Edge-facilitated Augmented Vision in Vehicle-to-Everything Networks

New publication in IEEE Transactions on Vehicular Technology: Pengyuan Zhou, Tristan Braud, Aleksandr Zavodovski, Zhi Liu, Xianfu Chen, Pan Hui, Jussi Kangasharju. "Edge-facilitated Augmented Vision in Vehicle-to-Everything Networks"

Vehicular communication applications require an efficient communication architecture for timely information delivery. Centralized, cloud-based infrastructures present latencies too high to satisfy the requirements of emergency information processing and transmission, while Vehicle-to-Vehicle communication is too variable for reliable in-time information transmission. In this paper, we present EAVVE, a novel Vehicle-to-Everything system, consisting of vehicles with and without comprehensive data processing capabilities, facilitated by edge servers co-located with roadside units. Adding computation capabilities at the edge of the network allows reducing the overall latency compared to vehicle-to-cloud and makes up for scenarios in which in-vehicle computational power is not sufficient to satisfy the service demand. To improve the offloading efficiency, we propose a decentralized algorithm for real-time task scheduling and a client/server algorithm for information filtering. We demonstrate the practical applications of EAVVE with a bandwidth-hungry, latency constrained real-life prototype system that connects vehicular vision through Augmented Reality vision. We evaluate this prototype system with real-life road tests. We complement this practical evaluation with extensive simulations based on real-world base station and vehicular traffic data to demonstrate the scalability of EAVVE and its performance in citywide scenarios. EAVVE decreases the latency by 42.6% and 78.7% compared to local and remote cloud solutions while relaxing congestion at the bottleneck by 99% with reasonable infrastructure expenditure.