簡易檢索 / 詳目顯示

研究生: 梁孝全
Hsiao-Chuan Liang
論文名稱: 應用在無線感測網路之高效率低耗能極值監測
Packet Reduction on the Monitoring of Extreme Values on WSNs
指導教授: 王家祥
Jia-Shung Wang
口試委員:
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年: 2008
畢業學年度: 96
語文別: 英文
論文頁數: 62
中文關鍵詞: Sensor NetworksMaximumCUSUM
相關次數: 點閱:2下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 無線感測網路因為其應用的廣泛性在近年來受到許多研究團隊的注意,其應用有:核子反應爐監測、災難現場監視控、工廠溫度監測等等。而在這些應用之中,最重要的問題就是極值監測。因此,設計一個節能的極值監控演算法便成為了我們的研究主題。在過去的研究證明了Hierarchy Adaptive Thresholds (HAT) 可以有效率的進行極值監測。HAT利用樹狀結構來過濾掉不可能成為極值的數值。更甚者,HAT還可以利用這個結構來過濾掉不必要的詢問封包(Query Packets)。然而,HAT雖然在極值監控有傑出的表現,它依然有許多可以被改進的空間。我們提出一個演算法不止可以像HAT一樣可以利用樹狀結構過濾掉不必要的封包,而且我們會依據過去的歷史資料來進行各個感測器的回報速率調節。除此之外,我們還利用分散回報的方式來減輕MAX node的父節點的流量負擔。更甚者,我們還利用適時的傳送調節速率的封包而不是隨時隨地都傳送調節速率的封包來降低我們所產生出來的額外能量消耗。根據我們的實驗指出,本篇論文所提出的演算法可以比HAT降低15%到19%左右的封包傳送。而因為這些減少傳送的封包所產生的錯誤也可以維持在1%之下。另外,我們也利用分散回報來降低MAX node所產生的龐大封包量。再者,我們還利用線性回歸以及CUSUM來判斷我們什麼時候該傳送調節速率的封包。利用這樣的方法,我們可以減少相對於隨時隨地傳送調節封包的方式大約60%到70%左右的額外能量消耗。


    中文摘要 I Abstract II 致謝 III Table of Contents IV List of Figures V List of Tables VI Chapter 1. Introduction 1 Chapter 2. Related Works 5 2-1. Temporal Suppression 5 2-2. Range Caching 7 2-3. Single-Level Adaptive Thresholds 8 2-4. Single-Level Adaptive Thresholds with Aggregation 10 Chapter 3. Proposed Extreme Values Monitoring Algorithm 12 3-1. Overview 12 3-2. Hierarchy Adaptive Thresholds 14 3-2-1. Booting Stage 16 3-2-2. Node initiated reporting stage 16 3-2-3. Root initiated query Stage 17 3-3. Classification of Important and Unimportant Nodes 20 3-4. Reporting Rate Adjustment Strategy 21 3-4-1. Round Basis 22 3-4-2. Moving Average and Linear Regression methods 23 3-4-3. A Fast Detection Algorithm on Popularity Modeling 26 3-5. Architecture of Proposed Algorithm 35 Chapter 4. Experimental Results 37 4-1. Basic HAT demonstration 38 4-2. The Performance of Proposed Algorithm 40 4-2-1. Data Segment 1 41 4-2-2. Data Segment 2 52 Chapter 5. Conclusion 60 References 61

    [1]. A. Silberstein, K. Munagala, and J. Yang, “Energy-Efficient Monitoring of Extreme Values in Sensor Networks,” in Proc. ACM SIGMOD Int’l Conf. on Management of Data, pp. 169-180, 2006.
    [2]. C. Olston, B. Loo, and J. Widom, “Adaptive Precision Setting for Cached Approximate Values,” in Proc. ACM SIGMOD Int’l. Conf. on Management of Data, Santa Barbara, California, USA, May 2001.
    [3]. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A Survey on Sensor Networks,” IEEE communications magazine, vol. 40, pp. 102–114, Aug 2002.
    [4]. C. Chong and S. P. Kumar, “Sensor networks: Evolution, opportunities, and challenges,” in Proc. of IEEE, vol. 91, no. 8, pp. 1247–1256, Aug 2003.
    [5]. C. Wang, S. Eisenman, and A. Campbell, “CODA: Congestion Detection and Avoidance in Sensor Networks,” in Proc. Int’l Conf. on Embedded Networked Sensor Systems (ACM SenSys’ 03), 2003.
    [6]. Y. Sankarasubramaniam, O. Akan, and I. Akyidiz, “ESRT: Event-to-Sink Reliable Transport in Wireless Sensor Networks,” in Proc. ACM Int’l Symp. On Mobile Ad Hoc Networking and Computing(ACM MobiHoc’03), 2003.
    [7]. C.T. Ee and R.Bajcsy, “Congestion Control and Fairness for May-to-One Routing in Sensor Networks,” in Proc. Int’l Conf. on Embedded Networked Sensor Systems (ACM SenSys’ 04), Nov. 2004.
    [8]. C. Wang, B. Li, K, Sohraby, M. Daneshmand, and Y. Hu., “Upstream Congestion Control in Wireless Sensor Networks Through Cross-Layer Optimization,” IEEE journal on Selected Areas in Communications, vol. 25, NO. 4, May 2007.
    [9]. B. Hull, K. Jamieson, and H. Balakrishnan., “Mitigating Congestion in Wireless Sensor Networks,” in Proc. Int’l Conf. on Embedded Networked Sensor Systems (ACM SenSys’ 04), 2004.
    [10]. K. Karenos, V. Kalogeraki, and S. V. Krishnamurthy., “Cluster-based Congestion Control for Supporting Multiple Classes of Traffic in Sensor Networks,” in Proc. 2nd IEEE workshop on Embedded Networked Sensors, 2005.
    [11]. Y. S. Hsu. “A Fast Detection Algorithm on Popularity Modeling”, July 2007. M.S. thesis, Taiwan, National Tsing Hua University.
    [12]. J. Gao, and L. Zhang., “Load Balanced Shortest Path Routing in Wireless Networks,” in Proc. IEEE Int’l Conf. on Computer Communications (INFOCOM), 2004
    [13]. Intel Berkeley Research Lab. http://berkeley.intel-research.net/labdata/
    [14]. The Network Simulator ns-2. http://www.isi.edu/nsnam/ns
    [15]. S. Makridakis, S. C. Wheelwright, and R. J. Hyndman, Forecasting: Methods and Applications -3rd edition, John Wiley and Sons, Inc., 1998
    [16]. C. C. Zou, W. Gong, D. Towsley, and L. Gao, “The Monitoring and Early Detection Internet Worms,” in IEEE/ACM Trans. on Networking, Oct. 2005.
    [17]. H. Wang, D. Zhang, and K.G. Shin, “Change-Point Monitoring for Detection of DOS Attacks,” in IEEE Trans. on Dependable and Secure Computing, vol. 1, no. 4, 2004.
    [18]. Z. Chen, L. Gao, and K. Kwiat, “Modeling the Spread of Active Worm,” in Proc. IEEE Int’l Conf. on Computer Communications (INFOCOM), 2003.
    [19]. J. O. Kephart, and S. R. White, “Measuring and Modeling Computer Virus prevalence,” in Proc. IEEE Symp. on Security and Privacy, pp. 2-15, 1993.
    [20]. T. Bu, A. Chen, S. V. Wiel, and T. Woo, “Design and Evaluation of a Fast and Robust Worm Detection Algorithm,” in Proc. IEEE Int’l Conf. on Computer Communications (INFOCOM), 2006.
    [21]. Crossbow Inc. MRP-Mote Processor RadioBoard Users’s Manual.

    無法下載圖示 全文公開日期 本全文未授權公開 (校內網路)
    全文公開日期 本全文未授權公開 (校外網路)

    QR CODE