Whale Optimization Algorithm with Exploratory Move for Wireless Sensor Networks Localization

In the modern era, with the development of new technologies, such as cloud computing and the internet of things, there is a greater focus on wireless distributed sensors, distributed data processing and remote operations. Low price and miniaturization of sensor nodes have led to a large number of applications, such as military, forest fire detection, remote surveillance, volcano monitoring, etc. The localization problem is among the greatest challenges in the area of wireless sensor networks, as routing and energy efficiency depend heavily on the positions of the nodes. By performing a survey of computer science literature, it can be observed that in the wireless sensor networks localization domain, swarm intelligence metaheuristics have generated compelling results. In the research proposed in this paper, a modified/improved whale optimization swarm intelligence algorithm, that incorporates exploratory move operator from Hooke-Jeeves local search method, applied to solve localization in wireless networks, is presented. Moreover, we have compared the proposed improved whale optimization algorithm with its original version, as well as with some other algorithms that were tested on the same model and data sets, in order to evaluate its performance. Simulation results demonstrate that our presented hybridized approach manages to accomplish more accurate and consistent unknown nodes locations in the wireless networks topology, than other algorithms included in comparative analysis.