4G LTE可以提供即時回報系統的應用程式良好的服務品質，因此物聯網這項技術已經被大量的運用在4G LTE的環境。在許多的文獻中，4G LTE有許多上傳的排程演算法，但大多的演算法是針對人對人的應用程式，並非針對物聯網的裝置；另外大部份物聯網的裝置都是裝載電池，因為電池能提供的電量是有限制的，所以我們必須透過排程演算法的排程結果減少耗能；此外，現在的文獻不是考慮睡眠時間最大化，就是傳送能量最小化，並沒有同時考慮這兩者。在我們的論文裡，我們有系統地同時考慮所有耗能的情況，並提出了兩個節能的排程演算法，這兩個排程演算法的選用是根據裝置跟LTE基地台的距離。第一個演算法是針對距離較遠的情況，它致力於減低傳送資料時候的能量；第二個演算法是針對距離較近的情況，它致力於減少裝置的活躍時間，並同時決定裝置是否要進入睡眠狀態或是只在閒置狀態。實驗結果表示我們提出的演算法比其他的演算法省電，此外我們的演算法依然可以維持較高的排程成功率及公平性。

Machine-to-Machine (M2M) communication technology has been utilized in 4G LTE to support a variety of M2M applications, which require real-time data reporting. In the literature, many uplink scheduling algorithms have been proposed for 4G LTE. Nevertheless, most of these scheduling algorithms are mainly designed for human-to-human applications, not for M2M applications. These scheduling algorithms typically focus on the fact that the machines in M2M communication are battery-powered. Considering that the energy budget of the battery is limited, these scheduling schemes aim to reduce the energy consumption. We note that the current literatures consider either the problem of sleep-time maximization or transmission power minimization, but not both. In this paper, we systematically consider all energy-consumption factors of M2M real-time reporting. We propose two energy-efficient scheduling algorithms to minimize the total energy consumption of the machines and these two algorithms depend on the distance between a machine and the serving LTE eNB. The first algorithm is for the case when the distance is long and it aims to reduce the energy consumption for the machines to transmit their data. The second algorithm is for the case when the distance is short. It aims to reduce the active slots of the machines and also determines whether the machines are worth going into the sleep mode or just staying at the active mode but idle state. The experiment results show that our algorithms have better performance than the com- pared algorithms in terms of energy consumption. Additionally, our algorithms can also maintain scheduling success ratios and fairness.

Abstract i
Contents ii
ListofFigures iv
ListofTables v
1 Introduction 1
2 SystemModel 5
2.1SimplifiedSC-FDMAMACModel.......................5
2.2ChannelModel.................................5
2.3PowerConsumption..............................8
3 Energy-EfficiencySchedulingProblem 11
3.1M2MCommunicationNetworkArchitecture.................11
3.2ProblemFormulation..............................12
3.2.1AllocationConstraint..........................14
3.2.2ContiguityConstraint.........................14
3.2.3Real-TimePacketConstraint.....................16
4 SchedulingAlgorithm 17
4.1TheFirstEnergy-EfficiencySchedulingAlgorithm..............17
4.1.1ConceptofAlgorithm1........................17
4.1.2TheProposedAlgorithm1.......................18
4.2TheSecondEnergy-EfficiencySchedulingAlgorithm.............19
4.2.1ConceptofAlgorithm2........................21
4.2.2TheProposedAlgorithm2.......................22
5 Simulation 24
5.1TwoObservationsbehindAlgorithm2....................24
5.1.1TheFirstObsevation..........................24
5.1.2TheSecondObsevation.........................26
5.2PerformanceEvaluation............................26
5.2.1TheImpactofNumberofNodes....................29
5.2.2TheImpactoftheDistance......................32
5.2.3TheComputationTime........................33
6 Conclusion 36

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