In this paper, each node in Wireless Sensor Networks (WSN) network is classified into either residual node or non-residual node using machine learning classification method. This proposed method consists of training which is followed by classification mode. The training mode of this proposed system trains both residual and nonresidual node in WSN network through feature extraction and feature optimization with Adaptive Neuro Fuzzy Inference System (ANFIS) classification in training mode, which produces trained pattern for both residual and nonresidual nodes in WSN network. The classification mode of this proposed system classifies the unknown node in WSN network through feature extraction and feature optimization with ANFIS classification in classification mode, which produces index factor. The performance of the proposed residual node detection methodology is analyzed with respect to energy consumption, Packet Delivery Ratio (PDR) and latency.
Volume 11 | 08-Special Issue
Pages: 187-194