Abstract
Vehicular ad hoc networks an important network type plays a significant role in various applications, such as traffic administration, media applications, secure financial transaction, etc. In VANETs, topology rapidly changes due to high vehicle movements, and scarce vehicle distribution (on highways) affects network scalability which makes the cluster of VANETs unstable and difficult to maintain routes of all vehicles in a network These challenges appeal to researchers' attention to allow vigorous, consistent, and scalable transmission and receiving of data, particularly in a highly compact network. This framework proposes and demonstrates an efficient clustering technique for routing optimization in Intelligent Transportation Systems (ITS). An intelligent probability-based bio-inspired Whale Optimization Algorithm for clustering in VANETs (i-WOA) has been proposed considering communication range, the number of nodes (density), velocity, route on the highway during the process of cluster formation for vehicular communication by incorporating fitness function probability thus minimizing the randomness. The results were compared with already established methods and demonstrate that the proposed i-WOA method produces an optimal number of cluster heads (CHs) in various scenarios, for instance, communication ranges, network size, and node density. Statistical tests are performed to further validate developed method superiority over other established bio-inspired methods. The results exhibit a 75% (regression analysis) improvement in cluster optimization for VANETs with application in ITS, consequently reducing communication cost and routing overhead hence increasing network lifetime.
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Husnain, G., Anwar, S. An Intelligent Probabilistic Whale Optimization Algorithm (i-WOA) for Clustering in Vehicular Ad Hoc Networks. Int J Wireless Inf Networks 29, 143–156 (2022). https://doi.org/10.1007/s10776-022-00555-w
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DOI: https://doi.org/10.1007/s10776-022-00555-w