Diversity Maximized Scheduling in RoadSide Units for Traffic Monitoring Applications

This paper develops an optimal data aggregation policy for learning-based traffic control systems based on imagery collected from Road Side Units (RSUs) under imperfect communications. Our focus is optimizing semantic information flow from RSUs to a nearby edge server or cloud-based processing units by maximizing data diversity based on the target machine learning application while taking into account heterogeneous channel conditions and constrained total transmission rate. To this end, we enforce fairness among class labels to increase data diversity for classification problems. Furthermore, we propose a greedy interval-by-interval scheduling policy powered by coalition game theory to reduce the computation complexity. Once, RSUs are selected, we employ a maximum uncertainty method to handpick data samples that contribute the most to the learning performance. Our method yields higher learning accuracy compared to random selection, uniform selection, and network-based optimization methods (e.g., FedCS)
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