An Improved Leach Algorithm Based on Hierarchical Clustering Approach for Wireless Sensor Network Application

Bhih, Amhmed; Abushiba, Walid; Elashheb, Adel

doi:10.1109/icece56287.2022.10048608

Cited by 3 publications

(1 citation statement)

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“…This method also selects the data route between cluster heads based on the residual energy of the cluster heads involved in multi-hop data transmission to the sink, the distance between these cluster heads, and the angle formed by the current cluster head, the next hop cluster head, and the base station. In [20], Bhih et al proposed an improved LEACH-based algorithm, LEACH-Kmeans, that utilizes the K-means clustering technique to select the cluster heads. Similarly to traditional LEACH, LEACH-Kmeans starts with setting the number of cluster heads and then uses the K-means technique to find the cluster center (closer to all nodes in the cluster) and assign it as a cluster head.…”

Section: Leachmentioning

confidence: 99%

HAC: Hierarchical Agglomerative Clustering With Linear Programming for Wireless Sensor Networks

Dine,

Finnerty,

Ohta

2024

IEEE Access

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Wireless sensor networks are a collection of tiny sensor devices powered by energy-scarce batteries. Depending on the nature of the application, those tiny devices might be deployed in areas that make it difficult to recharge or change their battery. Therefore, it is important to optimize the energy consumption of these devices to prolong their lifetime for as long as possible. Clustering is regarded as the most efficient approach to extend the lifetime of wireless sensor networks. This study proposes a novel method integrating cluster head selection and cluster formation to obtain as long a network lifetime as is possible. We employ a linear programming model to resolve the issue of cluster head selection and a Hierarchical Agglomerative Clustering (HAC) method to form clusters in the network. We evaluated HAC on different sets of inputs where we varied the position of the sink from the corner of the network to the center. HAC outperformed other existing clustering techniques for the first node death (FND) and half node death (HND) lifetime definitions in the cases where the sink is at the corner of the network. In the cases where the sink is located at the center of the network, HAC outperformed the other existing techniques for all definitions of network lifetime.INDEX TERMS Clustering, linear programming, network lifetime, power optimization, wireless sensor network.

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Section: Leachmentioning

confidence: 99%