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<p>In this era of automation, the wireless sensor network plays a vital role in recent research. The applications of Wireless Sensor Networks(WSNs) increased in all-important fields like environmental studies, landslide detection, healthcare field and agricultural field. Due to the use in various remote areas, it is important to use the available battery of sensors so that the available network can be utilized with maximum lifetime. Clustering techniques utilize the available battery and to maximize the network lifetime. Clustering divides, the WSN in some clusters and optimize the data communication process to optimize battery utilization in delivery of data to final destination or sink node. The sink node is easily accessible by the user to get the information either by the internet or directly to the base station. The Cluster Head (CH) is responsible to collect the information of its vicinity and to transfer it to the sink node or Base Station (BS). Various authors have suggested a lot of algorithms for CHs finalization. Selecting CHs based on multiple parameters yields the best cluster heads to extend the networks lifespan. We have improved the fitness functions efficiency in this suggested algorithm, which chooses the cluster head depending on a number of factors. We have utilized Ordered Weight Averaging (OWA) operators to generate the optimal weights of various parameters used in the fitness function so that optimal cluster heads are selected. Simulation results verified that our suggested algorithm outperforms others and extends network lifetime by 5–12%.</p>

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Multi-Objective Clustering Algorithm Using Ordered Weighted Averaging (OWA) Operator for Heterogeneous WSN

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • Prince Rajpoot,
  • Ram Avtar,
  • Amit Kumar Pandey,
  • Vishal Singh Chandel,
  • Ritika Yaduvanshi,
  • Vikas Srivastava

摘要

In this era of automation, the wireless sensor network plays a vital role in recent research. The applications of Wireless Sensor Networks(WSNs) increased in all-important fields like environmental studies, landslide detection, healthcare field and agricultural field. Due to the use in various remote areas, it is important to use the available battery of sensors so that the available network can be utilized with maximum lifetime. Clustering techniques utilize the available battery and to maximize the network lifetime. Clustering divides, the WSN in some clusters and optimize the data communication process to optimize battery utilization in delivery of data to final destination or sink node. The sink node is easily accessible by the user to get the information either by the internet or directly to the base station. The Cluster Head (CH) is responsible to collect the information of its vicinity and to transfer it to the sink node or Base Station (BS). Various authors have suggested a lot of algorithms for CHs finalization. Selecting CHs based on multiple parameters yields the best cluster heads to extend the networks lifespan. We have improved the fitness functions efficiency in this suggested algorithm, which chooses the cluster head depending on a number of factors. We have utilized Ordered Weight Averaging (OWA) operators to generate the optimal weights of various parameters used in the fitness function so that optimal cluster heads are selected. Simulation results verified that our suggested algorithm outperforms others and extends network lifetime by 5–12%.