Cloudlet Dwell Time Model and Resource Availability for Vehicular Fog Computing

Urban computing has become a significant driver in supporting the delivery and sharing of services, being a strong ally to intelligent transportation. In an intelligent transportation scenario, smart vehicles present computing and communication capabilities that enable many autonomous vehicular safety and infotainment applications. Vehicular Fog computing appears as a new paradigm in enabling and facilitating efficient service and resource sharing in urban environments. Several vehicu-1ar resource management works have attempted to deal with the highly dynamic vehicular environment following diverse approaches, such as MDP, SMDP and policy-based greedy techniques. However, the high vehicular mobility causes several challenges compromising consistency and efficiency. RL-enabled adaptive vehicular Fogs can deal with the mobility for properly distributing load and resources over Fogs. Thus, we propose a mobility-based dwell time estimation method for accurately estimating vehicular resources in a Fog, leveraging the design of an adaptive and highly dynamic resource allocation model.