A Mobility Prediction-Based Relay Cluster Strategy for Content Delivery in Urban Vehicular Networks

• Main Authors: Shaoqi Yue, Qi Zhu
• Format: Article
• Language: English
• Published: MDPI AG 2021-02-01
• Series: Applied Sciences
• Subjects: vehicular networks; relay; content chunks; content delivery; prediction trajectory; cluster
• Online Access: https://www.mdpi.com/2076-3417/11/5/2157

In recent years, cache-enabled vehicles have been introduced to improve the efficiency of content delivery in vehicular networks. However, because of the high dynamic of network topology, it is a big challenge to increase the success probability of content delivery. In this paper, we propose a relay strategy based on cluster’s prediction trajectory for the situation of no cache near the request vehicles. In our strategy, the roadside unit (RSU) divides vehicles into clusters by their prediction trajectory, and then proactively caches contents at a cluster that will be about to meet the request vehicle. In order to decrease the probability of unsuccessful content delivery caused by communication duration that is too short between the request vehicle and content source vehicle, RSU caches content chunks at multiple vehicles in a cluster. By letting the request vehicle communicate with vehicle-caching content chunks one by one, our strategy enlarges the communication duration and increases the success probability. Our strategy also maximizes the success probability by optimizing the number of vehicles selected to cache content chunks. Besides, based on statistical characteristics of vehicles’ speed, we derive the formula of success probability of content delivery. The simulation results show that our strategy can increase the success probability of content delivery, as well as decrease time delay, for example. For example, , we increase the success probability by about 20%. Since the trajectory prediction-based cluster-dividing mechanism can improve clusters’ stability at intersections, this method is well suited for urban road scenarios.