Energy-saving clustering routing algorithm based on semi-fixed cluster for wireless sensor networks

被引：0

作者：

Zhang Z. ^{[1
]}

Zhou J. ^{[1
]}

机构：

[1] School of Mathematics and Statistics, Xidian University, Xi'an

来源：

Tongxin Xuebao/Journal on Communications | 2024年 / 45卷 / 04期

基金：

中国国家自然科学基金;

关键词：

clustering; load balance of cluster; optimization of energy consumption; routing; wireless sensor network;

D O I：

10.11959/j.issn.1000-436x.2024080

中图分类号：

学科分类号：

摘要：

To solve the problems of short network lifetime, caused by unbalanced node energy consumption, and large energy consumption in data transmission of wireless sensor network, a semi-fixed clustering routing algorithm was proposed in order to achieve the load balance of each cluster. After the nodes were deployed, each cluster was adjusted to ensure that each cluster was well-matched in the number of nodes. In the beginning, the cluster heads were selected according to the distances between nodes and the residual energy of nodes. In data transmission phase, the optimization model of relay nodes was constructed according to the residual energy of nodes, the distances between nodes and the base station, and the distances between nodes. Then the goal of balancing and reducing energy consumption was achieved. Furthermore, when the network ran for a period of time, due to the large differences in residual energy between clusters, the clusters were optimized again to make the energy of each cluster as close as possible. Finally, when the energy of cluster heads was lower than the set threshold, the cluster heads were rotated and optimized again to extend the working life of nodes. The simulation results show that the proposed algorithm reduces the energy consumption of nodes and provides longer network lifetimes of WSN. © 2024 Editorial Board of Journal on Communications. All rights reserved.

引用

页码：160 / 170

页数：10

共 33 条

[21] KOTARY D K, NANDA S J, GUPTA R., A many-objective whale optimization algorithm to perform robust distributed clustering in wireless sensor network, Applied Soft Computing, 110, (2021)
[22] XIE M, PI D C, DAI C L, Et al., A novel clustering strategy-based sink path optimization for wireless sensor network, IEEE Sensors Journal, 22, 20, pp. 20042-20052, (2022)
[23] WANG M H, WANG S B, ZHANG B W., APTEEN routing protocol optimization in wireless sensor networks based on combination of genetic algorithms and fruit fly optimization algorithm, Ad Hoc Networks, 102, (2020)
[24] LI J P, HAN Q, WANG W T., Characteristics analysis and suppression strategy of energy hole in wireless sensor networks, Ad Hoc Networks, 135, (2022)
[25] YAO Y D, XIE D Y, LI Y, Et al., Routing protocol for wireless sensor networks based on archimedes optimization algorithm, IEEE Sensors Journal, 22, 15, pp. 15561-15573, (2022)
[26] HEINZELMANWB, CHANDRAKASANAP, BALAKRISHNANH, An application-specific protocol architecture for wireless microsensor networks, IEEE Transactions on Wireless Communications, 1, 4, pp. 660-670, (2002)
[27] SUN H B, PAN D J, WANG D, Et al., LPLL-LEACH: a study of a low-power and low-delay routing protocol based on LEACH, Ad Hoc Networks, 140, (2023)
[28] WANG J H, GE Y Y., A radio frequency energy harvesting-based multihop clustering routing protocol for cognitive radio sensor networks, IEEE Sensors Journal, 22, 7, pp. 7142-7156, (2022)
[29] ZHAO X Q, CUI Y P, GUO Z, Et al., Energy-efficient clustering routing protocol for wireless sensor networks based on virtual force, Journal of Software, 33, 2, pp. 622-640, (2022)
[30] GONG Y D, GUO X Y, LAI G M., A centralized energy-efficient clustering protocol for wireless sensor networks, IEEE Sensors Journal, 23, 2, pp. 1623-1634, (2023)

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