Cutting-edge advances in wireless networking will soon enable a new generation of safer, smarter, and more autonomous vehicles. These vehicles will rely on real-time execution of complex Deep Learning (DL) tasks as well as high-speed multimedia streaming between road users for navigation purposes. Relying entirely on cellular networks (i) puts an unnecessary burden on an already overcrowded and expensive licensed spectrum; (ii) increases the latency of edge-offloaded tasks to intolerable levels for vehicular applications. Alongside the usage of a proper network infrastructure, vehicles will need to support on-board and offloaded cooperative intelligence. On this basis, we propose Edge-V , the first framework enabling practical vehicular edge intelligence and high-speed vehicular connectivity, using only unlicensed spectrum bands. Through a DSRC link, Edge-V acquires real-time localized knowledge, and coordinates the use of point-to-point millimeter Wave (mmWave) technologies to deliver high-bandwidth connectivity between vehicles. Edge-V also foresees smart offloading if on-board computing resources are insufficient. We prototype and evaluate Edge-V in a real-world laboratory testbed, showing its advantages with respect to cellular and cloud-based approaches.


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    Title :

    Edge-V : Enabling Vehicular Edge Intelligence in Unlicensed Spectrum Bands


    Contributors:


    Publication date :

    2023-06-01


    Size :

    1841437 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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