Strategic Node Deployment Scheme for Maximizing Coverage Area and Network Lifetime in UASNs Using Voronoi-Fuzzy C-Means and Salp Swarm Optimization

The strategic deployment of nodes in underwater acoustic sensor networks (UASNs) is pivotal for enabling critical network functions such as localization and efficient monitoring. Achieving optimal deployment necessitates balancing two vital parameters: area coverage and network lifetime. Improving o...

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Vydáno v:IEEE sensors journal Ročník 24; číslo 10; s. 16926 - 16934
Hlavní autoři: Sunil Kumar, Kammula, Singh, Deepak, Anand, Veena
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 15.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Centroids
Clustering algorithms
Convergence
Coverage holes
Energy consumption
Energy utilization
Heuristic algorithms
Monitoring
node deployment
Nodes
Optimization
Parameters
Particle swarm optimization
Performance evaluation
Polygons
salp swarm optimization (SSO)
Sensors
Service life assessment
underwater acoustic sensor networks (UASNs)
Underwater acoustics
Voronoi polygons
Wireless sensor networks
ISSN:1530-437X, 1558-1748
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Shrnutí:The strategic deployment of nodes in underwater acoustic sensor networks (UASNs) is pivotal for enabling critical network functions such as localization and efficient monitoring. Achieving optimal deployment necessitates balancing two vital parameters: area coverage and network lifetime. Improving one parameter in isolation may adversely affect the other, emphasizing the need for joint optimization of area coverage and energy utilization to enhance overall UASN efficiency. This article introduces a novel node deployment scheme leveraging Voronoi-fuzzy C-means (FCM) and salp swarm optimization (SSO) to address challenges associated with random node deployment, such as uneven distribution, coverage overlaps, and holes. First, FCM computes initial centroids for optimal node placement. Subsequently, Voronoi polygons around these centroids address issues of spatial overlaps and indistinct boundaries. Additionally, Voronoi facilitates the determination of an optimum sensing radius. SSO refines node locations within each polygon by converging the nodes toward centroids. Furthermore, a sleep-wake mechanism for sensor nodes further extends the network lifetime. Performance evaluation reveals that our proposed scheme outperforms existing methods in diverse network scenarios. The proposed method achieved a remarkable 99.31% area coverage, extended network lifetime, and reduced energy consumption across diverse scenarios, all accomplished with a high convergence rate.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3381957