機器間通訊 (Machine-to-Machine, M2M) 指的是機器在沒有人為介入的情況下彼此自動相互通訊，藉著這樣的訊息交換，來共同提供相關的M2M服務。關於機器間通訊之技術已有相當廣泛之應用，即時訊息回報 (Real-time data reporting) 對大多數應用而言是非常重要的需求，藉著M2M節點週期性的回報感測之訊息，後端M2M伺服器可以即時監控M2M節點端環境之變化並做出回應以提供相關服務。由於M2M節點通常只配備電池供應之電能，因此有效的節省M2M節點端之電能消耗是能否提供完整M2M服務之關鍵，然而，M2M節點配備了多種類型之感測器以感測不同種類之資料，並且會根據不同種類資料之回報周期定期回傳相關感測資訊，在缺乏適當排程機制來協調M2M節點端各種類型資料之傳送時間的情況下，隨著M2M節點要感測的資料種類增加，M2M節點必須在幾乎每一個時槽保持主動模式以傳送感測資料，進而導致大量之電能消耗。本篇論文中提出了一個針對即時資料回報之排程演算法，演算法的目的在於節省M2M節點端之電能消耗，我們首先將問題模組成一個電能最小化問題並證明此問題之複雜度，接著，針對此問題提出了我們的省電排程演算法，最後，在效能分析中，顯示我們的演算法可以有效的節省M2M節點端之電能消耗，並且保證感測訊息在傳送期限內送達以及資源的平均分配。

Machine-to-Machine (M2M) communication technology has recently gained intense attention and been utilized in a variety of applications. Real-time data reporting is a basic requirement of most of the M2M applications. By the real-time data reported from the M2M nodes, the back-end M 2M server can perform remote monitoring and provide corresponding services. Since M2M nodes are usually with limited battery power supply, energy-saving for M2M nodes becomes a critical issue on supporting M2M communications. In this paper, we propose an energy-efficient scheduling algorithm for real-time data reporting in the purpose of reducing the energy consumption of M2M nodes. We first model the energy-efficient problem into an optimization problem and prove the hardness of this problem. To address this problem, we propose our energy-efficient scheduling algorithm. Last, we measure the performance of our algorithm and show that our algorithm can effectively guarantee the fairness and data aborted rate while achieve energy efficiency.

Abstract i
Contents ii
List of Figures iv
List of Tables v
1 Introduction 1
2 Problem Formulation 5
2.1 Assumptions 5
2.2 Network Architecture and Notations 6
2.3 Problem Formulation 8
2.4 Problem Complexity 9
2.5 Difference from Bin-Packing 11
3 Energy-Efficient Scheduling Algorithm 12
3.1 Concept 12
3.2 Energy-Efficient Scheduling Algorithm 13
3.3 Procedure of the Energy-Efficient Scheduling Algorithm 15
4 Performance Evaluation 17
4.1 Performance on Energy Consuming Ratio 19
4.2 Performance on Data Aborted Ratio 21
5 Conclusion 23
Bibliography 24

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