Energy Aware Competition-Based Unequal Clustering Protocol for Wireless Sensor Networks

Abstract

In wireless sensor networks, there is a limitation of energy resources. For the enhancement of the network's operational lifetime, energy resources need to be utilized efficiently. In this paper, the energy aware competition-based unequal clustering (EACUC) protocol is suggested where issues related to energy efficiency and energy holes are addressed. In the EACUC protocol, the competition radius is calculated with the help of two parameters, that is, distance from BS and residual energy, and also the cluster radius is further resized based on the node degree. The most appropriate nodes are identified as cluster heads in a competition radius after consideration of two vital parameters: residual energy and node degree. In the suggested EACUC protocol, to lessen the communication overheads, the cluster structure is kept for a set count of communication rounds. The results are simulated in MATLAB, and the performance of the suggested EACUC protocol is compared with four existing protocols: energy-efficient uneven clustering (EEUC), energy aware unequal clustering using fuzzy approach (EAUCF), unequal cluster radius based on node density (URBD), and energy aware unequal clustering algorithm (EAUCA). The results show that EACUC protocol enhances the network operational life by 11.12%, 22.98%, 30.96%, and 46.26% for scenario-1 (base station at center) and 10.63%, 16.41%, 28.21%, and 33.34% for scenario-2 (base station at the corner) compared to EAUCA, URBD, EAUCF, and EEUC, respectively. The outcomes permit the significant improvement in the network lifespan for the suggested EACUC protocol and guarantee consistent energy consumption in addition to resolving the energy hole issue.

Received: 16 April 2024 | Revised: 19 June 2024 | Accepted: 22 July 2024

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Rohit Gupta: Conceptualization, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Vinith Chauhan: Conceptualization, Methodology, Software, Validation, Resources, Data curation, Supervision, Project administration.