AE-LEACH: An Incremental Clustering Approach for Reducing the Energy Consumption in WSN

Abstract

In recent years, optimizing sensor nodes energy appears to be a strong element in improving the performance of the Wireless Sensor Network (WSN). In the focus of these different approaches, substantial outputs regarding energy optimization are revealed by applying clustering-based object tracking techniques. Clustering is achieved through an enhanced adaptive LEACH algorithm, named Advanced Efficient Low-Energy Adaptive Clustering Hierarchy (AE-LEACH) algorithm. The cluster head (CH) is adopted through firm descriptors like residual energy and reserve distance from the Base Station (BS) in the clustering process. The selected CH predicts the target trajectory using Particle Filter (PA) algorithm and selectively activates from the next round of sensor nodes to continuously track the target. The target nodes’ predictable locations are routed to the base station through the backbone that lies in the cluster head. Gini index is used to assess the energy efficiency of the clustering algorithm . Thus, the proposed performance metrics stated on network timespan, residual energy, energy consumption, and Gini index for remaining energy and achieves significantly lower Gini index of upto 78.38%, 86.11%, and 85.92% as compared to K means clustering , HEED clustering, and LEACH clustering algorithm, respectively. Moreover, time accuracy is directly proportional to the network lifetime and energy consumption.