Node Localization in WSN and IoT Using Harris Hawks Optimization Algorithm

During the last decade Wireless Sensor Networks and IoT have taken an overwhelming place in engineering and environmental applications. Thus, with growing interest in large size and complex networks, some critical challenges have to be taken care of such as energy consumption and node localization, especially when dealing with real-time applications. Such factors affect strongly the system performance in sensitive fields like e-health or military applications. The primary purpose is to determine the location of the sensor node that triggers the event. Energy consumption of each node is a serious problem for WSN when it comes to extend the life time of the whole network. In this paper, we propose a multicriteria optimization scheme based on a bio-inspired algorithm called Harris Hawks Optimization Algorithm (HHOA). It is shown that the proposed paradigm is capable of increasing the localization rate, as well as minimizing the energy consumption of the nodes. HHOA populations are able to share information in a multi-agent fashion to compute the trigger's position. Developing this algorithm on a huge WSN with millions of nodes reveals a particularly high performance. To assess this, several experiments in different scenarios are carried out in a decentralized environment of WSN. Finally, a comparative study is carried out also to some recent bio-inspired algorithms.