Q-Learning Based Optimized Localization in WSN

Localization is an critical issue to solve because, in some instances, it is crucial to identify the pinpoint location of the nodes due to unfavorable circumstances. Various optimization and machine learning algorithms have been employed to locate the nodes accurately. This paper proposes an efficient Q-Learning Based Optimized Localization Algorithm. The proposed work localizes the nodes by generating the k-fold path and choosing the best path through the Q-Learning (QL) technique. The data is transmitted using the AOMDV routing algorithm. The efficacy of the suggested approach was thoroughly scrutinized and compared to established localization-based learning algorithms like K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), and Support Vector Machine (SVM). The simulation results revealed that the proposed Q-Learning Based Optimized Localization algorithm performs effectively for sensor node localization concerning the considered performance parameters.