Diversity Maximized Scheduling in RoadSide Units for Traffic Monitoring Applications

TL;DR

This paper proposes an optimized scheduling and data aggregation method for traffic monitoring using Road Side Units, enhancing data diversity and learning accuracy under communication constraints.

Contribution

It introduces a greedy, coalition game-based scheduling algorithm that maximizes data diversity and fairness for traffic classification tasks in RSU networks.

Findings

Outperforms random and uniform data selection methods.

Achieves higher classification accuracy with optimized scheduling.

Effectively manages heterogeneous communication conditions.

Abstract

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 (e.g., delay, error rate) and constrained total transmission rate. As a proof-of-concept, we enforce fairness among class labels to increase data diversity for classification problems. The developed constrained optimization problem is non-convex. Hence it does not admit a closed-form solution, and the exhaustive search is NP-hard in the number of RSUs. To this end, we propose an approximate algorithm that applies a greedy interval-by-interval scheduling policy by selecting RSUs to transmit. We use coalition game formulation to maximize the overall added fairness by the selected RSUs in each transmission interval. Once, RSUs are selected, we employ a maximum uncertainty method to handpick data samples that contribute the most to the learning performance. Our method outperforms random selection, uniform selection, and pure network-based optimization methods (e.g., FedCS) in terms of the ultimate accuracy of the target learning application.