隨著物聯網設備和行動裝置數量的增加，這些裝置所產生的資料流量造成高度網路負載，而且這些裝置與雲端通訊時，必須忍受很長的存取延遲。邊緣計算的架構提出後，在網路的邊緣執行快取與預取機制可以解決這些問題。然而，邊緣計算快取與預取機制的相關研究中，大多是針對多媒體資料。因此，我們在階層式邊緣計算架構中，提出物聯網資料的快取與預取機制，而此篇論文的目標為，降低使用者存取的延遲。在我們的架構中，快取設置於第一層及第二層邊端節點上，而快取機制則是特別為物聯網資料所設計。此外，我們分析所有使用者存取要求的關係，並根據使用者喜好的預測結果，預取在所有使用者間受歡迎的資料至快取中。其中，預測是根據資料型態關係、過去資料趨勢和現在資料趨勢的規則集，這些規則集會進一步分別用於被動式預取、主動式預取和發布-訂閱式預取。實驗結果顯示，我們所提出的機制可以有效地減少使用者存取的延遲。

With the growing number of the Internet of Things devices and mobile devices, the data traffic produced by these devices causes high network load, and these devices endure long access latencies to communicate with the cloud. The emergence of Edge Computing, caching and prefetching at the edge of the networks are promising solutions to these problems. However, previous researches on caching and prefetching in Edge Computing mostly focus on multimedia data. Therefore, we propose caching and prefetching schemes for the Internet of Things data based on a four-tier hierarchical Edge Computing architecture, and our goal is to reduce user access latencies. In our architecture, caches are deployed at 1st- and 2nd-tier edge nodes, and the caching scheme is designed especially for the Internet of Things data. We analyze the relations of all users’ access requests and prefetch popular data among all users to the cache based on the predictions of user preferences. The predictions are done according to data-type relation ruleset, past-data trend ruleset, and current-data trend ruleset, and these rulesets are used for reactive prefetching, proactive prefetching, and publish-subscribe prefetching, respectively. The experimental results show that the proposed schemes can effectively reduce user access latencies.

I. Introduction....................................1
II. Related Work....................................5
III. Hierarchical Edge Computing Architecture........9
IV. IoT Data Caching and Prefetching Schemes........14
4.1 IoT Data Caching................................14
4.2 IoT Data Prefetching............................16
4.2.1 Characteristics of Users’ Requests......16
4.2.2 Rule Generation.........................18
4.2.3 Prefetching Scheme......................24
V. Performance Evaluation..........................28
5.1 System Architecture for Simulation..............28
5.2 Evaluation Setup................................29
5.3 Benchmark.......................................31
5.4 Performance Metric..............................32
5.5 Numerical Results...............................33
VI. Conclusion......................................43
References..............................................44

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