The current paper therefore discusses the significance of effective task-offloading strategies especially in an urban environment due to the evolution of means to boost both service provision and customer satisfaction. More precisely, we look into issues and opportunities in offloading vehicular services in realistic urban conditions with more reference to cities of Saudi Arabia. The research explores the application of VEC through using MEC strategies in that helps to offload computationally intensive tasks from the vehicles to the edge servers minimising resource consumption. By considering some of the important factors such as movement of vehicles, network delay and load, we present a dynamic task-offloading model that seeks to reduce delay times and energy usage while at the same time offering high service quality. The efficiency of the model is established through CloudSim-SUMO simulations comparing actual and simulated traffic in the Dammam District. We have established constant connectedness and effective task execution even with a highly mobile vehicle environment. This work is useful in developing the smart transport systems of Saudi Arabia by adding solutions that improve vehicular services and solve issues associated with ITS.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Enhancing Vehicular Service Allocation with MEC Dynamic Task Offloading in a Dammam District Using CloudSim-SUMO


    Beteiligte:


    Erscheinungsdatum :

    15.01.2025


    Format / Umfang :

    732993 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Dynamic Task Offloading in Urban Saudi Arabia: Enhancing Vehicular Services through Edge Computing

    Barakat, Mohammed / Saeed, Rashid A. / Edam, Salaheldin | IEEE | 2024



    Joint Task Offloading and Resource Allocation for NOMA-Based Vehicular Networks

    Song, Yunfei / Gao, Yongqiang / He, Yipei | IEEE | 2024


    V2V-Based Task Offloading and Resource Allocation in Vehicular Edge Computing Networks

    He, Junjin / Wang, Yujie / Du, Xin et al. | ArXiv | 2021

    Freier Zugriff