Presenting the paper which is green strategy to reduce the network traffic congestion is an important step toward improvement for VANET infrastructure as we consider the dynamically changeable infrastructure of the RSU. With the latest technique of ad hoc network routing in computing generation and communications protocols, we are able to retrieve any form of sensors data and get maintain off in actual-time vehicles traffic congestion at every location of roadside using GPS. This routing technique observes and introduces a latest algorithms that goals to optimize the route selection of Internet site online traffic road congestion in actual-time primarily based mostly on the ant colony optimization algorithm and VANET communication system. The VANET is used as a communication era a good way to exchanging the routing table information among several vehicles and routes. The principle of the ACO is used to compute the shortest routing path that can be observed by way of the using force to handle the congested path. The proposed protocol is based totally on a multi-hop count scenario, in which all region CHs routers work collectively to expose the street and highway traffic congestion and assist the drivers to fast arrive at their destination locations through the use of the tremendous routes with less congestion. Simulation results evaluate that the proposed method can consumes very less time to cover the whole distance traveled from source to destination with optimized path, while compared to the traditional “shortest distance covering technique”.


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    Titel :

    Study of Decreasing Traffic Routing System Using VANET Hybrid Ant Colony Optimization


    Weitere Titelangaben:

    Algorithms for Intelligent Systems


    Beteiligte:
    Kumar, Sandeep (Herausgeber:in) / Hiranwal, Saroj (Herausgeber:in) / Purohit, S. D. (Herausgeber:in) / Prasad, Mukesh (Herausgeber:in) / Solanki, Prithviraj Singh (Autor:in) / Joshi, Ganpat (Autor:in)


    Erscheinungsdatum :

    27.09.2022


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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