FMS: Enhancing Fleet Management Scheme with Long Term Low-Latency V2X Services and Edge-based Video Stream Analytics

Abstract

V2X (Vehicle-to-everything) communication has garnered much attention in propelling the Internet of Vehicles (IoV) to seek shelter for many mission-critical edge-based applications in Intelligent Transportation Systems (ITS). The goal of achieving end-to-end latency (E2E) for on-road video analytics has become an essential critique to ensure the timely realization of computation-intensive tasks. By adopting the edge services (ES) along with the deployment of better application configuration, the co-optimization of video analytics accuracy and E2E latency can be achieved. However, there are certain challenges to this, such as poor application configuration, variable network conditions, erratic movement of the vehicles, which compromise the E2E latency, and passive strategies of congestion control that fail to avoid the oversubscription of the available bandwidth. To address the key challenges discussed, we propose a Fleet management scheme (FMS), a traffic video stream orchestrator in this work, which introduces Synergetic Service placement and Cost Minimization algorithm (SSPCM) to provision accurate streaming analytics. SSPCM is solved based on Lyapunov optimization and operates online without needing future information, and attains a verifiable performance bound on the Long-term low-latency (LTLL) constraint violation. Extensive evaluations using realistic data reveal the superior performance of the proposed scheme in balancing accuracy with E2E latency.