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 (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

Performance analysis of communication model on position based routing protocol: Review analysis

Research on the Vanet system has its own challenges and obstacles with the communication system between nodes being the main issue. Four categories in the Vanet system topology, namely position based routing protocols, broadcast based routing protocols, cluster based routing protocols and multicast/geocast routing protocols, have fundamental differences, especially in the concept of sending data and information between nodes. For this reason, in this study, the selection of standardization and integration of data delivery between nodes is of particular relevance. The ability to send data properly in busy and fast traffic conditions is another challenge. For this, there are many variables that must be considered to improve communication between nodes