Edge-Facilitated Augmented Vision in Vehicle-to-Everything Networks
Pengyuan Zhou, Tristan Braud, Aleksandr Zavodovski, Zhi Liu, Xianfu Chen, Pan Hui, Jussi Kangasharju
Abstract
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.