Mobility-Aware and Interest-Predicted Caching Strategy Based on IoT Data Freshness in D2D Networks

The Internet of Things (IoT) will generate a large amount of data and the desired data are similar for users in particular regions. In these situations, data can be shared by D2D communication, which can significantly ease the traffic on BSs and effectively reduce the load. In this article, we propose an optimization algorithm for solving the joint problem of file caching and updating aimed at the D2D content-sharing scenario in the IoT. First, we construct a user-mobility model based on a Markov chain and a user-interest prediction model based on social proximity, user preference, and freshness. Then, we formulate the mobility-aware, freshness-based, and user-interest predicted optimization problem as a 0-1 multiple Knapsack problem, which is decomposed into two subproblems: 1) a cache problem and 2) an update problem. We prove that the cache problem's optimization objective is of the monotone submodular function over one matroid and multiple Knapsack constraints categories, while the update problem's optimization objective is a monotone decreasing function. The simulation results confirm that the optimization algorithm proposed in our article predicts the interests of users more accurately, improves the caching hit probability of files effectively, and maximizes utility for IoT network users.