Localization with beacon based support vector machine in Wireless Sensor Networks

Recent developments in radio technology and processing systems, Wireless Sensor Networks (WSNs) are tremendously being used to perform an assortment of tasks from their atmosphere. Localization plays the most important task in WSNs. Accuracy is the one of the major problems facing localization. In this paper, we propose an improved localization algorithm based on the learning concept of support vector machine (SVM). In SVM classification the finite size of grid cells offer the localization accuracy. The localization error using the proposed algorithm is calculated and compared with basic SVM and fuzzy logic. Simulation result demonstrates that the improved support vector machine can effectively reduce the localization error and thus achieve the objective of better accuracy.