An Adaptive Broadcasting Strategy for Efficient Dynamic Mapping in Vehicular Networks

In this work, we face the issue of achieving an efficient dynamic mapping in vehicular networking scenarios, i.e., obtaining an accurate estimate of the positions and trajectories of connected vehicles in a certain area. State-of-the-art solutions are based on the periodic broadcasting of the position information of the network nodes, with an inter-transmission period set by a congestion control scheme. However, the movements and maneuvers of vehicles can often be erratic, making transmitted data inaccurate or downright misleading. To address this problem, we propose to adopt a dynamic transmission scheme based on the actual positioning error, sending new data when the estimate overcomes a preset error threshold. Furthermore, the proposed method adapts the error threshold to the operational context according to an innovative congestion control algorithm that limits the collision probability among broadcast packet transmissions. This threshold-based strategy can reduce the network load by avoiding the transmission of redundant messages, and is shown to improve the overall positioning accuracy by more than 20% in realistic urban scenarios.