research-article

Improved clustering algorithm based on energy consumption in wireless sensor networks

Authors:

Chunxue WuAuthors Info & Claims

IET Networks, Volume 6, Issue 3

Pages 47 - 53

https://doi.org/10.1049/iet-net.2016.0115

Published: 01 May 2017 Publication History

Abstract

Wireless sensor networks (WSNs) are widely used in military, traffic, medical and so on. The design of routing protocol for WSNs is limited by the single nature of the local topology information. Meanwhile, the power supply of sensor networks node, communication ability and storage capacity are limited, so how to improve the efficient energy of nodes and extend the networks life cycle is the focus of current research. This study proposes the improved algorithm for the LEACH (Low Energy Adaptive Clustering Hierarchy) clustering algorithm, considering the residual energy of the nodes and the factors of the long distance node, the T (n) is readjusted and the new method is proposed. Then the data fusion rate is introduced to allow the cluster‐heads to fuse data before sending the data, and send the data to the base station. Finally, the free‐space model and the multi‐path fading model are adopted to avoid the excessive consumption of energy caused by the node d⁴. The authors’ simulation results show that the improved algorithm can reduce the energy consumption of the networks and prolongs life cycle.

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Cited By

Yesodha KKrishnamurthy MThangaramya KKannan A(2024)Elliptic curve encryption-based energy-efficient secured ACO routing protocol for wireless sensor networksThe Journal of Supercomputing10.1007/s11227-024-06235-180:13(18866-18899)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11227-024-06235-1
Cui QLi LHuang W(2022)A Unified Synchronization Criterion for Reaction-Diffusion Neural Networks with Time-Varying Impulsive Delays and System DelayNeural Processing Letters10.1007/s11063-022-10994-455:3(2989-3006)Online publication date: 10-Aug-2022
https://dl.acm.org/doi/10.1007/s11063-022-10994-4
Wang FLi RWang HZhu HXiong N(2021)TS-PADMWireless Communications & Mobile Computing10.1155/2021/66564982021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6656498
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Published In

cover image IET Networks

IET Networks Volume 6, Issue 3

May 2017

22 pages

EISSN:2047-4962

DOI:10.1049/ntw2.v6.3

Issue’s Table of Contents

© 2017 The Institution of Engineering and Technology.

Publisher

John Wiley & Sons, Inc.

United States

Publication History

Published: 01 May 2017

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Cited By

Yesodha KKrishnamurthy MThangaramya KKannan A(2024)Elliptic curve encryption-based energy-efficient secured ACO routing protocol for wireless sensor networksThe Journal of Supercomputing10.1007/s11227-024-06235-180:13(18866-18899)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11227-024-06235-1
Cui QLi LHuang W(2022)A Unified Synchronization Criterion for Reaction-Diffusion Neural Networks with Time-Varying Impulsive Delays and System DelayNeural Processing Letters10.1007/s11063-022-10994-455:3(2989-3006)Online publication date: 10-Aug-2022
https://dl.acm.org/doi/10.1007/s11063-022-10994-4
Wang FLi RWang HZhu HXiong N(2021)TS-PADMWireless Communications & Mobile Computing10.1155/2021/66564982021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6656498
He PWu CLiu KXiong N(2020)Deep Reinforcement Learning Based on Spatial-Temporal Context for IoT Video Sensors Object TrackingSmart Computing and Communication10.1007/978-3-030-74717-6_24(226-235)Online publication date: 29-Dec-2020
https://dl.acm.org/doi/10.1007/978-3-030-74717-6_24
Wu PWu GWu XYi XXiong N(2020)Birds Classification Based on Deep Transfer LearningSmart Computing and Communication10.1007/978-3-030-74717-6_19(173-183)Online publication date: 29-Dec-2020
https://dl.acm.org/doi/10.1007/978-3-030-74717-6_19

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