Proactive Content Caching Scheme in Urban Vehicular Networks

Stream media content caching is a key enabling technology to promote the value chain of future urban vehicular networks. Nevertheless, the high mobility of vehicles, intermittency of information transmissions, high dynamics of user requests, limited caching capacities and extreme complexity of business scenarios pose an enormous challenge to content caching and distribution in vehicular networks. To tackle this problem, this paper aims to design a novel edge-computing-enabled hierarchical cooperative caching framework. Firstly, we profoundly analyze the spatio-temporal correlation between the historical vehicle trajectory of user requests and construct the system model to predict the vehicle trajectory and content popularity, which lays a foundation for mobility-aware content caching and dispatching. Meanwhile, we probe into privacy protection strategies to realize privacy-preserved prediction model. Furthermore, based on trajectory and popular content prediction results, content caching strategy is studied, and adaptive and dynamic resource management schemes are proposed for hierarchical cooperative caching networks. Finally, simulations are provided to verify the superiority of our proposed scheme and algorithms. It shows that the proposed algorithms effectively improve the performance of the considered system in terms of hit ratio and average delay, and narrow the gap to the optimal caching scheme comparing with the traditional schemes.

Automated summary

This paper aims to design a novel edge-computing-enabled hierarchical cooperative caching framework to tackle the challenges posed by the high mobility of vehicles, intermittency of information transmissions, high dynamics of user requests, limited caching capacities, and extreme complexity of business scenarios in vehicular networks. By analyzing the spatio-temporal correlation between the historical vehicle trajectory of user requests and content popularity, the system model is constructed to predict the vehicle trajectory and content popularity, which lays a foundation for mobility-aware content caching and dispatching. Privacy protection strategies are also studied to realize a privacy-preserved prediction model. Based on trajectory and popular content prediction results, content caching strategy and adaptive and dynamic resource management schemes are proposed for hierarchical cooperative caching networks. Simulations are provided to verify the effectiveness of the proposed scheme and algorithms in improving the performance of the considered system.