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

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 n...

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Veröffentlicht in:Computational intelligence and neuroscience 2021, Vol.2021 (1), p.9808449-9808449
Hauptverfasser: Cao, Li, Yue, Yinggao, Zhang, Yong
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Zhang, Yong
description 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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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.</description><identifier>ISSN: 1687-5265</identifier><identifier>EISSN: 1687-5273</identifier><identifier>DOI: 10.1155/2021/9808449</identifier><identifier>PMID: 34630559</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Algorithms ; Analysis ; Clustering ; Collaboration ; Competition ; Data collection ; Data entry ; Decision making ; Energy consumption ; Energy efficiency ; Energy use ; Genetic algorithms ; Life cycles ; Neural networks ; Nodes ; Optimization ; Optimization algorithms ; Performance evaluation ; Radio equipment ; Routing (telecommunications) ; Search algorithms ; Self organizing maps ; Sensors ; Strategy ; Wireless networks ; Wireless sensor networks</subject><ispartof>Computational intelligence and neuroscience, 2021, Vol.2021 (1), p.9808449-9808449</ispartof><rights>Copyright © 2021 Li Cao et al.</rights><rights>COPYRIGHT 2021 John Wiley &amp; Sons, Inc.</rights><rights>Copyright © 2021 Li Cao et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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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. 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subjects Algorithms
Analysis
Clustering
Collaboration
Competition
Data collection
Data entry
Decision making
Energy consumption
Energy efficiency
Energy use
Genetic algorithms
Life cycles
Neural networks
Nodes
Optimization
Optimization algorithms
Performance evaluation
Radio equipment
Routing (telecommunications)
Search algorithms
Self organizing maps
Sensors
Strategy
Wireless networks
Wireless sensor networks
title A Data Collection Strategy for Heterogeneous Wireless Sensor Networks Based on Energy Efficiency and Collaborative Optimization
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