Distributed energy efficient clustering and swarm intelligence based cluster head selection in heterogeneous wireless sensor network

Abstract

Heterogeneous Wireless Sensor Network (HWSN) the energy usage of newlineSensor nodes is not certainly sufficient. In order to optimize the durability of newlineHWSN, it is essential to minimize the utilization of energy. Clustering is newlineefficient scheme for network topology management by scalability, reduced newlineenergy usage, and lesser routing delay. Cluster Head (CH) selection plays a newlinemajor important role in clustering algorithms. Some of the issues during CH newlineselection in the existing algorithms are ignoring the energy, heterogeneity of newlinesensor nodes, choosing unsuitable CH nodes and reduced scalability of newlinecentralized design. newlineIn this work, distributed energy efficient clustering algorithms and CH newlineselection via the Swarm Intelligence (SI) methods has been introduced for newlineHWSN. First contribution of the work Distributed Entropy Energy-Efficient newlineClustering Algorithm (DEEEC) and Chaotic Firefly Algorithm Cluster Head newline(CFACH) for HWSN is proposed for energy efficient in HWSN. Second newlinecontribution of the work, solves the issue of energy efficient in Precision newlineAgriculture (PA) via Heterogeneous Precision Agriculture with Improved newlineNode Selection and Distributed Clustering Algorithm in WSN . newlineInitial Contribution of the work, Chaotic Firefly Algorithm Cluster newlineHead (CFACH) selection, and Distributed Entropy Energy-Efficient newlineClustering (DEEEC) algorithm is introduced for HWSN. Proposed DEEEC newlineAlgorithm, CH selection has two main stages. In the first stage, the newlineidentification of temporary CH along with its entropy value is found using the newlinecorrelative measure of residual and original energy. Along with the rotating newlineepoch and its entropy value must be predicted automatically by its sensor newlinenodes. newline