伴隨著無線通訊技術的進步，物聯網概念也隨之迅速發展，目前已廣泛應用於智慧建築、工業自動化與生醫資訊等環境中。近年來，智慧型行動裝置的普及化也成為了物聯網的發展助力，基於智慧型行動裝置的可攜性、無線通訊與行動運算等優點，藉由與物聯網之相互結合，能衍生出更豐富且創新的應用服務，達到智慧生活的發展目標。

現今於智慧生活之應用情境下，智慧型行動裝置僅做為資料通過的中繼站，所有的資料皆交由雲端負責運算，藉此避免智慧型行動裝置的電源過度消耗。值得注意的是，智慧型行動裝置隨著近年來的快速發展，裝置本身的運算效能與電源消耗都有了更好的表現。因此，本研究所探討之議題在於，如何自動分析物聯網裝置之資料特性，並依照分析結果動態調整資料處理方式，進而達到智慧型行動裝置省電之功效。

本研究提出了一套省電物聯網架構，使用智慧型行動裝置作為物聯網之中間層，並將省電機制與回饋機制建立於中間層內。隨著應用程式啟動，物聯網裝置收集之環境資訊將透過代理人傳輸至中間層，待掃描模組擷取資訊後，資訊將交由特徵評估模組與門檻值評估模組進行分析，並運用導向決策模組比較分析結果，判定較省電之資料處理方式。省電機制執行後，回饋機制將會記錄實際消耗電量，並根據實際執行經驗動態的調整門檻值，提升門檻值訂立的準確性。最後，本研究提出了兩個智慧生活應用，分別為生醫資訊與智慧建築，透過不同之應用情境進行分析。根據研究結果顯示，相較於將所有資料透過雲端運算，省電物聯網架構分別節省了24%與9.2%的電源消耗，故動態調整資料的處理方式能確實降低電源消耗。

With the rapid development of wireless communication technology, the concept of the Internet of Things (IoT) has gained increasing attention. The concept of IoT is extensively applied to smart buildings, industrial automation and biomedicine. Recently, the extensive use of smart devices has contributed to the development of the IoT. Wireless communication and mobile computing enable smart phones to be easily connected to the IoT and increasing numbers of creative IoT applications and services are appearing to achieve the goal of smart living.

Currently, in smart living, smart devices act as middleware that propagates data from sensors to the cloud. All of these data are processed in the cloud to reduce the power consumption on the smart devices. The rapid development of smart devices has increased the power of this middleware. This work focuses on power-saving by analyzing the features of data that are received from IoT sensor devices to optimize the data processing policy. Finally, the goal of reducing the power consumed by smart devices will be achieved.

This work proposes an architecture for a power-saving IoT, with the power saving and feedback mechanism implemented in the IoT middleware. When the application is launched, IoT sensor devices collect the data and send them to the middleware via an agent. After the scanning module in the IoT middleware has received the data, the data will be analyzed by a feature evaluation module and a threshold analysis module. Based on the results of the analysis, the policy decision module decides to process the data in the middleware or in the cloud. Then, the feedback mechanism records the power consumption after every evaluation and then, based on the history of those records, adjusts the threshold value dynamically to increase accuracy. Finally, two smart living applications – a biomedical application and a smart building application – are proposed. A comparison of all of data that are processed in the cloud reveals that the power-saving IoT architecture reduces power consumption by 24% and 9.2% for the respective applications. Therefore, the dynamic switching between processing data in the cloud and in the IoT middleware can reduce power consumption.

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