forward and used to narrow down and restrict the feasible solution space, which helps to speed up the global search. Then, an adaptive standard deviation (SD) is presented to replace the constant SD in the original IWO, which helps the algorithm to improve the convergence speed, and make it more exploitive. Results show that the proposed localization algorithm achieves higher accuracy with lower network costs and energy consumption compared to the existing schemes.