LAUSR

With the development of wireless communication technology, the amount of data in the Internet of Things has increased rapidly, and the application mode has become ubiquitous. The content sharing mode has the characteristics of diversified services, diversified contents, and diversified scenarios. Content sharing from device to device (D2D) can cope with the increasing traffic pressure on cellular networks. Users can use mobile devices to transmit and share content across spaces by opportunities. However, in the content sharing process, the available cache space for users is limited, and content may delay delivery in searching cooperative sharing users. Researching effective content sharing and forwarding algorithms can improve such a transmission environment. In the content sharing process in the mobile Internet of Things, users can analyze and judge the surrounding fields according to the shared content attributes and personalized preferences and search for suitable sharing targets. This paper proposes a content sharing algorithm based on dynamic behavior and collaborative prediction (DBCPNF). It establishes a user preference model based on the user’s historical behavior, cooperation users, and content attributes. By calculating the matching degree between content attributes and user preferences, users with matching higher are adopted as target users to share content cooperatively. Through experimental analysis and comparison with other algorithms, our algorithm has the best performance on the content delivery ratio and improves the network’s overall efficiency.