On the Prediction Policy for Timely Status Updates in Space-Air-Ground Integrated Transportation Systems

In this paper, we investigate the timeliness of the vehicular status updates in space-air-ground integrated networks (SAGIN) for intelligent transportation systems (ITS). The Age of Information (AoI) is introduced to capture the timeliness of the vehicular status updates. To overcome the inherent end-to-end latency taken by the long-distance communications in SAGIN for ITS, prediction has attracted extensive attention in the existing literature and shown its superiority. Nevertheless, it is not clear whether prediction is beneficial to the AoI. Inspired by the motivation, we first formulate a model of a real-time vehicular communication link with prediction, where the generated update can be predicted and transmitted to the receiver in advance. Then, we derive the explicit expression of the average age and show that the prediction is not always beneficial to the AoI. Instead, prediction is more applicable for the short-distance communications than long-distance communications. Further, to improve the AoI performance, a MDP framework is presented to obtain a switching structure of the optimal prediction policy. The results show the advantage of the optimal prediction policy over the policy of predicting all the time or with no predicting.