Univrsity Of Tehran Press Journal of Information Technology Management 2980-7972 14 4 2022 07 01 AI-WSN: Direction of Arrival Estimation Based on Bee Swarm Optimization for Wireless Sensor Networks 69 86 88136 10.22059/jitm.2022.88136 EN Devika E Research Scholar, Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi, India-642 107. Saravanan A Asssociate Professor, Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi, India-642 107. Journal Article 2022 07 13 An Artificial Intelligence (AI) technique plays the most crucial factor to consider in energy utilization in a wireless sensor network (WSN). AI transforms industrial operations by optimizing the energy consumption in sensor nodes. As a result, it is crucial for improving sensor node location accuracy, particularly in unbalanced or Adhoc environments. Because of this, the purpose of this research is to improve the accuracy of the localization process in locations where sensor nodes encounter barriers or obstacles on a regular basis. The Bees Swarm Optimization (BSO) algorithm is used to segment sensor nodes in order to increase the accuracy of the Direction of Arrival (DoA) estimate between the anchor and unknown node pairs. Even in the presence of unbalanced conditions, the proposed DoA- BSO involving three separate bee colonies can identify plausible anchor nodes as well as segment nodes arranged in clusters. In order to obtain the intended result, the objective function is designed to take into consideration the hops, energy, and transmission distance of the anchor and unknown node pairs, among other factors. The studies are carried out in a large-scale WSN using sensor node pairs in order to determine the precision with which the DoA-BSO can be located. When comparing DoA-BSO to conventional approaches, the findings of the meta-heuristic algorithm show that it improves the accuracy and segmentation of nodes significantly https://jitm.ut.ac.ir/article_88136_cc1df04d507c28d10f9cfb6b824ec9ae.pdf