Energy-efficient coverage optimization in wireless sensor networks based on Voronoi-Glowworm Swarm Optimization-K-means algorithm

Deployment of sensor nodes is one of the crucial factors in mobile wireless sensor networks for improving the performance of the network. The network's lifetime primarily depends on the consumed energy and area coverage by the sensor nodes. The efficiency of mobile wireless sensor networks incr...

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Vydáno v:Ad hoc networks Ročník 122; s. 102660
Hlavní autoři: Chowdhury, Aparajita, De, Debashis
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.11.2021
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ISSN:1570-8705, 1570-8713
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Abstract Deployment of sensor nodes is one of the crucial factors in mobile wireless sensor networks for improving the performance of the network. The network's lifetime primarily depends on the consumed energy and area coverage by the sensor nodes. The efficiency of mobile wireless sensor networks increases by the efficient deployment of the sensors. Coverage and energy consumption mainly depends on the effective deployment schemes of sensors. This article presents an energy-efficient coverage optimization technique with the help of the Voronoi-Glowworm Swarm Optimization-K-means algorithm. In this approach, Glowworm Swarm Optimization, K-means algorithm, and Voronoi cell structure enhance the coverage area with a minimum number of active nodes. This approach considers optimum sensing radius calculation for efficient sensor deployment. Furthermore, the proposed method improves the lifetime of the deployed network by decreasing the consumed energy by the deployed sensor nodes using multi-hop transmission and the sleep-wake mechanism. The simulation result shows that area coverage is achieved by the proposed method up to 99.99% with the optimum number of active sensor nodes.
AbstractList Deployment of sensor nodes is one of the crucial factors in mobile wireless sensor networks for improving the performance of the network. The network's lifetime primarily depends on the consumed energy and area coverage by the sensor nodes. The efficiency of mobile wireless sensor networks increases by the efficient deployment of the sensors. Coverage and energy consumption mainly depends on the effective deployment schemes of sensors. This article presents an energy-efficient coverage optimization technique with the help of the Voronoi-Glowworm Swarm Optimization-K-means algorithm. In this approach, Glowworm Swarm Optimization, K-means algorithm, and Voronoi cell structure enhance the coverage area with a minimum number of active nodes. This approach considers optimum sensing radius calculation for efficient sensor deployment. Furthermore, the proposed method improves the lifetime of the deployed network by decreasing the consumed energy by the deployed sensor nodes using multi-hop transmission and the sleep-wake mechanism. The simulation result shows that area coverage is achieved by the proposed method up to 99.99% with the optimum number of active sensor nodes.
ArticleNumber 102660
Author De, Debashis
Chowdhury, Aparajita
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  surname: Chowdhury
  fullname: Chowdhury, Aparajita
  organization: Department of Information Technology, Techno Main Salt Lake, Kolkata-700091, West Bengal, India
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  givenname: Debashis
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  fullname: De, Debashis
  email: dr.debashis.de@gmail.com
  organization: Centre of Mobile cloud computing, Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, West Bengal, Kolkata -700064, West Bengal, India
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Keywords Voronoi diagram
Lifetime enhancement
K-Means algorithm
Coverage maximization
Glowworm swarm optimization
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Snippet Deployment of sensor nodes is one of the crucial factors in mobile wireless sensor networks for improving the performance of the network. The network's...
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StartPage 102660
SubjectTerms Coverage maximization
Glowworm swarm optimization
K-Means algorithm
Lifetime enhancement
Voronoi diagram
Title Energy-efficient coverage optimization in wireless sensor networks based on Voronoi-Glowworm Swarm Optimization-K-means algorithm
URI https://dx.doi.org/10.1016/j.adhoc.2021.102660
Volume 122
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