Internet of Vehicles (IoV) supported by terrestrial networks can satisfy the necessities of multiple computation-intensive applications. However, current terrestrial networks and resource management mechanisms may only partially guarantee vehicle and in-vehicle user equipment (VUE)’s quality of service due to the limited coverage of roadside units (RSU), especially in remote areas. This paper investigates vehicular edge computing (VEC) in satellite-terrestrial integrated networks with multiple low-earth orbit (LEO) satellites, ground RSUs, and VUEs. In remote areas without RSU coverage, VUEs can offload their partial tasks to satellites to save energy and guarantee latency. We aim to minimize VUEs’ weighted sum energy consumption by jointly optimizing VUEs’ association, data partition, computing resource allocation, power control, and bandwidth assignment under the constraints of maximum tolerant latency, maximum number of outage time slots, computation capacity at each satellite and each RSU, and maximum allowable transmission power at VUEs. Furthermore, we introduce an iterative algorithm by decomposing the original non-convex problem into several sub-problems. We efficiently solve each sub-problem by utilizing variable substitutions, the difference of convex functions algorithms, the Lagrangian dual method, and the Karush-Kuhn-Tucke conditions. Simulation results show that the introduced satellite-terrestrial integrated networks-enabled VEC scheme significantly reduces VUEs’ energy consumption compared to other schemes.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Vehicular Edge Computing in Satellite-Terrestrial Integrated Networks


    Beteiligte:
    Li, Caiguo (Autor:in) / Shang, Bodong (Autor:in) / Feng, Jie (Autor:in) / Liu, Lei (Autor:in) / Chen, Shanzhi (Autor:in)


    Erscheinungsdatum :

    01.04.2025


    Format / Umfang :

    1759209 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    LEO Satellite-Assisted Vehicular Edge Computing

    Li, Caiguo / Shang, Bodong / Feng, Jie et al. | IEEE | 2023


    Resource Management for Intelligent Vehicular Edge Computing Networks

    Duan, Wei / Gu, Xiaohui / Wen, Miaowen et al. | IEEE | 2022


    Collaborative Task Processing in Vehicular Edge Computing Networks

    Bai, Xiao / Chen, Shanzhi / Shi, Yan et al. | IEEE | 2021


    Intelligent Offloading Balance for Vehicular Edge Computing and Networks

    Wu, Yu / Fang, Xuming / Min, Geyong et al. | IEEE | 2025


    Cooperative Computation Offloading in Blockchain-Based Vehicular Edge Computing Networks

    Lang, Ping / Tian, Daxin / Duan, Xuting et al. | IEEE | 2022