Enhanced Augmented Reality Applications in Vehicle-to-Edge Networks
Pengyuan Zhou, Wenxiao Zhang, Tristan Braud, Pan Hui, Jussi Kangasharju · 2019
Vehicular communication applications, be it for driver-assisting augmented reality systems or fully driverless vehicles, 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. In this paper, we present EARVE, a novel Vehicle-to-Edge infrastructure, with computational units co-located with the base stations and aggregation points. Embedding computation at the edge of the network allows to reduce the overall latency compared to vehicle-to-cloud and significantly trim the complexity of vehicle-to-vehicle communication. We present the design of EARVE and its deployment on edge servers. We implement EARVE through a bandwidth-hungry, latency constrained real-life application. We show that EARVE reduces the latency by up to 20% and the bandwidth at the server by 98% compared to cloud solutions at city scale.