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A Novel Energy-Aware Clustering Method (Eacm) for Increasing Network Lifetime in Wireless Sensor Network

SSRN, ISSN: 1556-5068
2023
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Article Description

Wireless Sensor Networks (WSNs) consist of many tiny sensors that can be a powerful tool for data collection in various data-intensive environments. The ideal state in sensor networks is for all nodes to reach the end of their energy, together or through regular scheduling, to maximize the network's lifetime. Battery depletion in sensors means the loss of those sensors, and considering the environmental conditions in which these networks are deployed, replacing the batteries of thousands of sensor nodes is practically impossible. Thus, one of the significant challenges in these networks is the limitation of energy consumption, which directly affects the lifetime of the WSN. According to studies, clustering preserves the limited energy resources of sensors, leading to energy savings and an effective approach for better data aggregation and scalability in large-scale WSNs.In this paper, an ink drop spread is used for clustering. Initially, ink drops are spread using the ink drop spread (IDS) operator with a weight proportional to the energy of each node, and clustering as well as routing is performed based on it. Therefore, clustering is done dynamically, meaning that in each round, clustering is performed again, the cluster heads (CHs) are determined, and ultimately, the node with zero energy is registered as a dead node in the system. The proposed algorithm (EACM) has resulted in improving the quality of clustering, reducing energy consumption in nodes, and ultimately increasing the network's lifetime. Our proposed algorithm is compared with the popular algorithms Leach, PEGASIS, Leach_EX, and the latest algorithms EECPK-means, RaCH, and C3HA. When compared to commonly used algorithms, it demonstrates a high capability. On average, the proposed algorithm has more alive nodes in the network, and the remaining energy is at least 17% higher than the best other algorithms.

Bibliographic Details

Edris Alimohammadi; Sajad Haghzad Klidbary; Mohammad Javadian

Elsevier BV

Multidisciplinary; Wireless Sensor Network (WSN); Active Learning Method; Density-Based Clustering; Energy Optimization; Leach.

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