Online sequential DV-Hop Localization Algorithm Based on Machine Learning for Wireless Sensors Network

Abstract

Abstract In many applications of Wireless Sensor Network (WSN), the transmitted data collected from the connected wireless sensor nodes has not great significance without knowing its geographical location. In this paper, firstly, we propose an analytical probabilistic model for multi-hops distance estimation between anchor nodes positions and unknown nodes positions. Moreover, we use the Machine Learning Technique (MLT) to optimize the accuracy of node localization in WSN. A range-free technique, based on the multi-hop localization method, is used with the proposed MLT. The proposed method consists of probabilistic expected hop-size and the On-line Sequential Extreme Learning Machine. The localization performance of the proposed techniques is proved through simulation tests when compared with the other soft-computing algorithms in term of Average Localization Errors (ALE).