請用此 Handle URI 來引用此文件：
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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林永松 | zh_TW |
| dc.contributor.advisor | Yeong-Sung Lin | en |
| dc.contributor.author | 劉柏辰 | zh_TW |
| dc.contributor.author | Bo-Chen Liu | en |
| dc.date.accessioned | 2023-05-11T05:33:08Z | - |
| dc.date.available | 2023-11-10 | - |
| dc.date.copyright | 2023-05-10 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-01-24 | - |
| dc.identifier.citation | References
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/87146 | - |
| dc.description.abstract | 近年來，無線感測網路因為其成本低，耗電量低，體積小和容易布置等等特性，被大量的運用在各領域中。但其缺點也顯而易見。因為每個感測器中是以無線的方式傳送資料，因此電源的來源通常來自感測器內的電池。感測網路中能源的節省和功率控制便成為一個極其重要的議題。
在此論文中我們將此無線網路最小化之問題構建為數學模型，並且此問題受延遲和產出之限制。在模型中我們需要決定感測器活躍的機率，傳送之距離和傳送之封包大小並藉以最小化能耗。此些決策變數中存在著不同的平衡，我們也藉由後續的實驗找出他們之間不同的關係。 此論文於運用拉格朗日鬆弛法，將其分解成子問題一一解出並最終產出緊貼於上界之下界數值。在實驗中我們也找出不同拉格朗日係數的使用方法，並期望能達到能源消耗的最小化，並同時也能維持網路的連接和資料之輸出。 | zh_TW |
| dc.description.abstract | In recent years, Wireless Sensor Networks(WSNs) have been widely used in various fields because of their low cost, low power consumption, small size and easy deployment. But its shortcomings are also obvious. Because each sensor transmits data wirelessly, the source of power usually comes from the battery inside the sensor. Energy conservation and power control in sensing networks becomes an extremely important issue.
In this paper we model this wireless network minimization problem as a mathematical model, and the problem subjected to delay and throughput constraints. In the model we need to determine the probability of the sensor being active, the distance to transmit and the size of the transmitted packet to minimize energy consumption. There are different trade offs among these decision variables, and we also find different relationships between them through follow-up experiments. This paper uses the Lagrangian relaxation method and decompose it into sub-problems and solve them one by one, and find a lower bound that is tight with the upper bound value. In the experiment, we also find out different use of the Lagrangian multipliers, and hope to achieve the minimization of energy consumption, and at the same time to maintain the network connection and data output. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-05-11T05:33:08Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-05-11T05:33:08Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 摘要 i
Abstract ii Contents iv List of Figures ix List of Tables x Chapter 1 Introduction 1 1.1 Background Overview . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Characteristics of WSN . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.2 Architecture of WSN . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Chapter 2 Literature Review 7 2.1 Duty Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Control of Transmission Power . . . . . . . . . . . . . . . . . . . . 9 2.3 Topology Control and Routing . . . . . . . . . . . . . . . . . . . . . 10 Chapter 3 Problem Formulation 13 3.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Model 1 : One-to-One Relationship . . . . . . . . . . . . . . . . . . 14 3.3 Model 2 : Many-to-One Relationship . . . . . . . . . . . . . . . . . 20 3.4 Model 3 : Network Tree Structure Relationship . . . . . . . . . . . . 27 Chapter 4 Solution Approach 38 4.1 Lagrangian Relaxation Method . . . . . . . . . . . . . . . . . . . . . 38 4.2 Model 1 : One-to-One Relationship . . . . . . . . . . . . . . . . . . 41 4.2.1 Deal with Decision Variables . . . . . . . . . . . . . . . . . . . . . 41 4.2.2 The LR Subproblems . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.2.2.1 Subproblem 1(related to decision variable xi) . . . . . 48 4.2.2.2 Subproblem 2(related to decision variable xj ) . . . . . 50 4.2.2.3 Subproblem 3(related to decision variable yi) . . . . . . 52 4.2.2.4 Subproblem 4(related to decision variable yj ) . . . . . 54 4.2.2.5 Subproblem 5(related to decision variable qi) . . . . . . 57 4.2.2.6 Subproblem 6(related to decision variable qj ) . . . . . 59 4.2.2.7 Subproblem 7(related to decision variable ri) . . . . . . 61 4.2.2.8 Subproblem 8(related to decision variable rj ) . . . . . 63 4.2.2.9 Subproblem 9(related to decision variable Zij ) . . . . . 66 4.2.2.10 Subproblem 10(related to decision variable βij ) . . . . 67 4.3 Model 2 : Many-to-One Relationship . . . . . . . . . . . . . . . . . 70 4.3.1 Deal with Decision Variables . . . . . . . . . . . . . . . . . . . . . 70 4.3.2 The LR Subproblems . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.3.2.1 Subproblem 1(related to decision variable xi) . . . . . 77 4.3.2.2 Subproblem 2(related to decision variable xκ) . . . . . 79 4.3.2.3 Subproblem 3(related to decision variable yi) . . . . . . 81 4.3.2.4 Subproblem 4(related to decision variable yκ) . . . . . 83 4.3.2.5 Subproblem 5(related to decision variable qi) . . . . . . 85 4.3.2.6 Subproblem 6(related to decision variable qκ) . . . . . 87 4.3.2.7 Subproblem 7(related to decision variable ri) . . . . . . 89 4.3.2.8 Subproblem 8(related to decision variable rκ) . . . . . 91 4.3.2.9 Subproblem 9(related to decision variable Ziκ) . . . . . 94 4.3.2.10 Subproblem 10(related to decision variable βiκ) . . . . 95 4.3.2.11 Subproblem 11(related to decision variable Ii) . . . . . 97 4.4 Model 3 : Network Tree Structure Relationship . . . . . . . . . . . . 99 4.4.1 Deal with Decision Variables . . . . . . . . . . . . . . . . . . . . . 99 4.4.2 The LR Subproblems . . . . . . . . . . . . . . . . . . . . . . . . . 108 4.4.2.1 Subproblem 1(related to decision variable xθi ) . . . . . 108 4.4.2.2 Subproblem 2(related to decision variable xκi ) . . . . . 110 4.4.2.3 Subproblem 3(related to decision variable xξ ) . . . . . 112 4.4.2.4 Subproblem 4(related to decision variable yθi ) . . . . . 113 4.4.2.5 Subproblem 5(related to decision variable yκi ) . . . . . 114 4.4.2.6 Subproblem 6(related to decision variable yξ ) . . . . . 116 4.4.2.7 Subproblem 7(related to decision variable qθi ) . . . . . 117 4.4.2.8 Subproblem 8(related to decision variable qκRi ) . . . . 119 4.4.2.9 Subproblem 9(related to decision variable qξ ) . . . . . 121 4.4.2.10 Subproblem 10(related to decision variable rθi ) . . . . 122 4.4.2.11 Subproblem 11(related to decision variable rκi ) . . . . 123 4.4.2.12 Subproblem 12(related to decision variable rξ ) . . . . . 125 4.4.2.13 Subproblem 13(related to decision variable Zθi,κi ) . . . 126 4.4.2.14 Subproblem 14(related to decision variable Zκi,ξ ) . . . 128 4.4.2.15 Subproblem 15(related to decision variable βθi,κi ) . . . 129 4.4.2.16 Subproblem 16(related to decision variable βκi,ξ ) . . . 132 4.4.2.17 Subproblem 17(related to decision variable Iθi ) . . . . 133 4.4.2.18 Subproblem 18(related to decision variable IκRi ) . . . . 134 4.4.2.19 Subproblem 19(related to decision variable Dθi,ξ ) . . . 136 4.4.2.20 Subproblem 20(related to decision variable qκSi ) . . . . 138 4.4.2.21 Subproblem 21(related to decision variable IκSi ) . . . . 139 4.5 Lagrangian Dual Problem and The Subgradient Method . . . . . . . 140 4.6 Getting Primal Feasible Solution . . . . . . . . . . . . . . . . . . . . 145 4.6.1 Model 1(One-to-one relationship) . . . . . . . . . . . . . . . . . . 145 4.6.2 Model 2(Many-to-one relationship) . . . . . . . . . . . . . . . . . . 146 4.6.3 Model 3(Network Tree Structure) . . . . . . . . . . . . . . . . . . . 147 Chapter 5 Experimental Results and Discussion 150 5.1 Experimental Environment . . . . . . . . . . . . . . . . . . . . . . . 150 5.2 Experiments and Results . . . . . . . . . . . . . . . . . . . . . . . . 152 5.2.1 The Design of Probability and Energy Consumption Functions . . . 152 5.2.2 Experiment 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 5.2.3 Experiment 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 5.2.4 Experiment 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 5.2.5 Experiment 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 5.2.6 Experiment 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 5.2.7 Experiment 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Chapter 6 Conclusions and Future Work 169 6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 References 171 | - |
| dc.language.iso | en | - |
| dc.subject | 最佳化 | zh_TW |
| dc.subject | 功率控制 | zh_TW |
| dc.subject | 能耗節省 | zh_TW |
| dc.subject | 綠能科技 | zh_TW |
| dc.subject | 無線感測網路 | zh_TW |
| dc.subject | 拉格朗日鬆弛法 | zh_TW |
| dc.subject | Optimization | en |
| dc.subject | Power Control | en |
| dc.subject | Energy-Saving | en |
| dc.subject | Green Information Technology | en |
| dc.subject | Wireless Sensor Network | en |
| dc.subject | Lagrangian Relaxation | en |
| dc.title | 運用最佳化技術於綠能無線感測網路能耗控制機制 | zh_TW |
| dc.title | An Optimization-based Power Control Mechanism for Saving Energy in Green Wireless Sensor Networks | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 鍾順平;孔令傑;李家岩;呂俊賢 | zh_TW |
| dc.contributor.oralexamcommittee | Shun-Ping Chung;Ling-Chieh Kung;Chia-Yen Lee;Chun-Hsien Lu | en |
| dc.subject.keyword | 功率控制,能耗節省,綠能科技,無線感測網路,拉格朗日鬆弛法,最佳化, | zh_TW |
| dc.subject.keyword | Power Control,Energy-Saving,Green Information Technology,Wireless Sensor Network,Lagrangian Relaxation,Optimization, | en |
| dc.relation.page | 179 | - |
| dc.identifier.doi | 10.6342/NTU202210101 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-02-01 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 資訊管理學系 | - |
| 顯示於系所單位： | 資訊管理學系 | |
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