Vehicle ad-hoc networks have grown in relevance as vehicle intelligence and road infrastructure have advanced, owing to their ability to promote improved transportation efficiency and traffic management. A mobile ad-hoc network (MANET) occurs when some or all of the nodes in a network move; a vehicular ad-hoc network (VANET) is a subset of MANET in which certain nodes are attached to cars on the road and are thus subject to the same mobility constraints as road traffic. The successor of MANETs, VANETs affirm few indistinguishable features, but are extremely variable in accordance with the type of node, its communication ability, mobility, speed, direction, and the intended pattern of vehicle movement. Wireless communications technologies such as WLANs, 3G, 4G, and 5G cellular systems, among others, have accelerated the development of intelligent transportation systems (ITS), which strengthens the installation and deployment of VANETs and provides dependable services to its customers, as a primary element in the implementation of smart cities and ITS in the Indian scenario. In this research work, we are analyzing the performance of designed VANET scenario on specified 166 vehicles for a 30 s simulated scenario. The performance metrics, basic safety messages (BSM) packet delivery ratio (PDR), average goodput, and MAC/PHY overhead, are analyzed over four VANET routing protocols—ad-hoc on-demand vector (AODV), destination sequenced distance vector (DSDV), dynamic source routing (DSR), and optimized link state routing (OLSR) in a defined area. The results showed improvement in DSR and OLSR for goodput, overhead, and packet delivery ratio.


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    Titel :

    Analysis of Densed Vehicular Ad-Hoc Networks Using NS3 and SUMO


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Tomar, Anuradha (Herausgeber:in) / Mishra, Sukumar (Herausgeber:in) / Sood, Y. R. (Herausgeber:in) / Kumar, Pramod (Herausgeber:in) / Kour, Satveer (Autor:in) / Singh, Manjit (Autor:in) / Sarangal, Himali (Autor:in) / Singh, Butta (Autor:in)

    Kongress:

    International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication ; 2023 ; Saharanpur, India November 28, 2023 - November 29, 2023



    Erscheinungsdatum :

    23.12.2024


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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