Recent trends in clustering algorithms for wireless sensor networks: A comprehensive review

计算机科学 聚类分析 可扩展性 无线传感器网络 路由协议 领域(数学) 过程(计算) 数据挖掘 分布式计算 布线(电子设计自动化) 机器学习 计算机网络 数据库 数学 纯数学 操作系统
作者
Adnan Ismail Al-Sulaifanie,Bayez K. Al-Sulaifanie,Subir Biswas
出处
期刊:Computer Communications [Elsevier BV]
卷期号:191: 395-424 被引量:11
标识
DOI:10.1016/j.comcom.2022.05.006
摘要

In the past two decades, network clustering has been proven as efficient approach for data collection and routing in wireless sensor networks (WSNs). It provides several advantages over other methods in terms of energy efficiency, scalability, even energy distribution, etc. Given the limited capabilities of sensor nodes energy resources, processing power, and communication range, cluster-based protocols accommodate the network’s operation with these constraints. Several survey papers present and compare many clustering algorithms from various perspectives. However, most of these surveys either are outdated or have limited scope. This paper provides a comprehensive review of clustering algorithms where the new ideas and concepts proposed in each phase of the clustering process are extensively studied. Three topics are discussed in this review. First, we present the objectives, characteristics and challenges of clustering algorithms. Second, the cluster-head selection methods for different types of WSNs are extensively studied. Third, this review presents a detailed description of newly proposed methods to handle energy heterogeneity, energy harvesting, fault-tolerance, scalability, mobility and data correlation in WSNs. Furthermore, the protocols taxonomy in each phase is discussed to provide a deeper understanding of current clustering approaches. Finally, a set of criteria is presented to simplify the comparison and identify each protocol’s pros and cons. This review presents a comprehensive introduction and can be a useful guidance for new researchers in this field. Also, it will help system designers to identify alternative solutions for selecting an appropriate method in each phase of the clustering process.
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