fernandovj / rlbased_traffic_reporting_vehicular_networks

Traffic data reporting mechanism based on Reinforcement Learning for reducing overhead in vehicular networks

RL-based traffic data reporting scheme for vehicular networks

Vehicles with OBUs can communicate with the roadside infrastructures (\textit{e.g.,} RSUs and 5G base station), edge servers and cloud (\textit{e.g.,} cellular communication and vehicular networking) to report traffic-related data and request/receive urban-related dynamics (\textit{e.g.}, traffic conditions). The vehicles need to sense the environment and frequently report traffic-related information to enable accurate traffic condition estimation and detect congestion. Therefore, if all vehicles try to report simultaneously, they can overload the network with redundant transmissions and degrade the overall system performance. Thus, the traffic data reporting mechanism needs to be optimized to reduce network overhead. This is am RL-based data reporting scheme aiming to minimize the network overhead when the vehicles report their traffic information.