Hybridized Black Widow-Honey Badger Optimization: Swarm Intelligence Strategy for Node Localization Scheme in WSN

The evolutionary growth of Wireless Sensor Networks (WSN) exploits a wide range of applications. To deploy the WSN in a larger area, for sensing the environment, the accurate location of the node is a prerequisite. Owing to these traits, the WSN has been effectively implemented with devices. Using various localization techniques, the information related to node location is obtained for unknown nodes. Recently, node localization has employed the standard bio-inspired algorithm to sustain the fast convergence ability of WSN applications. Thus, this paper aims to develop a new hybrid optimization algorithm for solving the node localization problems among the unknown nodes in WSN. This hybrid optimization scheme is developed with two efficient heuristic strategies of Black Widow Optimization (BWO) and Honey Badger Algorithm (HBA), named as Hybridized Black Widow-Honey Badger Optimization (HBW-HBO) to achieve the objective of the framework. The main objective of the developed heuristic-based node localization framework is to minimize the localization error between the actual locations and detected locations of all nodes in WSN. For validating the developed heuristic-based node localization scheme in WSN, it is compared with different existing optimization strategies using different measures. The experimental analysis proves the robust and consistent node localization performance in WSN for the developed scheme than the other comparative algorithms.