Cooperative Edge Caching Strategy Based on Mobile Prediction and Social-Aware in Internet of Vehicles

Abstract

AbstractThe explosion of traffic caused by the rapid growth of multimedia services of Internet of Vehicles (IoV) has brought heavy load to mobile networks. The edge caching of the Internet of vehicles is considered as a promising technology. When the existing content caching strategy is used in the vehicle network, it faces the challenge of high content caching delay caused by the high-speed mobility of vehicle users and insufficient social relations. To address these challenges, this paper proposes a Cooperative Edge Caching Scheme based on Mobility Prediction and Society Aware (CCMPSA). In this strategy, the Long Short-Term Memory (LSTM) network is used to predict the location of the vehicle at the next moment, the vehicle cache nodes are selected according to the social relationship reflected by the similarity of interest and communication probability between the vehicles, and the dynamic decision of the content cache problem is realized by deep reinforcement learning. The simulation results show that the performance of the proposed strategy is better than random caching and non-cooperative caching algorithms, and it not only reduces the content transmission delay and improves the cache hit ratio, but also improves the experience quality of the whole system.KeywordsCaching decisionsDeep reinforcement learningThe edge cachingThe long short-term memory networkInternet of Vehicles