Dynamic Content Cache Strategy Based on Content Prediction in the Internet of Vehicles

The rapid development of Internet of Vehicle(IoV) technology has brought the improvement of user experience satisfaction. Subsequently, a variety of vehicle applications put forward higher requirements for information transmission and storage space. A new intelligent edge content caching mechanism is proposed to adapt to the dynamic change of vehicles and differences of storage space of existing facilities. Firstly, based on Long Short Term Memory(LSTM), a resource request prediction model is proposed to effectively estimate the number of content request over a period of time. Then, considering the popularity of the requested content and the preferences of different vehicle users, this paper proposes Dynamic content cache algorithm(DCCA). Among them, the model is built through Markov Decision Process(MDP) to update and optimize the request content using Double Deep Q-Network(DDQN). This experiment shows that DCCA is able to improve the hit rate by 50% and reduce the average delay by 20ms in the face of complex and varied request contents with limited cache capacity.