在傳統考勤及門禁控制器管理系統中，伺服器透過RS485直接連線或是網路方式連上門禁控制器。伺服器能遠端控制門禁控制器，也能將使用者通行資料(RFID卡片號碼和可通行時段資料)儲存在門禁控制器之中，在RFID感應時能決定是否開門，並且能定期從門禁控制器之中取得感應RFID的事件資料，而且伺服器能利用事件資料與使用者資料計算上下班時間、出缺勤、薪水計算達到完成考勤的功能。但隨著公司規模擴大及使用者數量與使用者的身分類別增加，門禁控制器無法存放所有RFID卡片號碼和可通行時段資料。本研究利用OM2M物聯網中介軟體框架，架構有伺服器、伺服器端和裝置端中介軟體、裝置，其中傳統門禁控制器系統及新種類的系統視為裝置，裝置與雲端伺服器會透過中介軟體轉換通訊格式，雲端伺服器不需要因應新種類的裝置去更新系統，達到異質門禁控制器管理，然後為了加快使用者回應時間，加入cache的概念，將傳統門禁控制器系統視為第一層cache，具有cache功能的裝置端中介軟體視為第二層cache，雲端伺服器視為最上層，每一層在cache miss時會向上一層詢問資料，達到門禁控制器能夠主動得到伺服器的資料，最後為了增加cache命中率，在門禁控制器伺服器加入多個cache替換策略，透過大量實驗得到各種情境中最好的cache替換策略，加入以實驗結果為基礎的策略預測演算法的雲端伺服器週期地預測第一層cache接下來最好的cache替換策略，並命令第一層cache更換cache替換策略。

In a traditional attendance and access controller management system, the server can connect to the access controller using an RS485 direct connection or network can control the access controller remotely. It can store user access data (RFID card number and time zone) to the access controller, which can determine whether to open the door based on the data when someone tags the RFID card and can periodically obtain RFID tagging event data from the access controller. It can also calculate attendance information, such as commute time, absences, and salary, by combining the event data with the user’s information. However, with company growth, the number of users and, thus, user identities increases, and the access controller cannot store all of the user access data. This study uses an OM2M IoT middleware framework. The system architecture consists of a server, server- and device-side middleware, and devices. The traditional access controller system and a new type of system are treated as devices. The devices and the cloud server convert communication protocol and data formats using the middleware. Cloud servers do not need to be updated because of the new types of devices. It uses heterogeneous access controller management. Then, to increase the user response time, we add the cache concept. The traditional access controller system is treated as the first-layer cache. The device-side middleware with cache capability is treated as the second-layer cache. The cloud server is treated as the top-layer cache. When each layer of cache encounters a cache miss, it requests data from the next layer. This allows the access controller to initiate a request for the server’s data. Finally, to increase the cache hit rate, we add multiple cache replacement policies to the access controller server. Experiments were conducted to determine the best cache replacement policy for various scenarios. Periodically adding an experimental results-based policy prediction algorithm predicts the best cache replacement policy for the first-layer cache and then requires the first-layer cache to change the cache replacement strategy.

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