The effect of vehicle route uncertainty in green roadside communication

This paper addresses the problem of scheduling transmission requests in vehicular networks so that long-term road side unit (RSU) energy costs are minimized. We demonstrate that knowledge of vehicular routes greatly improves the energy service costs and request drop ratio of RSU transmission. At the same time, simple and fast prediction algorithms can recover a significant portion of the loss incurred by a lack of vehicle route knowledge. The proposed algorithms use recent historical traffic data and simple calculations, such as Bayesian estimates, to predict the next few routing decisions by a vehicle, in order to load balance the scheduling of its requests over the RSU network. Our simulation results show that, while the common assumption in the literature of knowing the vehicle routes is indeed crucial for achieving good performance, simple algorithms can be used in cases where vehicle routes are not known ahead of time, in order to achieve comparable costs and loss ratios.