博碩士論文 etd-0717107-033248 詳細資訊


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姓名 劉品佑(Pin-yu Liu) 電子郵件信箱 E-mail 資料不公開
畢業系所 資訊管理學系研究所(Information Management)
畢業學位 碩士(Master) 畢業時期 95學年第2學期
論文名稱(中) 藉由調節取樣次數來最佳化在無線感測器網路上的連續查詢
論文名稱(英) Using Resampling to Optimizing Continuous Queries in Wireless Sensor Networks
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    摘要(中) 由於最近在通訊跟電腦技術的進步使得我們能開發出低成本、低秏電、多功能的感測器,這些感測器的體積很小而且具備短距離無線通訊的能力。而一個感測器網路是由數量非常多的感測器所構成的,它可以很密集的部署在我們所以監控環境的四周。感測器網路開啟了一個新的機會讓我們能觀察我們現在所在的這個真實的世界。
    儘管最近在感測器網路應用與技術上的進步,但是感測器網路仍然遭遇到很多因為電力受限所帶來的問題。這是因為大部份的感測器是使用電池來做為電力的來源,而當電池電力秏盡時,很多時候或因為地點的關係是很難去更換這些電池的。當我們了解某些特殊的應用可能會發生的情況、會感測的事物與任務時,我們可能會有辦法去找出一個對於感測器網路更有效率的通訊技術。
    我們目標主要是集中在如何更有效的處理連續查詢,所謂的連續查詢是會更據使用者在下指令時所指定的取樣時間間距,每隔一段取樣時間就傳回一次查詢的結果。在這篇論文當中我們主要會處理二種類型的連續查詢,第一種是要求全部的感測器傳回測量到的資料,第二種則是只針對某些特定的感測器。為了能去處理這種查詢,資料必須依照使用者所指定的一個取樣時間間距持續的傳回基地台,但是這樣會消秏掉大量的電力。先前的論文開發出二種方法來降低電力的消秏。他們主要是藉由使用者可以忍容傳回的資料有多大的誤差來決定是不是要每次都傳回測量到的資料。第一個方法只是單純的記憶住上次傳回的內容,而第二種方法則是使用復雜的多維模型來預測。無論如何,我們提出的這個方法會要求使用者輸入一個可以容忍的誤差範圍。另外,我們假設感測器測量到的資料是精確的,這在現實世界並不一定會是真的。這篇論文是藉由卡門瀘波器來校正跟預測測量到的資料。結果,每一個感測器取樣的取樣時間間距是可以不斷的自動調整的,這是有系統的決定感測器資料測量跟傳輸的取樣時間間距。我們使用現實世界感測器網路所收集到的資料來評估我們所提出的方法是否能比其他方法消秏更少的電力。
    摘要(英) The advances of communication and computer techniques have enabled the development of low-cost, low-power, multifunctional sensor nodes that are small in size and capable of communicating in short distances. A sensor network is composed of a large number of sensor nodes that are densely deployed either inside the phenomenon to be observed or very close to it. Sensor networks open up new opportunities to observe and interact with the physical world around us.
    Despite the recent advances in sensor network applications and technology, sensor networks still suffer from the major problems of limited energy. It is because most sensor nodes use battery as their energy srouce and are inconvenient and sometimes difficult to be replaced when the battery run out. Understanding the events, measures, and tasks required by certain applications has the potential to provide efficient communication techniques for the sensor network.
    Our focus in this work is on the efficient processing of continuous queries, by which query results have to be generated according to the sampling rate specified by the user for an extended period of time. In this thesis, we will deal with two types of continuous queries. The first type of queries requires data from all sensor nodes; while the other is only interested in the data returned by some selected nodes. To answer these queries, data have to be sent to the base station at some designated rate, which may consume much energy. Previous works have developed two methods to reduce the energy consumption. They both base on the error range which the user can tolerate to determine whether current sensing data should be transmitted. While the first uses simple cache method, the second uses complex multi-dimensional model. However, the proposed methods required the user to specify the error range, which may not be easy to specify. In addition, the sensed data reported by the sensors were assumed to be accurate, which is by no means true in the real world. This thesis is based on Kalman filter to correct and predict sensing data. As a result, the sampling frequency of each sensor is dynamically adjusted, referred to as resampling which systematically determine the data sensing/transferring rate of sensors. We evaluate our proposed methods using empirical data collected from a real sensor network.
    關鍵字(中)
  • 卡門
  • 無線感測器網路
  • 連續查詢
  • 關鍵字(英)
  • Kalman filter
  • Continuous Queries
  • Sensor Networks
  • 論文目次 CHAPTER 1 - Introduction 1
    1.1 Background 1
    1.2 Motivation 2
    CHAPTER 2 - Literature review 4
    2.1 Overview of Sensor Networks 4
    2.2 The Queries of sensor network 6
    2.3 Reducing data transferring rate 8
    2.4 Kalman Filter 13
    CHAPTER 3 - Problem Definition 18
    CHAPTER 4 - Our Approach 21
    4.1 Direct Communication Scheme 21
    4.2 In Network Aggregation 27
    CHAPTER 5 - Performance Evaluation 32
    5.1 Experimental Settings 32
    5.11 Description about the dataset 32
    5.2 Experimental Results on Direct Communication Schema 34
    5.2.1 Transmission times and amount of transmission 34
    5.2.2 Quality of Data 36
    5.2.3 Summary 39
    5.3 Experimental Results on Indirect Communication 40
    5.3.1 WHERE clause no involves the measured data 40
    5.3.2 WHERE clause involving the measured data 47
    CHAPTER 6 - Conclusions 51
    6.1 Summary 51
    6.2 Future work 51
    References 52
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    口試委員
  • 鄭炳強 - 召集委員
  • 李偉柏 - 委員
  • 陳嘉玫 - 委員
  • 黃三益 - 指導教授
  • 口試日期 2007-06-22 繳交日期 2007-07-17

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