Robust Predictive Routing for Internet of Vehicles Leveraging Both V2I and V2V Links

Abstract

With the developments of the Internet of Vehicles (IoV) from 4G to 5G, vehicle-to-infrastructure (V2I) communications are becoming attractive for vehicle users (VUEs) to obtain diverse cloud service through base stations (BSs). To tackle V2I link deterioration caused by blockage and out-of-coverage cases, multi-hop V2X routing with both vehicle-to-vehicle (V2V) and V2I links needs to be investigated. However, traditional routing reacts to statistical or real-time information, which may suffer link degradation during path switchover in fast-changing vehicular networks. Predictive routing protocols take timely actions by forecasting link connectivity, but they fail to satisfy specific QoS requirements. Low robustness to link failures is also incurred without considering imperfect prediction. To build continual paths between VUEs and BSs for QoS provision of cloud service, a robust predictive routing framework (ROPE) is proposed with three major components: 1) an early warning scheme detects V2I link deterioration in advance via predicting vehicle mobility and link signal strength to facilitate seamless path switchover; 2) a virtual routing mechanism finds top3 paths that have the highest path strength and satisfy the connectivity and hop count constraints based on the prediction results to fulfill QoS requirements of cloud service; 3) a path verification protocol checks availability and quality of the top3 paths shortly before switchover and activates one qualified path for switchover to ensure routing robustness. We implement ROPE in a simulation framework incorporating real-world urban maps, microscopic traffic generation, geometry-based channel modeling, and offline data analysis as well as online inference. Extensive simulations demonstrate the superiority of ROPE over direct V2I communications and a connectivity-based predictive routing protocol under various scenarios.