Energy-aware Routing Algorithm In Wireless Sensor Networks Using Water-cycle Algorithm And Fuzzy Logic System

被引:0
作者
Li, Yinghua [1 ]
机构
[1] XIAN MingDe Inst Technol, Xian 710124, Shanxi, Peoples R China
来源
JOURNAL OF APPLIED SCIENCE AND ENGINEERING | 2024年 / 27卷 / 12期
关键词
WSN; Routing; Clustering; Energy efficiency; Optimization; CLUSTERING-ALGORITHM; INTERNET;
D O I
10.6180/jase.202412_27(12).0004
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Clustering in Wireless Sensor Networks (WSNs) has emerged as a critical strategy for improving network efficiency and extending the network's lifespan. Optimizing energy efficiency becomes paramount as the demand for WSNs grows in applications such as underground mining, healthcare, security surveillance, and environmental monitoring. This work introduces a novel hybrid clustering approach that combines the WaterCycle Algorithm (WCA) with a fuzzy logic system to address the inherent challenges in clustering WSNs. The primary motivation for this research is to enhance energy efficiency, prolong network operation, and address the shortcomings of traditional clustering methods. These shortcomings include unbalanced clusters, suboptimal cluster head selection, and limited adaptability to diverse application requirements. The proposed approach aims to overcome these limitations by utilizing the WCA's inspiration from the natural water cycle coupled with a dynamic fuzzy logic system for cluster head selection. The proposed approach is tested for different network sizes and compared with existing algorithms. The results suggested that the suggested algorithm is superior to its competitors regarding network lifetime and energy consumption.
引用
收藏
页码:3635 / 3644
页数:10
相关论文
共 27 条
[1]  
Abedinia O., 2011, P 2011 10 INT C ENV, P1, DOI [10.1109/EEEIC.2011.5874843, DOI 10.1109/EEEIC.2011.5874843]
[2]  
Abedinia O, 2014, Journal of Modeling in Engineering, V12, P1
[3]   A Novel Hybrid Artificial Bee Colony-Based Deep Convolutional Neural Network to Improve the Detection Performance of Backscatter Communication Systems [J].
Aghakhani, Sina ;
Larijani, Ata ;
Sadeghi, Fatemeh ;
Martin, Diego ;
Shahrakht, Ali Ahmadi .
ELECTRONICS, 2023, 12 (10)
[4]   Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization [J].
Ahmadian I. ;
Abedinia O. ;
Ghadimi N. .
Frontiers in Energy, 2014, 8 (04) :412-425
[5]   A localization and deployment model for wireless sensor networks using arithmetic optimization algorithm [J].
Bhat, Soumya J. ;
Santhosh, K., V .
PEER-TO-PEER NETWORKING AND APPLICATIONS, 2022, 15 (03) :1473-1485
[6]   Decision tree based ensemble machine learning model for the prediction of Zika virus T-cell epitopes as potential vaccine candidates [J].
Bukhari, Syed Nisar Hussain ;
Webber, Julian ;
Mehbodniya, Abolfazl .
SCIENTIFIC REPORTS, 2022, 12 (01)
[7]   New Approach of Energy-Efficient Hierarchical Clustering Based on Neighbor Rotation for RWSN [J].
Chen, Jie ;
Zhang, Degan ;
Zhang, Jie ;
Zhang, Ting ;
Zhu, Haoli ;
Qiu, Jianning .
IEEE ACCESS, 2020, 8 :123123-123134
[8]   Dominant Feature Selection and Machine Learning-Based Hybrid Approach to Analyze Android Ransomware [J].
Gera, Tanya ;
Singh, Jaiteg ;
Mehbodniya, Abolfazl ;
Webber, Julian L. ;
Shabaz, Mohammad ;
Thakur, Deepak .
SECURITY AND COMMUNICATION NETWORKS, 2021, 2021
[9]   Distributed on-demand clustering algorithm for lifetime optimization in wireless sensor networks [J].
Ghosal, Amrita ;
Halder, Subir ;
Das, Sajal K. .
JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 2020, 141 :129-142
[10]  
Han C., 2023, Front. Bus. Econ. Manag, V8, P51, DOI [10.54097/fbem.v8i2.6616, DOI 10.54097/FBEM.V8I2.6616]