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An adaptive intersection selection mechanism using ant Colony optimization for efficient data dissemination in urban VANET

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Abstract

Vehicular ad-hoc network (VANET) is capable of offering a diverse set of services, and thus gains lot of attention from both academic and industrial communities. In VANET, the communicating links are prone to break easily; therefore, more attention is required to find reliable routes at a faster rate. In this context, information about connectivity and delay are valuable asset to establish and maintain a robust communication. In the proposed work, an Adaptive Intersection Selection Mechanism using Ant Colony Optimization (AISM), the problem of discovering a promising route subject to multiple Quality-of-service (QoS) constraint is emphasized. In previously reported literature, the best searched route cannot guarantee successful data packet transmission, even when it fulfills the QoS constrains at the time of discovery. To overcome this gap, AISM follows two simple strategies: Firstly, it exploits prediction-based mechanism for real time road evaluation; secondly, the route is formed between two consecutive intersections, instead of a long route between the network nodes. The connectivity and delay information obtained from small control packets called forward and backward ants are used to prioritize the candidate routes. Furthermore, by means of extensive simulation, outcomes in urban settings show that AISM outperforms the existing protocols in terms of packet delivery ratio, average delay and hop count.

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Authors and Affiliations

  1. Department of Electronics & Communication Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh, 211004, India

    Ankita Srivastava, Arun Prakash & Rajeev Tripathi

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  1. Ankita Srivastava
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  2. Arun Prakash
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  3. Rajeev Tripathi
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Correspondence to Ankita Srivastava.

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Srivastava, A., Prakash, A. & Tripathi, R. An adaptive intersection selection mechanism using ant Colony optimization for efficient data dissemination in urban VANET. Peer-to-Peer Netw. Appl. 13, 1375–1393 (2020). https://doi.org/10.1007/s12083-020-00892-8

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