Nature-Inspired Optimization Algorithms for Localization in Static and Dynamic Wireless Sensor Networks—A Survey

Abstract

In the current global economy, localization in WSNs becomes a hot topic and it is attracting many researchers towards itself. WSN plays an important role in tracking objects in indoor as well as outdoor environments. The sensor data is useless until the location of the reporting node is unknown. The main aim of localization in wireless sensor networks (WSNs) is to determine the coordinates of target nodes in the sensing field by applying different approaches. This can be done either by the global positioning system (GPS) or any other methods. Usually, in localization methods, anchor nodes (GPS-equipped) broadcast beacon signals in the sensing field to help target nodes to localize themselves. The terms localization accuracy, localized nodes and computing time are mainly responsible for the performance of WSNs. The transmission range and density of anchor nodes directly affect the localization error. In this paper, the author surveys the various localization techniques optimized by nature-inspired algorithms and compares the salp swarm optimization algorithm with some well-known existing algorithms.