An intelligent cluster optimization algorithm based on Whale Optimization Algorithm for VANETs (WOACNET)

Vehicular Ad hoc Networks (VANETs) an important category in networking focuses on many applications, such as safety and intelligent traffic management systems. The high node mobility and sparse vehicle distribution (on the road) compromise VANETs network scalability and rapid topology, hence creatin...

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Veröffentlicht in:PloS one Jg. 16; H. 4; S. e0250271
Hauptverfasser: Husnain, Ghassan, Anwar, Shahzad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 21.04.2021
Public Library of Science (PLoS)
Schlagworte:
Ad hoc networks (Computer networks)
Algorithms
Biology and Life Sciences
Clustering
Clustering (Computers)
Communication
Computer and Information Sciences
Computer science
Conversion ratio
Decision making
Earth Sciences
Engineering
Genetic algorithms
Heuristic
Mathematical optimization
Mathematical programming
Mobile ad hoc networks
Motor vehicles
Optimization
Optimization algorithms
Physical Sciences
Research and Analysis Methods
Social Sciences
Vehicles
Pakistan
Peshawar Pakistan
ISSN:1932-6203, 1932-6203
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Zusammenfassung:Vehicular Ad hoc Networks (VANETs) an important category in networking focuses on many applications, such as safety and intelligent traffic management systems. The high node mobility and sparse vehicle distribution (on the road) compromise VANETs network scalability and rapid topology, hence creating major challenges, such as network physical layout formation, unstable links to enable robust, reliable, and scalable vehicle communication, especially in a dense traffic network. This study discusses a novel optimization approach considering transmission range, node density, speed, direction, and grid size during clustering. Whale Optimization Algorithm for Clustering in Vehicular Ad hoc Networks (WOACNET) was introduced to select an optimum cluster head (CH) and was calculated and evaluated based on intelligence and capability. Initially, simulations were performed, Subsequently, rigorous experimentations were conducted on WOACNET. The model was compared and evaluated with state-of-the-art well-established other methods, such as Gray Wolf Optimization (GWO) and Ant Lion Optimization (ALO) employing various performance metrics. The results demonstrate that the developed method performance is well ahead compared to other methods in VANET in terms of cluster head, varying transmission ranges, grid size, and nodes. The developed method results in achieving an overall 46% enhancement in cluster optimization and an F-value of 31.64 compared to other established methods (11.95 and 22.50) consequently, increase in cluster lifetime.
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Competing Interests: The authors have declared that no competing interests exist
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0250271