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Fog computing has been advocated as an enabling technology forcomputationally intensive services in smart connected vehicles. Most existingworks focus on analyzing the queueing and workload processing latenciesassociated with fog computing, ignoring the fact that wireless access latencycan sometimes dominate the overall latency. This motivates the work in thispaper, where we report on a five-month measurement study of the wireless accesslatency between connected vehicles and a fog/cloud computing system supportedby commercially available LTE networks. We propose AdaptiveFog, a novelframework for autonomous and dynamic switching between different LTE networksthat implement a fog/cloud infrastructure. AdaptiveFog's main objective is tomaximize the service confidence level, defined as the probability that thelatency of a given service type is below some threshold. To quantify theperformance gap between different LTE networks, we introduce a novelstatistical distance metric, called weighted Kantorovich-Rubinstein (K-R)distance. Two scenarios based on finite- and infinite-horizon optimization ofshort-term and long-term confidence are investigated. For each scenario, asimple threshold policy based on weighted K-R distance is proposed and provedto maximize the latency confidence for smart vehicles. Extensive analysis andsimulations are performed based on our latency measurements. Our results showthat AdaptiveFog achieves around 30% to 50% improvement in the confidencelevels of fog and cloud latencies, respectively.

AdaptiveFog: A Modelling and Optimization Framework for Fog Computing in Intelligent Transportation Systems