JOURNAL ARTICLE

Energy Efficient Clustering Algorithm for Wireless Sensor Networks

Abstract

In Wireless Sensor Networks (WSNs), sensor nodes are usually deployed with limited energy reserves in remote environments for a long period of time with less or no human intervention. It makes energy efficiency as a challenging issue both for the design and deployment of sensor networks. This paper presents a novel approach named Energy Efficient Clustering Algorithm (EECA) for Wireless Sensor Networks which is based on two phases clustering model and provides maximum network coverage in an energy efficient way. In this framework, an effective resource-aware load balancing approach applied for autonomous methods of configuring the parameters in accordance with the signaling patterns in which approximately the same bit rate data is provided for each sensor. This resource-efficient clustering model can also form energy balanced clusters which results in increasing network life time and ensuring better network coverage. Simulation results prove that EECA is better than LEACH, LEA2C and EECS with respect to network lifetime and at the same time achieving more network coverage. In addition to obtained an optimal cluster size with minimum energy loss, the proposed approach also suggests new and better way for selecting cluster heads to reduce energy consumption of the distributed nodes resulting in increased operational reliability of sensor networks.

Keywords:
Wireless sensor network Cluster analysis Computer science Energy consumption Key distribution in wireless sensor networks Efficient energy use Distributed computing Computer network Mobile wireless sensor network Real-time computing Wireless network Wireless Engineering Telecommunications

Metrics

13
Cited By
1.36
FWCI (Field Weighted Citation Impact)
0
Refs
0.82
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Energy Efficient Wireless Sensor Networks
Physical Sciences →  Computer Science →  Computer Networks and Communications
IoT-based Smart Home Systems
Physical Sciences →  Engineering →  Electrical and Electronic Engineering

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