Hybridised swarm intelligence approach for multi-objective-based node localisation in wireless sensor network: hybrid glow-worm and cat swarm algorithm

Abstract

ABSTRACT The widely used communication network is a Wireless Sensor Network (WSN), and it is utilized to track the target in different places and also it is used to monitor the disaster in the natural environment. The localization of the sensor nodes in the WSN is a critical issue. Improper localization of sensor nodes reduces the performance during communication. To alleviate the aforementioned problem, a novel approach of node localization is proposed. Especially, an effective localization model is developed for WSN by computing the displacement measure among the anchor nodes and non-anchor or unknown nodes by deriving the objective function. As the known position of anchor nodes in WSN, it is aided for finding the place of unidentified nodes using a hybrid optimization algorithm. Here, the Modernized Position-based Glowworm and Cat Swarm Optimization (MP-GCSO) algorithm is employed for deriving the multi-objective function. After allocating the position, the optimum positions are obtained by the maximum hop counts using a hybrid model. Finally, the experimentation is carried out and analyzed with various constraints and three shapes as C-shape, H-shape, and S-shape. Hence, the outcome proves that the model appropriately finds the location of an unknown node in WSN.