Energy-efficient clustering optimization for heterogeneous wireless sensor networks with K-nearest neighbors algorithm

Abstract

Wireless sensor networks (WSNs) consist of autonomous sensing nodes whose operation relies on limited energy resources. To extend the operational lifetime of such systems, optimizing power consumption is essential. Clustering plays a key role in enhancing network stability and improving communication efficiency. This work proposes an improved clustering strategy based on the K-nearest neighbors (KNN) algorithm to reduce transmission distances in heterogeneous networks. By integrating this approach into hierarchical protocols such as Department of Electrical and Electronic Engineering (DEEC), Electrical Engineering Center (EEC), Center for Advanced Materials and Processes (CAMP), the method refines both node selection and cluster organization. Simulation results obtained using network simulator 2 (NS2) demonstrate that the modified protocols significantly reduce energy dissipation while increasing overall network lifetime. This combined approach improves system efficiency and provides a robust solution for sustainable energy management in WSNs, meeting the requirements of highly demanding applications.