AN OPTIMIZED ALGORITHM DESIGN FOR A TARGET TRACKING IN WIRELESS SENSOR NETWORKS - Haroon Rashid Hammood Al Dallal; Doaa Ashour Sultan (Journals Park Publishing, 2023)
Due to its sensor technologies, wireless sensor networks have captured the interest of many academics over the past few decades. Several application is a developing approaches in the development of WSNs. In a WSN, numerous sensor nodes are placed throughout a sizable geographic area, and communication happens via wireless technology. Networks having sensors that can electronically detect, analyze, store, and communicate are known as wireless sensor networks (WSNs). Multiple sensors that can detect physical variables like temperatures, light, moisture, and vibrations can be connected in each network terminal. In many applications, including spotting enemy movement in military applications, the positioning of a sensor network in WSNs is significant. Finding the coordinates of all target nodes with the aid of cluster centers is the main goal of the localization algorithm. Two variations of the bat optimization algorithm (BOA) are suggested in this study to more effectively localize the sensor nodes and to get over the basic BOA's limitations, such as becoming stuck in locally optimal solutions. The outcomes of different models for different target nodes and node density counts are compared with the original optimized algorithm and other optimization techniques already in use for the node localization problems. Additionally, given a range of target and node number values, the suggested BOA versions 1 and 2 are compared with the original BOA in terms of different mistakes and localization effectiveness. The model results show that the suggested BOA variation 2 has several advantages over the suggested BOA variant 1 and the present BOA. In comparison to the proposed BOA variation 1, BOA, and other current optimization methods, the node localization based on the suggested BOA variant 2 is more efficient since operations are completed faster and the mean translation error is lower.
Keywords: Edge intelligence, Node localization, target tracking, Target nodes, WSN, Mobile agent (MA), collaborative computing, target tracking, deep reinforcement learning.