Intelligent Mobile Edge Caching for Popular Contents in Vehicular Cloud Toward 6G

Abstract

Although vehicles are the most commonly used means of transportation in our daily lives, their computational power and memory resources are often ignored. The new paradigm, Internet of Vehicles, has turned on-road vehicles into intelligent service providers, making full use of on-board processing and storage capabilities. With the advent of 6 G technology, e.g., millimeter wave and terahertz, fast and massive data transfers become possible between moving objects. In this paper, we consider a scenario where vehicles act as mobile edge caching nodes and form a local cloud, to provide popular contents for nearby users via 6 G communication. To maximize the efficiency of content allocation under the limitation of short communication range, we first predict the driving trajectories of vehicles with a Markov chain model and estimate the popularity level of contents in each urban area. Next, we design a scoring system based on the above predictions to compare users' satisfaction and computational efficiency of different algorithms. Finally, we find a subset of vehicles with appropriate content to serve the demand in the area. The simulation experiments demonstrate that the proposed scheme outperforms the conventional methods by providing a cost-efficient content allocation solution with lower delay.