Can Edge Computing fulfill the requirements of automated vehicular services using 5G network ?
Wendlasida Ouedraogo, Andrea Araldo, Badii Jouaber, Hind Castel, Remy Grunblatt
TL;DR
This work assesses whether Multi-Access Edge Computing (MEC) can meet the stringent latency and reliability requirements of Connected and Automated Vehicles (CAVs) in 5G networks. It combines a queueing-theory-based lower-bound analysis with a comprehensive simulation campaign using OMNeT++, Simu5G, Veins, SUMO, and OpenStreetMap to estimate the MEC capacity needed per service and how many vehicles a single MEC node can support. The results reveal strong service dependence: high-demand services like remote driving and cooperative sensing require substantial MEC resources and support only a few vehicles, while cooperative maneuver and cooperative awareness are more scalable, though overall scalability is impeded by uplink/downlink delays and bandwidth sharing. The findings provide preliminary deployment guidance for operators and highlight the need to consider full edge-cloud-vehicle hierarchies in future evaluations.
Abstract
Communication and computation services supporting Connected and Automated Vehicles (CAVs) are characterized by stringent requirements, in terms of response time and reliability. Fulfilling these requirements is crucial for ensuring road safety and traffic optimization. The conceptually simple solution of hosting these services in the vehicles increases their cost (mainly due to the installation and maintenance of computation infrastructure) and may drain their battery excessively. Such disadvantages can be tackled via Multi-Access Edge Computing (MEC), consisting in deploying computation capability in network nodes deployed close to the devices (vehicles in this case), such as to satisfy the stringent CAV requirements. However, it is not yet clear under which conditions MEC can support CAV requirements and for which services. To shed light on this question, we conduct a simulation campaign using well-known open-source simulation tools, namely OMNeT++, Simu5G, Veins, INET, and SUMO. We are thus able to provide a reality check on MEC for CAV, pinpointing what are the computation capacities that must be installed in the MEC, to support the different services, and the amount of vehicles that a single MEC node can support. We find that such parameters must vary a lot, depending on the service considered. This study can serve as a preliminary basis for network operators to plan future deployment of MEC to support CAV.