Abstract
Node Localization (NL) in wireless sensor networks (WSNs) is main procedure for defining physical matches such as longitude, latitude, and altitude of Sensor Nodes (SNs) organized in a provided region. Exact NL is very essential for numerous WSN applications like surveillance, asset tracking and environmental monitoring. Localization models involve GPS hardware, anchor nodes with recognized locations or algorithms that influence distance dimensions and connectivity designs amongst SN in order to evaluate their places. Trustworthy NL improves exactness and efficiency of data collection and study in WSNs, finally boosting up the network’s performance and quality information it offers. This article introduces a tuna swarm algorithm-based node localization (TSA-RNL) technique in WSN. The major aim of the TSA-RNL model to focus unknown nodes in WSN. TSA-RNL technique is developed for enhancing the localization accuracy in the WSN. The TSA, stimulated from the collective nature of tuna fish, optimizes the localization process by iteratively refining node positions. Over wide simulation and experimentation, we estimate the TSA-RNL model performance and establish its authority in gaining great accurateness node localization in WSNs. The methods provide potential advantages for many applications that based on specific node positioning, environmental monitoring, data fusion and target tracking that contributing to the development of WSN methodology.
Keywords: Node Localization, Sensor Node (SN), Target Node, Tuna Swarm Algorithm (TSA), Wireless Sensor Network.