A Novel Localization Algorithm Based on Grey Wolf Optimization for WSNs

Abstract

As an information acquisition and processing method, wireless sensor network is also an important component of the Internet of Things. As a key core technology of wireless sensor networks, localization technology has become an important direction in the current and future research of wireless sensor networks, which is also a key issue related to the real application of wireless sensor networks and the Internet of Things. The research of localization algorithm based on intelligent computing technology is paid more attention. In this paper, a novel intelligent computing method — grey wolf optimization was used to localization in wireless sensor network and proposed a novel localization algorithm. The validity and practicability of the proposed algorithm were verified by simulation experiments. The convergence performance and localization result was discussed and compared by the classical traditional intelligent computing methods—particle swarm optimization algorithm. Moreover, the localization performance under different anchor node proportion and different communication radius were analyzesed in this paper. The simulation results show that the proposed algorithm has higher localization accuracy, and it needs fewer anchor nodes and smaller communication radius to achieve the same accuracy, thus saving cost.