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Exploring data connection information from vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications using advanced machine learning approaches, an intelligent transportation system (ITS) can provide better safety services to mitigate the risk of road accidents and improve traffic efficiency. In this work, we propose an end-edge-cloud architecture to deploy machine learning-driven approaches at network edges to predict vehicles’ future trajectories, which is further utilized to provide an effective safety message dissemination scheme. With our approach, the traffic safety message will only be disseminated to relevant vehicles that are predicted to pass by accident areas, which can significantly reduce the network data transmission overhead and avoid unnecessary interference. Depending on the vehicle connectivity, our system adaptively chooses vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communications to disseminate safety messages. We evaluate the system by using a real-world VANET mobility dataset, and experimental results show that our system outperforms other mechanisms without considering any predicted vehicle trajectory density information.

This paper presents a new video delivery scheme in mobile networks using Multi-Access Edge Computing (MEC). Our goal is to improve the quality of video streaming experienced by the mobile video consumer. Our approach is based upon Dynamic Adaptive Streaming over HTTP. We present a novel algorithm, which uses information obtained from the Radio Network Information Service of MEC to provide the mobile user with a video quality matching the current radio link quality and channel capacity. We evaluate our approach using a real experiment performed on a Long Term Evolution (LTE) femto cell test-bed. Our algorithm displays enhanced adaptation of video rates in comparison to other state of the art solutions.