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研究生： 石天依
DESTI SYUHADA
論文名稱： 最佳化無人機路徑決策用於災難復原網路
A Novel Pathway System for Optimizing UAV Synthesis for Decision-Making in Disaster Resilience Network
指導教授： 馬奕葳
Yi-Wei Ma
口試委員: 陳永昇
Yong-Sheng Chen
黎碧煌
Bih-Hwang Lee
馬奕葳
Yi-Wei Ma
學位類別： 碩士
Master
系所名稱： 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年： 2023
畢業學年度： 112
語文別： 英文
論文頁數： 62
中文關鍵詞： 災區 、停留時間 、k-means分群 、韌性網路 、軌跡規劃 、決策
外文關鍵詞： post-disaster area, stay time duration, k-means clustering, resilience network, trajectory planning, decision making
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    • 在現今社會，資訊技術的依賴已經成為日常生活的常態，尤其是行動網路更是生活中不可或缺的存在。為了獲得低延遲高品質的行動網路，擁有高效且可靠的基地台網路是關鍵。然而，單靠基地台進行物聯網連接可能因潛在問題而導致網路訪問中斷。自然災害，如地震、海嘯、天體撞擊或土壤變動，可能會對網路系統的基礎設施造成嚴重損害甚至摧毀。無人機 (Unmanned Aerial Vehicle, UAV) 除了作為向地面用戶提供通訊的基地台外，還具有其他優點。其最顯著的優點之一是能夠快速部署並有效利用基礎設施，從而在時間和成本上都顯得更加高效。

    災害發生後，受影響地區網路中斷成為一大挑戰，這一挑戰因基地台故障和網路基礎設施的完全缺失而加劇。因此，本研究旨在通過最佳化覆蓋區域和提供高品質服務，在災後系統中建立一個有系統、充分支撐的解決方案。本研究目的在於透過考慮用戶特定需求和UAV的飛行高度，制定合理且有系統的災後飛行路徑，以增強覆蓋範圍。在用戶密集區域，由於通訊訪問需求增加，UAV將在該區域進行長時間停留。相反地，在需求較少的區域，將縮短停留時間。

    實驗環境分為一般使用者和緊急使用者兩種類型。在模擬區域方面，涵蓋50x50平方公里的範圍。透過利用整合k-means分群原理的路徑系統技術，可以實現96.8%的總區域涵蓋率，還考慮了 1000 個使用者中的 750 個一般使用者和 218 個緊急使用者。本研究指出在可用性方面，相較於不考慮使用者並且只包含分群長度的隨機值有12.7%的優勢。

    In contemporary times, society relies heavily on technology, wherein many resources and conveniences are readily accessible. The base station's efficient and reliable network is necessary to obtain this access. Nevertheless, relying solely on a base station for internet connectivity is not feasible due to potential issues that may lead to the disruption of network access provided by the base station. Natural calamities, such as seismic activities, tidal waves, celestial impacts, or soil movements, can disrupt or even annihilate network system infrastructure. In addition to serving as a base station for transmitting communications to ground users, UAVs provide other advantages. One notable advantage is their swift deployment and efficient infrastructure utilization, making them time-efficient and cost-effective.
    After a disaster, the interruption of networks in affected areas is a significant challenge, exacerbated by the malfunction of base stations and the complete absence of network infrastructure. Hence, the objective of this study is to achieve a systematic and well-supported path in the post-disaster system through the optimization of coverage area and the provision of high-quality service. Therefore, this study aims to enhance the extent of coverage by considering the specific needs of users and the height of UAVs to establish a logical and systematic flight path in a post-disaster scenario. Hovering will be developed in areas with a large concentration of users because of the heightened demand for communication access. The duration of UAV staytime in each cluster will be arranged into three stages based on priority order.
    The experimental environment has two classifications: normal and emergency users. The simulation area covers an area of 50x50 km2. A path system technique that integrates k-means clustering principles can achieve 96.8% of the total area coverage. It considers 750 normal users and 218 emergency users out of 1000 users. Regarding availability, this study shows a 12.7% advantage over random values that do not consider users and only generalize cluster lengths.

    Contents 摘要 I Abstract II Acknowledgement III Contents IV List of Figures VI List of Tables VII List of Notations VIII Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Contribution 5 1.3 Chapter Structure 6 Chapter 2 Background and Knowledge 7 2.1 Background 7 2.2 Networks for Disaster Resilience 8 2.2.1 Disaster Resilience Networks Concept 8 2.2.2 Disaster Resilience Networks' Difficulties 9 2.3 Unmanned Aerial Vehicles (UAVs) Role 10 2.3.1 Unmanned Aerial Vehicles in Disaster Response 10 2.3.2 Current UAV Applications in Disaster Response 10 2.4 Optimizing UAV Synthesis for Decision-Making 11 2.4.1 Research Gap 11 2.4.2 An Innovative Pathway System 11 2.5 Related Works 11 Chapter 3 Proposed Pathway System 16 3.1 Proposed System Architecture 16 3.2 Basic Layer 17 3.2.1 Collection Function 18 3.2.2 Monitor Function 20 3.2.3 Execution Function 21 3.3 Analysis Layer 21 3.3.1 Clustering Function 22 3.3.2 Priority Screening Function 24 3.3.3 Duration Function 25 3.4 Decision Layer 26 3.4.1 Service Area Function 27 3.5 Availability 28 3.6 Robustness 28 3.7 Fairness 29 3.8 Concept for Employment 29 Chapter 4 Performance Analysis 34 4.1 Experimental Environment 34 4.2 Assumptions 35 4.3 Experimental Parameters 35 4.4 Experimental Results and Analysis 35 4.4.1 Clustering 35 4.4.2 Stay Time Duration 36 4.4.3 Density Priority Value 39 4.4.4 Robustness 40 4.4.5 Availability Result 43 4.4.7 Fairness Result 44 Chapter 5 Conclusion and Future Work 47 5.1 Conclusion 47 5.2 Future Works 48 Reference 49

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