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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 魏宏宇 | zh_TW |
| dc.contributor.advisor | Hung-Yu Wei | en |
| dc.contributor.author | 徐永霖 | zh_TW |
| dc.contributor.author | Yung-Lin Hsu | en |
| dc.date.accessioned | 2025-02-18T16:16:11Z | - |
| dc.date.available | 2025-02-19 | - |
| dc.date.copyright | 2025-02-18 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2025-02-06 | - |
| dc.identifier.citation | [1] 3GPP, “Study on New Radio (NR) to support non-terrestrial networks,” 3rd Generation Partnership Project (3GPP), Technical Report (TR) 38.811, 10 2020, version 15.4.0.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96466 | - |
| dc.description.abstract | 整合感測與通信是國際電信聯盟無線電通信部門與第三代合作夥伴計畫推薦的第六代及更高世代的潛在應用場景。本論文研究了兩種整合感測與通信環境監測場景：智慧工廠和災害救援。在智慧工廠中，業主可以通過減少能源使用降低運營成本；而在災害救援中，可以通過節約能源延長感測器的壽命。然而，系統效能（如資訊的新鮮度）不應因此受到影響。在此背景下，本論文的三個研究工作的最終目標是，在滿足資訊陳舊度要求的前提下，實現數據傳輸能耗的最小化。
本論文的第一個研究工作探討了一個實時環境監測場景，其中感測器主動收集環境數據並將其傳輸至控制器。第一個工作採用熵風險測度，並結合李亞普諾夫優化的概念，在滿足陳舊度違反概率和超過預定閾值的平均極端陳舊度約束下，實現能耗的最小化。為應對極端陳舊度，本研究融入聯邦學習的框架以生成全局統計數據。數值結果表明，基於聯邦學習的解決方案在建模能耗方面優於集中式基線方法。繼承第一項工作的基礎，第二項研究進一步探討感測器數據採樣頻率與數據傳輸能耗之間的權衡，同時保持資訊的新鮮度。同樣地，第二項工作利用熵風險測度實現全球傳輸能耗的最小化，並滿足陳舊度約束。為尋找最合適的平衡，該研究提出了一個基於聯邦學習的兩階段優化框架，通過李亞普諾夫優化迭代學習最佳採樣頻率與相應的全球傳輸能耗。數值結果顯示，該基於聯邦學習的框架在輕微性能損失的情況下，比集中式基線方案節省了更多計算能耗。 考慮到地球表面活動的不斷增加，第三項研究著重於為災害救援整合臨時地面與非地面感測網絡。為了保持採樣數據的新鮮度，本研究提出了一種方法其包括數據流量安排機制和資源分配最佳化，目的在考慮兩種數據時效性要求（即端到端延遲和條件監測間隔）的情況下，最佳化感測數據傳輸的能耗。具體而言，感測數據流量以動態輪詢方式進行管理，而功率和頻寬分配則採用交替方向乘子法進行最佳化，以滿足上述的的兩種數據時效性要求。除了多場景分析外，數值結果顯示，從簡化提出之方法中節省能量對於系統成本（即目標能量、處理能耗和約束損失總和）而言並不有效。 | zh_TW |
| dc.description.abstract | Integrated sensing and communication (ISAC) is a promising sixth generation (6G) and beyond usage scenarios recommended by the International Telecommunication Union Radiocommunication Sector (ITU-R) and the 3rd Generation Partnership Project (3GPP). This dissertation studies two ISAC environment monitoring scenarios: the smart factory and disaster relief. In the smart factory, the owner can lower the operation costs by reducing energy usage, whereas in disaster relief, the sensor's life cycle can be prolonged by conserving energy. However, the system performance, e.g., the freshness of information, should not be compromised. In this regard, the ultimate objective of the three works in this dissertation is to minimize data transmission energy subject to some staleness requirements.
In this dissertation, the first work studies a real-time environment monitoring scenario, where the sensors proactively collect environmental data and transmit it to the controller. The first work adopts the entropic risk measure (ERM) to minimize the energy consumption subject to the constraints on the staleness violation probability and the average extreme staleness exceedances over a pre-defined threshold by leveraging the concept of Lyapunov optimization. To deal with extreme staleness, this work weaves the framework of federated learning (FL) to create global statistics. Numerical results demonstrate that the proposed FL-based solution outperforms the centralized baseline regarding modeling energy consumption. Inheriting the first work, the second work further investigates the trade-off between the sensor's data-sampling frequency and the data transmission energy while maintaining information freshness. Similarly, the second work minimizes global transmission energy by leveraging ERM and is subject to staleness constraints. To find the most appropriate trade-off, the second work proposes an FL-based two-stage optimization framework to iteratively learn the optimal sampling frequency with the corresponding global transmission energy via Lyapunov optimization. Numerical results show that the proposed FL-based framework saves more computing energy than the centralized baseline with slight performance degradation. Considering Earth's increasing surface activity, the third work focuses on integrated Ad Hoc terrestrial and non-terrestrial sensing networks for disaster relief. To keep the sampling data fresh, this work proposes an approach, which includes a data traffic orchestration mechanism and a resource allocation optimization scheme, to optimize the sensing data transmission energy while taking into account two staleness requirements, i.e., the End-to-End Delay (EED) and the Condition Monitoring Interval (CMI). Therein, the traffic of sensing data is managed in a dynamic round-robin manner, while the power and bandwidth allocation is optimized for sensing data transmission in an alternating direction approach of multipliers (ADMM) manner regarding the EED and CMI requirements. In addition to the multi-scenario analysis, the numerical results reveal that saving energy by simplifying the approach is inefficient regarding the system cost as the sum of objective energy, processing energy, and constraint loss. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-02-18T16:16:11Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-02-18T16:16:11Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 摘要 iii Abstract v Contents vii List of Figures x List of Tables xii Chapter 1. Introduction 1 1.1 Background 1 1.2 Introduction of the Works 6 1.3 Contribution of the Works 8 1.3.1 Chapter 2: Age-Optimal Power Allocation in Risk-Sensitive Industrial IoT 8 1.3.2 Chapter 3: Optimized Data Sampling and Energy Consumption in Industrial IoT 10 1.3.3 Chapter 4: Staleness-Aware Resource Allocation in Disaster Relief Integrated Networks 11 1.4 Preliminaries 12 1.4.1 Federated Learning Framework 12 1.4.2 Lyapunov Optimization 13 Chapter 2. Age-Optimal Power Allocation in Risk-Sensitive IIoT 16 2.1 Introduction 16 2.2 Related work 17 2.3 System Model and Problem Formulation 18 2.4 FL-Based Distributed Power Allocation 22 2.4.1 Lyapunov Optimization Framework 22 2.4.2 Distributed Power Allocation with Federated Learning for AoI Exceedances 23 2.5 Numerical Results 28 2.6 Summary 34 Chapter 3. Optimized Data Sampling and Energy Consumption in IIoT 35 3.1 Introduction 35 3.1.1 Related Work 36 3.2 System Model and Problem Formulation 38 3.2.1 System Architecture 38 3.2.2 Problem Formulation 40 3.3 Data Staleness-Aware Power Allocation 42 3.3.1 Lyapunov-Based Iterative Transmission Optimization Scheme (Lya-ITOS) 43 3.3.2 Bayesian Optimization-Based Sampling Granularity Criterion Optimization Scheme (Bay-SCOS) 52 3.4 Numerical Results 56 3.4.1 Performance Evaluation of FL-Based Lya-ITOS 59 3.4.2 Performance Evaluation of FL-Based Bay-SCOS 64 3.4.3 The Trade-Off Between Training Performance and Energy Consumption 68 3.5 Summary 71 Chapter 4. Staleness-Aware Resource Allocation in Disaster Relief Integrated Networks 73 4.1 Introduction 73 4.1.1 Related Work 74 4.2 System Model 76 4.2.1 Traffic Orchestration Mechanism 77 4.2.2 Problem Formulation 81 4.3 ADMM-Based Power and Bandwidth Allocation 83 4.4 Simulated System Settings 88 4.4.1 LEO Deployment 88 4.4.2 TC Channel Gain 90 4.4.3 NTC Channel Gain 91 4.4.4 Simulated Architecture 92 4.5 Numerical Results 92 4.5.1 Merit of the Proposed Traffic Orchestration Mechanism 92 4.5.2 Optimal Results in Different Scenarios 95 4.5.3 Effects of EED and CMI Requirements 101 4.5.4 Trade-off Between the Processing Energy and the Objective Energy 105 4.6 Summary 108 Chapter 5. Conclusions 110 5.1 Conclusions 110 5.2 Future Study Orientations 112 Bibliography 114 Appendix A: The Proof of Corollary 1 in Chapter 3 127 Appendix B: The iteration manner to updating parameter of the augmented Lagrange function (4.13) in Chapter 4 129 | - |
| dc.language.iso | en | - |
| dc.subject | 工業物聯網 | zh_TW |
| dc.subject | 資源分配 | zh_TW |
| dc.subject | 地面與非地面整合網路 | zh_TW |
| dc.subject | 災害救援 | zh_TW |
| dc.subject | 資訊新鮮度 | zh_TW |
| dc.subject | 感測與通訊整合 | zh_TW |
| dc.subject | information freshness | en |
| dc.subject | disaster relief | en |
| dc.subject | Industrial Internet of Things (IIoT) | en |
| dc.subject | Integrated Sensing and Communication (ISAC) | en |
| dc.subject | resource allocation | en |
| dc.subject | integrated terrestrial and non-terrestrial networks | en |
| dc.title | 智慧感測器節能的信息年齡感知資源分配 | zh_TW |
| dc.title | Age-Aware Resource Allocation for Intelligent Sensor Energy Saving | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-1 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 王奕翔;謝宏昀;高榮鴻;李佳翰;許裕彬 | zh_TW |
| dc.contributor.oralexamcommittee | I-Hsiang Wang;Hung-Yun Hsieh;Rung-Hung Gau;Chia-Han Lee;Yu-Pin Hsu | en |
| dc.subject.keyword | 感測與通訊整合,工業物聯網,資訊新鮮度,災害救援,地面與非地面整合網路,資源分配, | zh_TW |
| dc.subject.keyword | Integrated Sensing and Communication (ISAC),Industrial Internet of Things (IIoT),disaster relief,information freshness,integrated terrestrial and non-terrestrial networks,resource allocation, | en |
| dc.relation.page | 130 | - |
| dc.identifier.doi | 10.6342/NTU202500462 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-02-06 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電信工程學研究所 | - |
| dc.date.embargo-lift | 2030-02-06 | - |
| 顯示於系所單位： | 電信工程學研究所 | |
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