Abstract
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.
Highlights
In the last few decades, meta-heuristic approaches are getting popular in the area of computer vision and machine learning
The problem addressed in this paper is to develop an innovative, intelligent WOACNET considering many parameters simultaneously, finding an efficient solution that incorporates all the challenges and issues of Vehicular Ad hoc Networks (VANETs)
The results are presented from diverse perceptions like grid size, transmission range, and a number of nodes
Summary
In the last few decades, meta-heuristic approaches are getting popular in the area of computer vision and machine learning. The cluster which comprises of vehicles will be directly relational to the communication limit of those nodes While creating these clusters considering other important parameters like transmission ranges, grid size, and the number of nodes, speed, and direction of nodes are very important as the lifetime of these clusters be increased directly and the overall performance of the entire network could be optimized indirectly. WOA method has been revisited/ modified resulting in proposing and developing a novel WOACNET as per the requirements of clustering optimization in Vehicular Ad Hoc Network for Intelligent Transportation to reduce the number of clusters increasing the network lifetime.
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