非自主性移動無線感測器網路:模型化與應用｜國立清華大學博碩士論文庫

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研究生：	李皇辰 Lee, Huang-Chen
論文名稱：	非自主性移動無線感測器網路:模型化與應用 Non-Autonomous Mobile Wireless Sensor Network: Modeling and Application
指導教授：	金仲達 King, Chung-Ta
口試委員:
學位類別：	博士 Doctor
系所名稱：	電機資訊學院 - 資訊工程學系 Computer Science
論文出版年：	2010
畢業學年度：	99
語文別：	英文
論文頁數：	74
中文關鍵詞：	無線感測器網路、移動模型、環境監控、現象內監測、漂浮軌跡、土石流
外文關鍵詞：	wireless sensor network, mobility model, environment monitoring, in-situ monitoring, drifting trajectory, debris flow
相關次數：	點閱：3 下載：0
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使用無線感測器進行環境的監測，可以克服傳統監測方式的許多限制，並提高監測範圍與有效性。一般無線感測器網路系統的設計，多是假設無線感測器安裝於固定地點，或者是具有自主移動的能力。本論文針對非自主性移動的無線感測器網路(Non-Autonomous Mobile Wireless Sensor Network,簡稱NAMWSN)，例如被動順流於河流、海面或土石流上的流體的無線感測器，進行分析探討。此種感測器系統可以作到現有固定式感測器系統無法完成的功能。以土石流監控的例子來說，經過特別設計的無線感測器，可以隨著土石流移動，並於土石流內部進行當下現象的量測。
本研究設計並實作NAMWSN系統來進行土石流災害監控。傳統的土石流監測系統為固定式安裝於河岸、山坡等特定區域，被動的偵測土石流發生時所伴隨的現象。此種定點監測的方式，無法有效的持續監控移動的土石流，更無法監測土石流內部的資訊。本論文提出利用現象內監測的方式，讓無線感測器可以隨著土石流一起移動，使其可持續的監測土石流內部的資訊，包括：速度、方向與孔隙壓力等，並且即時將資訊回傳，直接監測現象內的情形。為了使無線感測器能夠長時間且有效率的在戶外運作，此種戶外的無線感測器系統設計考量與一般室內運作的無線感測器系統有顯著的差異。本研究針對系統各項設計考量，包含低耗電、即時反應性、通訊性能與包裝設計等，作了完整的探討、分析與測試。
設計NAMWSN系統，所仰賴的相關技術和傳統固定式的感測器有很大的差異，其中一項關鍵技術就是移動模型(mobility model)的建立。本文即使用在水上流動的非自主性移動無線感測器，透過蒐集其GPS移動軌跡，以此進行移動模型的分析與建立。所建立的移動模型，可產生大量虛擬的漂流軌跡，並提供NAMWSN無線感測器佈建策略與無線網路通訊的分析與模擬，節省實際測試系統的成本，並提升系統的NAMWSN系統的監測與網路傳輸效率。

Monitoring natural environments and disasters using wireless sensors could eliminate the constraints of wires that exist in traditional methods and could expand monitoring regions and increase monitoring efficiency. Currently, most wireless sensor systems are designed with some implicit assumptions, including that sensors are either stationary and installed in the ground or that they are able to move autonomously. In this thesis, we study a Non-Autonomous Mobile Wireless Sensor Network (NAMWSN), i.e., wireless sensor that drift on the surface of a river, sea, or debris flow and are moved passively by currents. The advantage of a NAMWSN is that the wireless sensors can directly and in-situ measure the parameters inside the target phenomena, which existing immobile equipments cannot do.
In this thesis, we design and implement a non-autonomous mobile wireless sensor system to monitor debris flow. Traditional debris flow monitoring systems are stationary, but are expected to monitor moving debris. Obviously, this approach cannot efficiently track debris flows. For this reason, based on the NAMWSN concept, we propose a novel method of in-situ monitoring debris flow by dropping specially designed sensors in a debris flow and letting them move within the flow; thereby making the continuous tracking of debris flow and real-time reporting of data possible. The design considerations are significantly different from an indoor wireless sensor system. We analyze and evaluate the proposed wireless-sensor-based debris flow monitoring system in many aspects, including low power operation, system responsiveness, communication performance and packaging, etc.
In contrast to traditional immobile WSNs, designing a NAMWSN system requires the development of new techniques, such as building a mobility model of the NAMWSN sensors. In this thesis, we collect the drifting trajectories of the wireless sensors using GPS; these trajectories are converted into maps and are used to build the mobility model of drifting objects. Based on the mobility model, an unlimited number of virtual drifting trajectories can be generated and fed into simulators. Therefore, we can analyze the performance of the newly designed NAMWSN system without costly experiments and can improve system performance.

Table of Contents
中文摘要    1-1
Abstract    1-2
Acknowledgment    1-4
Table of Contents    1-5
List of Figures    1-6
List of Tables    1-8
Chapter 1 Introduction    1-9
Chapter 2 Related Works    2-14
2.1    Debris Flow Monitoring System    2-14
2.2    Surface Current Measurement and Mobility Model    2-17
Chapter 3 Design of a Multi-Functional Wireless Sensor for In-Situ Monitoring of Debris Flows    3-20
3.1    Introduction    3-20
3.2    Design Requirements    3-23
3.3    System Architecture and Design    3-24
3.4    System Evaluation and Discussion    3-36
3.5    Summary    3-42
Chapter 4 Measuring Surface Currents and Building Mobility Model of Drifting Objects    4-43
4.1    Introduction    4-43
4.2    Collecting Trajectories    4-46
4.3    Analysis and Building Parameter Maps    4-50
4.4    Mobility Model of Drifting Objects    4-56
4.5    Discussion    4-65
4.6    Summary    4-67
Chapter 5 Discussion and Conclusion    5-68

List of Figures
Figure 1 1. The different trajectories of the NAMWSN wireless sensors affect the deployment strategy of the wireless receivers.    1-11
Figure 2 1. An overview of the Shen-Mu debris flow monitoring station at Shen-Mu Village, Nantou County, Taiwan    2-14
Figure 2 2. The snapshot of the debris flow in Ai-Yu-Zi River at Shen-Mu Village, Nantou County, Taiwan on Aug 8, 2009 during Typhoon Morakot.    2-16
Figure 3 1. An overview of the proposed debris flow monitoring system.    3-22
Figure 3 2. The system architecture of the proposed debris flow monitoring system.    3-24
Figure 3 3. The internal structure of the INSIDER.    3-25
Figure 3 4. Standby Communication Schedule (SCS) of INSIDERs in the STANDBY mode.    3-28
Figure 3 5. Active Communication Schedule (ACS) of INSIDERs in ACTIVE mode.    3-29
Figure 3 6. INSIDER’s MRT and current under different cycle lengths.    3-33
Figure 3 7. An INSIDER stands in floodwater on the riverbed to test its waterproof encapsulation.    3-35
Figure 3 8. The IS2CO PDR in different antenna and distance settings.    3-38
Figure 3 9. The IS2CO PDR in different IS2CO LQI and IS2CO RSSI.    3-39
Figure 3 10. The figure shows the typical riverbed in the Shen-Mu debris flow monitoring station. Note that the regions of riverbed closed to both riverbanks are dry and suitable for setting INSIDERs.    3-40
Figure 4 1. The workflow of the proposed method.    4-44
Figure 4 2. The parameter map showing the current directions in the harbor entrance. The region of the harbor entrance is divided into rectangular areas.    4-45
Figure 4 3. The wireless GPS-sensor that floats on the water and records its drifting trajectory.    4-45
Figure 4 4. A bird’s-eye view of the area for the proof-of-concept experiments. The GPS-sensors were dropped at the lower right (upstream) and retrieved at the upper left (downstream).    4-48
Figure 4 5. The scatter plot of 4557 points from 30 collected trajectories.    4-48
Figure 4 6. The cumulative probability function (CDF) of the current velocity (Distvelocity).    4-49
Figure 4 7. The probability distribution of the current direction (Distdirection).    4-49
Figure 4 8. The point quantity map (N Pmap) from our proof-of-concept experiment.    4-54
Figure 4 9. The general velocity map (V Pmap) from our proof-of-concept experiment.    4-54
Figure 4 10. The general direction map (D Pmap) from our proof-of-concept experiment.    4-55
Figure 4 11. The pseudo code of the proposed trajectory generation algorithm and the flow chart.    4-58
Figure 4 12. The point quantities of the surrounding cells of CurrentCell are used to decide the probabilities of the direction of the bouncing movement.    4-59
Figure 4 13. Three virtual trajectories (in red) are shown over the points of the collected trajectory (in green).    4-60
Figure 4 14. The cumulative probability of current velocity.    4-61
Figure 4 15. The cumulative probability of current direction    4-61
Figure 4 16. The matching ratio vs. the point quantity of virtual trajectory.    4-64
Figure 4 17. The coverage ratio vs. the point quantity of virtual trajectory.    4-64
 List of Tables
Table 3 1. MRT versus time slot length and cycle length    3-33
Table 3 2: The antenna used in the evaluation    3-37

                                

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