物聯網 (Internet of Things, IoT) 已被認為是未來最重要的網路示例之一。對於物聯網，確保其偵測的正確性是極為重要的。正確性包含準確度 (Accuracy) 及精確度 (Precision) 兩個要素。另一方面，為了減少網路中運算及傳輸的能量消耗，行動邊緣運算 (Mobile Edge Computing, MEC)，即於行動邊緣處理物聯網所產生的大數據已成為了一個很有前途的方法。然而，確保物聯網之準確度及精確度需求，且同時在行動邊緣網路中最小化能量消耗的問題，在過去並未被探討。因此，在本篇論文中，我們探討了在行動邊緣網路中之能量消耗最小化問題，並且同時考慮了：(1) 物聯網之準確度、精確度及覆蓋 (coverage) (2) 行動邊緣伺服器 (MEC server) 上的大數據處理 (3) 物聯網網路和行動邊緣網路之能量消耗。具體來說，給定：(1) 物聯網裝置集合 (2) 目標集合 (3) 行動邊緣網路 (4) 能量消耗模型 (5) 準確度和精確度需求，我們制定了一個新的最佳化問題，稱為Accuracy and Precision-Aware IoT Device Selection (APAIDS)，以最小化於行動邊緣網路中之整體能量消耗。我們證明APAIDS是NP-hard，並且提出了一個新的演算法，稱為Energy Efficient Device and MEC Server Selection (EDMS)，藉由共同選擇物聯網裝置、配置行動邊緣運算連接及選擇伺服器處理每個目標的資料，以最小化能量消耗。最後，我們在兩個真實網路拓樸中評估EDMS。相較於基線的方法，模擬結果顯示整體能量消耗能夠減少超過60%。

Internet of Things (IoT) has been regarded as one of the most significant network paradigms in the future. For IoT, it is crucial to ensure the correctness of detection which includes the factors of accuracy and precision. On the other hand, Mobile Edge Computing (MEC) has emerged as a promising way to process big IoT data at the network edge so as to reduce the computation and transmission energy in the networks. Nevertheless, minimizing the energy consumption in MEC networks while ensuring both the accuracy and precision requirements of IoT has not been explored before. In this thesis, therefore, we explore the energy minimization problem in MEC networks by considering the accuracy, precision, and coverage of IoT, big data processing on MEC servers as well as the dissipated energy in both IoT and MEC networks. Specifically, given 1) a set of IoT devices, 2) a set of observed targets, 3) an MEC network, 4) the energy consumption model, and 5) the accuracy and precision requirements, we formulate a new optimization problem, named Accuracy and Precision-Aware IoT Device Selection (APAIDS), to minimize the overall energy consumption in MEC networks. We prove the NP-hardness of APAIDS and then propose a new algorithm, named Energy Efficient Device and MEC Server Selection (EDMS), to minimize energy consumption by jointly selecting IoT devices, configuring MEC association, and selecting processing servers for dealing with the data of each target. Finally, we evaluate EDMS on two real networks. In comparison with the baseline schemes, the results manifest that the overall energy consumption can be reduced by more than 60%.

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