Heuristic Selection of Right Functioning Node for Iterated Unscented Kalman Filter-based Mobile Target Tracking System in WSN
Year: 2025
Type: article
Abstract: Node tracking is one of the essential techniques in many military and cellular communication-related applications.Wireless Sensor Network (WSN) has a limited sensing range which influences target tracking and energy consumption.Optimization models in target tracking are well-desired solutions, as they maintain good convergence and reliability.In this work, an efficient approach is developed to perform the target tracking in WSN.At first, the tracking reliability requirement is ensured by selecting the right functioning nodes in the WSN with the help of the Tiger Beetle Algorithm (TBA).The TBA adopts energy as an objective function for attaining better performance in the node selection process.The overall energy consumption of WSN is enhanced with the help of optimum selection of right functioning nodes which is used to prolong the network lifetime.After selecting the right functioning nodes, the target tracking is performed using the Iterated Unscented Kalman Filter (IUKF).Finally, evaluation is conducted to verify the performance of the developed optimized IUKF target tracking model.Root Mean Squared Error (RMSE) validation was performed in the suggested TBA-IUKF-based mobile target tracking model and accomplished 5.63, which is less than classical techniques.The proposed TBA-IUKF has achieved a minimal comparative energy consumption value of 12.5%, 10.3%, 14.8%, and 9.8% compared to existing optimization techniques.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409406694