Context-aware task offloading with QoS-provisioning for MEC multi-RAT vehicular networks

The next step towards vehicular networks in smart cities would be the deployment of autonomous shuttles with multiple on-board applications. Their need to offload task towards Road Side Units (RSUs) is inevitable, especially with a certain proportion of urgent data needing to be processed in the shortest possible delay. Therefore, QoS-provisioning appears as imperative, along with the optimization of resource requesting at RSUs. To this end, we propose CAVTOMEC, a multi-RAT location-aware, context-aware task offloading solution with QoS provisioning for MEC vehicular networks. Our solution consists of three intertwined mechanisms: traffic classification, location-awareness exploiting the contents of CAM beacons and V2N-enhanced resource polling. Traffic classification identifies high, low, and indifferent task priorities, while location and resource awareness help to select the most appropriate RSU to offload tasks to depending on these priorities. Performance evaluations show that our proposal offers better load balancing at the RSUs than traditional offloading schemes, thus satisfying high priority task offloading at better rates and freeing up more resources in case an unexpected event occurs.