Fuzzy-Topsis-Based Cluster Head Selection in Mobile Wireless Sensor Networks

Khan, Bilal; Bilal, Rabia

doi:10.4018/978-1-5225-1785-6.ch012

Cited by 3 publications

(1 citation statement)

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“…Aiming at the problem of cluster head selection in mobile heterogeneous wireless sensor networks, Verma et al proposed a genetic algorithm-based cluster head selection method, which balances the energy consumption and improves the efficiency of the network [ 23 ]. In [ 24 ], the author proposed a fuzzy TOPSIS cluster head selection method based on multicriteria decision-making, which effectively selects the channel and maximizes the lifetime of the wireless sensor network. The comprehensive consideration of cluster head selection includes remaining energy, node energy consumption rate, number of adjacent nodes, average distance between adjacent nodes, and distance to the receiver.…”

Section: Related Workmentioning

confidence: 99%

A Data Collection Strategy for Heterogeneous Wireless Sensor Networks Based on Energy Efficiency and Collaborative Optimization

Cao

Yue

Zhang

2021

Computational Intelligence and Neuroscience

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In the clustering routing protocol, prolonging the lifetime of the sensor network depends to a large extent on the rationality of the cluster head node selection. The selection of cluster heads for heterogeneous wireless sensor networks (HWSNs) does not consider the remaining energy of the current nodes and the distribution of nodes, which leads to an imbalance of network energy consumption. A strategy for selecting cluster heads of HWSNs based on the improved sparrow search algorithm- (ISSA-) optimized self-organizing maps (SOM) is proposed. In the stage of cluster head selection, the proposed algorithm establishes a competitive neural network model at the base station and takes the nodes of the competing cluster heads as the input vector. Each input vector includes three elements: the remaining energy of the node, the distance from the node to the base station, and the number of neighbor nodes of the node. The best cluster head is selected through the adaptive learning of the improved competitive neural network. When selecting the cluster head node, comprehensively consider the remaining energy, the distance, and the number of times the node becomes a cluster head and optimize the cluster head node selection strategy to extend the network life cycle. Simulation experiments show that the new algorithm can reduce the energy consumption of the network more effectively than the basic competitive neural network and other algorithms, balance the energy consumption of the network, and further prolong the lifetime of the sensor network.

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Section: Related Workmentioning

confidence: 99%