Abstract
The accelerating deployment of vehicular networks in smart cities sparked a demand for a wider diversity of on-board applications, extending their performance requirements. Higher computation needs and stricter delay constraints provide new challenges for edge computing vehicular architectures, especially regarding urgent data and high-priority tasks. In that regard, QoS-provisioning appears as imperative, along with optimized 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. Three concurrent mechanisms are at play: traffic classification, CAM-beacon-enhanced location awareness and long-range V2N resource polling. Three-tier traffic classification identifies task priority, while location and resource awareness participate in the selection of the most appropriate offloading destination depending on these priorities. The resource awareness mechanism is developed in two phases: immediate available resources and prospective resources based on edge server queue state. Experimental results corroborate the gains of our scheme compared to a standard offloading scheme, especially for high-priority tasks, with success rates increased by up to 14%, and offloading delays reduced by up to 24%.
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