Abstract
Bandwidth is a valuable and scarce resource in mobile networks. Therefore, bandwidth reservation may become necessary to support time-sensitive and safety-critical networked vehicular applications such as autonomous driving. Such applications require individual and deterministic approaches for reservations. This is challenging as vehicles usually have insufficient information to reason about future driving paths as well as future network resources availability and costs. In particular, the optimal time for a vehicle to place a cost-efficient reservation request is crucial. If a reservation is conducted too early, the uncertainty in path prediction may become high resulting in frequent cancellations with high costs. If a reservation is requested too late, resources may no longer be available. In this paper, we study the optimal timing for a given vehicle to place a bandwidth reservation request for an upcoming trip. Our proposal is based on predicting bandwidth costs using well-selected temporal machine learning techniques while achieving high accuracy levels. The proposed reservation scheme relies on a corpus of real-world traffic data. The experimental results prove that the model can effectively learn to find an optimized timing for bandwidth reservation. In addition, our model may allow vehicles to save considerably costs compared to the baseline of an immediate reservation scheme.