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Advanced multi-hop clustering (AMC) in vehicular ad-hoc network

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Abstract

Clustering improves network stability and scalability significantly by efficiently handling the fast topological change in VANET. Multi-hop clustering is introduced in VANET to handle some significant issues observed in the single-hop clustering, i.e., low cluster coverage, high number of clusters formed, and inefficient bandwidth utilization in routing decisions and internetwork data processing. Among many, cluster forming based on neighbourhood following strategy is the most recent and advanced way to form multi-hop clustering. By exploring the weaknesses and limitations of the existing neighbourhood following strategy, this paper introduced an advanced multi-hop clustering (AMC) algorithm. The proposed algorithm is empowered with an efficient cluster formation procedure that helps the vehicle to select and follow the most suitable target vehicle from one-hop neighbours. The suggested CH selection and cluster merging procedure contribute to strengthening stability. Further, we suggested the routing protocol of AMC to check the data transmission performance. Here, one vehicle appointed as a cross node at the road junction assigns the weight to the road segments by proper connectivity analysis. The lowest weight route signifies the best data forwarding path. The algorithm performance is tested in the realistic type scenario where vehicles’ speed and arrival time are randomized. The detailed simulation result shows the strength of the AMC algorithm.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology Allahabad, Teliyarganj, Prayagraj, Uttar Pradesh, 211004, India

    Abhay Katiyar, Dinesh Singh & Rama Shankar Yadav

  2. Department of Computer Science and Engineering, Jabalpur Engineering College, Jabalpur, Madhya Pradesh, India

    Abhay Katiyar

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Katiyar, A., Singh, D. & Yadav, R.S. Advanced multi-hop clustering (AMC) in vehicular ad-hoc network. Wireless Netw 28, 45–68 (2022). https://doi.org/10.1007/s11276-021-02822-9

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