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Full title—Edge-V: Vehicular Edge Intelligence Through Multi-Band Unlicensed Spectrum Access
Technological advances in the automotive field are driving the development of smarter, greener, and more autonomous vehicles. These vehicles will need to communicate via Vehicle-to-Everything (V2X) wireless communications and perform advanced Deep Learning (DL) tasks while handling large data volumes with low latency and high reliability.
Although 5G is frequently viewed as a comprehensive solution for addressing the demanding environment of next-generation autonomous vehicles and of Vehicular Edge Intelligence (VEI), relying solely on cellular networks poses challenges like spectrum congestion, delays in edge offloading, and poor coverage in certain areas. Current unlicensed spectrum technologies also fall short of the VEI requirements.
On this basis, we propose Edge-V, a novel framework combining unlicensed spectrum technologies to provide low-latency, high-throughput connectivity with reliable task offloading. Edge-V uses a Dedicated Short-Range Communications (DSRC) link for exchanging standardized messages, traditional Wi-Fi for connecting on-board devices and sensors, and mmWave for high-speed, low-latency connectivity. With the aim of optimally allocating tasks, an Offloading Manager module is included, based on a system model which is mathematically formulated, and used to propose a sample greedy strategy within Edge-V.
Our laboratory and field tests, thanks to an open and low-cost Proof-of-Concept, show that Edge-V can reduce latency by up to 65% when compared to cellular/cloud-based solutions.
Full Article: IEEE Transactions on Vehicular Technology, Early Access
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