Scholarlink

<p>Clustering is a process that groups Internet of Things (IoT) devices or nodes to enhance network efficiency, reduce energy consumption, and improve data management. This research addresses the role of clustering and routing in IoT networks, which rely on sensor networks to collect, process, and transmit environmental data. The proposed method dynamically adjusts the cluster size and the number of cluster members based on factors such as energy consumption, node proximity, and network conditions. This paper proposes a method for selecting cluster heads and reserved cluster heads in an IoT network based on three parameters: residual energy, optimal number of cluster members, and distance from the base station. Using these parameters and a fitness function, each node is assigned a score. The two nodes with the highest scores in each cluster are then selected as the cluster head and the reserved cluster head, respectively. The inclusion of a reserved cluster head reduces overhead during the cluster reconfiguration phase. By optimizing clustering, the proposed method reduces communication overhead, minimizes energy consumption, and improves network scalability. Experimental results demonstrate that the proposed algorithm outperforms traditional clustering methods in terms of energy efficiency, network stability, and network performance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Proposing a dynamic clustering algorithm to improve routing performance in the Internet of Things

  • 期刊论文

  • 发表日期：

    2025年9月29日

查看全文
  • Maryam Masihi,
  • Amin Mehranzadeh,
  • Hamid Barati,
  • Ali Barati,
  • Mohsen Chekin

摘要

Clustering is a process that groups Internet of Things (IoT) devices or nodes to enhance network efficiency, reduce energy consumption, and improve data management. This research addresses the role of clustering and routing in IoT networks, which rely on sensor networks to collect, process, and transmit environmental data. The proposed method dynamically adjusts the cluster size and the number of cluster members based on factors such as energy consumption, node proximity, and network conditions. This paper proposes a method for selecting cluster heads and reserved cluster heads in an IoT network based on three parameters: residual energy, optimal number of cluster members, and distance from the base station. Using these parameters and a fitness function, each node is assigned a score. The two nodes with the highest scores in each cluster are then selected as the cluster head and the reserved cluster head, respectively. The inclusion of a reserved cluster head reduces overhead during the cluster reconfiguration phase. By optimizing clustering, the proposed method reduces communication overhead, minimizes energy consumption, and improves network scalability. Experimental results demonstrate that the proposed algorithm outperforms traditional clustering methods in terms of energy efficiency, network stability, and network performance.