Abstract
Vehicle ad-hoc networks (VANETs) are a subclass of mobile ad hoc networks (MANETs). The VANETs communication framework is used to provide communication between moving vehicles in highway and urban road scenarios. Dynamic properties of VANETs, such as high dynamic topology, frequent route failure, high mobility of nodes, and bandwidth constraints, reduce the efficiency of routing. The long length route between source and destination affects the efficiency of the protocol in the form of high overhead, frequent disconnections, high packet loss rate, low packet delivery rate, and low throughput. In this paper, we propose an Enhanced Hybrid Ant Colony Optimization Routing Protocol (EHACORP) to improve the efficiency of the routing process using the shortest path. The shortest path in the proposed protocol has low communication costs and the least number of hops between source and destination vehicles. The EHACORP has two phases. In phase 1, the EHACORP relies on a distance calculation method to compute the distance between vehicles. In phase 2, the source-based ant colony optimization is used to guide the ants to build a shorter path with the least number of hops to transmit data. The shortest path improves the efficiency of protocol in all aspects. The simulation results show that the EHACORP has improved packet delivery rate, throughput, end-to-end delay, routing overhead, and packet loss rate compared to Fuzzy based ant colony optimization (F-ANT), Ad hoc on-demand distance vector (AODV), ant colony optimization routing algorithm (ARA), and AntNet routing protocols.


























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RR carried out all research steps including finding the problem, analyzing the related works, and implementing the proposed method as well as writing the manuscript. MT has contributed to analyzing the data and editing the paper. All authors read and approved the final manuscript.
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Ramamoorthy, R., Thangavelu, M. An enhanced hybrid ant colony optimization routing protocol for vehicular ad-hoc networks. J Ambient Intell Human Comput 13, 3837–3868 (2022). https://doi.org/10.1007/s12652-021-03176-y
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DOI: https://doi.org/10.1007/s12652-021-03176-y