期刊
IEEE INTERNET OF THINGS JOURNAL
卷 8, 期 7, 页码 6024-6038出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.3033552
关键词
Device-to-device communication; Optimization; Internet of Things; Predictive models; Base stations; Prediction algorithms; Delays; D2D caching; data freshness; interest prediction; Internet of Things (IoT); mobility; partition matroid; submodular function
资金
- National Natural Science Foundation of China [61971239, 61631020]
This article presents an optimization algorithm for the joint problem of file caching and updating in the IoT D2D content-sharing scenario. By constructing user mobility and interest prediction models, the optimization problem is formulated as a 0-1 multiple Knapsack problem, decomposed into cache and update subproblems, with optimization objectives and function characteristics demonstrated.
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.
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