Learning-based Resource Management for Internet of Vehicles

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Internet of Vehicles (IoV) has been envisioned to enable next--generation mobile networks by offering many new technologies, which refers to dynamic mobile communication systems using V2V (vehicle--to-vehicle), V2I (vehicle-to--infrastructure), V2H (vehicle--to--human) and V2S (vehicle-to-sensor) interactions. Despite significant research efforts on resource management for IoV, a mature science to support real-time applications of high--confidence IoV is still missing, and traditional analysis tools/algorithms are unable to cope with the full complexity of IoV or adequately predict system behavior, due to great challenges that arise from the high mobility, dynamic changing environment and intrinsic heterogeneity of such systems. The long-term goal of this research is to advance the development of learning-based models and algorithms for allocating heterogeneous resources in IoV networks with real-time and low-complexity virtualized resource management. Ultimately, all research contributions and outcomes will be integrated into physical IoV networks and smart connected vehicle systems to further advance the engineering of such systems. The short-term objectives in the next five years are three-fold: 1) To model and characterize the impact of vehicle mobility prediction on resource management; 2) To develop a rigorous and systematic framework for designing dynamic learning-based resource management policies that can achieve outstanding performance in all the three dimensions of throughput optimality, on-time guarantee, and computational complexity; 3) To design efficient resource allocation algorithms considering various engineering costs and constraints. The proposed research includes three trusts: (1)Intra-cell resource management: each smart small-cell base station (SBS) allocates the spectrum resource to the surrounding vehicles, aiming at maximizing the throughput/utility of each cell based on the traffic prediction model powered by machine learning; (2)Network-wide resource management: adaptive and optimal cooperative resource management policies among SBSs in IoV networks will be developed by considering both traffic and spectrum access dynamics; (3)Application-level resource management:  to further improve the system performance in terms of key performance indicators, application data delivery, quality of service, and quality of experience, learning-based application-level resource management policies with network slicing will be developed in favor of real-time services for vehicles. It is anticipated that the proposed research program will generate significant scientific, technological, and social impacts in providing the scientific research foundation for supporting real--time applications in IoV. This program will also contribute to the training of highly qualified personnel, providing the trainees with solid knowledge and research background in R&D of IoV, and enabling them to contribute to Canadian high technology industry.