Cooperative Caching Strategy With Content Request Prediction in Internet of Vehicles

Abstract

In order to mitigate the impact of explosively increasing data traffic on content request services in the Internet of Vehicles (IoV), edge caching technology is implemented in IoV to accelerate the response process of content requests and release the backhaul burden of the base station. However, the content popularity obtained by the traditional content popularity method cannot capture the requests of vehicles accurately due to the time-varying characteristics of the content popularity, which results in a relatively low cache hit ratio. Thus, this article proposes a cooperative caching strategy with content request prediction (CCCRP) in IoV, which precaches the contents requested by vehicles with greater probability in other vehicles or the roadside unit (RSU) to reduce the content acquisition delay. Specifically, vehicles are first clustered using the K-means method to simplify the process of vehicle requesting and content transmission. Then, content requests from vehicles are predicted using the long short-term memory (LSTM) networks according to the historical content request information. Finally, reinforcement learning method is adopted to solve the objective function to obtain the optimal caching decision, which improves the Quality of Service (QoS) of vehicle requests. Simulation results demonstrate that CCCRP can improve the cache hit ratio and reduce content acquisition delay effectively. For example, the cache hit ratio of CCCRP can be increased by 5% and 7% compared to the traditional LFU and LRU caching strategies when the Zipf parameter equals 0.7, respectively.