Abstract

Enhanced energy for data aggregation in wireless sensor networks based on\nclustering techniques uses fuzzy-c clustering algorithm to perform energy efficient data\naggregation of the wireless sensor network models. In sensor network models, always data\naggregation is of prime importance due its dependency on numerous factors like topology of the\nnetwork, link factors, and energy constraints and so on. Traditional techniques fail in energy\nefficient data aggregation because of the node’s battery power and the degradation of network\nlifetime. Hence, in this research paper attempt is made to improve the data aggregation and\nnetwork lifetime of the sensor network model employing a new enhanced optimization\nalgorithm (EOA) based fuzzy c-means (FCM) clustering technique. The new enhanced\noptimization algorithm is developed considering the worst positions traversed by the population\nduring the optimization process and hence moving towards attainment of better optimal cluster\npoints. Fuzzy c-means clustering is designed in this paper by employing ensemble elbow\ntechnique based data aggregation process. The newly developed EOA algorithm is hybridized\nwith the fuzzy c-means clustering technique so as to model a novel hybrid EOA-FCM technique\nfor better data aggregation and improving the lifetime of the sensor network models. In this\nwork, Bray-Curtis similarity index is employed to evaluate the similarity between the sensor\nnodes and that of the cluster nodes. Simulation is carried out for the considered network model\nemploying the proposed EOA-FCM technique and the set performance metrics are evaluated to\nprove the superiority of the developed hybrid EOA-FCM technique in comparison with the\nexisting approaches from literatures.