Federated Learning Based Resource Allocation in Internet of Vehicles

点击翻译按钮获取中文摘要

Internet of Vehicles (IoVs) is a network derived from an intelligent transportation system, which allows on-road vehicles to exchange vehicle information through a wireless ad hoc network. High-performance IoVs can massively contribute to road traffic efficiency, reduce accidents, enable quality infotainment systems, and more. However, resources (e.g., networking, energy) supporting high-performance IoVs are limited, forming a critical challenge for high-performance IoVs. Meanwhile, the amount of information collected and exchanged by IoVs is massive, say 1GB per second. This PhD research will hence focus on advanced allocation and utilisation strategies for various resources required by IoVs, considering the impact of vehicle mobility while maintaining the quality of services.The project will investigate a wide range of modern technologies, including machine learning, energy harvesting, unmanned aerial vehicles, satellite networks, etc., to maximise resource allocations and utilisation for IoVs. Several work packages have been planned to carry out the study of a set of theoretical optimisations, efficient algorithms/protocols, and comprehensive experimental evaluations. The aims are to make useful contributions to emerging IoV applications so that such applications will be more environmentally friendly and enhancing of users' driving experience.