簡易檢索 / 詳目顯示

回結果列表
研究生： 張力元
Chang, Li-Yuan
論文名稱： 應用於無線感測網路錯誤節點偵測機制之設計與實現
The Design and Implementation of Sensor Fault Detection Schemes for Fusion Center in Wireless Sensor Networks
指導教授： 陳培殷
Chen, Pei-Yin
共同指導教授: 王藏億
Wang, Tsang-Yi
學位類別： 博士
Doctor
系所名稱： 電機資訊學院 - 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年： 2011
畢業學年度： 99
語文別： 英文
論文頁數： 150
中文關鍵詞： 無線感測網路 、錯誤偵測 、決策融合 、分散式估計
外文關鍵詞： wireless sensor network, fault detection, decision fusion, distributed estimation
相關次數： 點閱：116  下載：11 
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

    • 在本論文中，我們針對無線感測網路中的分散式決策融合(decision fusion)機制提出一個有效節點錯誤偵測演算法(collaborative sensor fault detection, CSFD)及低複雜度的近似方法(approximated CSFD, ACSFD)和其對應低成本的硬體實現方式。我們以一個合作式訊號處理的方式來建立決策融合錯誤率的上限。利用這個方式，系統可以有效的偵測出錯誤的感測節點。與傳統的方法做比較，我們提出的方法可以有效的挑選出錯誤的感測節點，大大改善決策融合的效能。此外為了要適合硬體實現，我們對於原本節點錯誤偵測的方法作了改善，並且提出了一個近似的節點錯誤偵測方法，經由實驗的結果證明該近似的方法只需要原本方法約三分之一的計算時間而其決策融合效能相當近似原本提出的軟體方法。接著我們根據這個近似的節點錯誤偵測方法提出了一個低成本的硬體架構。
    然後，本論文亦針對無線感測網路中的分散式估計(distributed estimation)機制提出一個有效節點錯誤偵測演算法(collaborative sensor fault detection, CSFD)及一個低複雜度的近似方法(efficient CSFD, ECSFD)和對應的低成本的硬體實現方式。我們利用同質性檢測(homogeneous testing)的方式來設計一個錯誤節點偵測機制並且提出這個方法的均方差(mean squared error, MSE)下限。經由實驗結果證明這個錯誤節點偵測機制可以有效的移除錯誤的感測節點並且的大幅減少分散式估計的誤差。此外為了要適合硬體實現，我們提出了一個低複雜度的節點錯誤偵測方法，經由實驗的結果證明該低複雜度的節點錯誤偵測方法只需要原本方法約二分之一的計算時間而其分散式估計效能近似原本提出的軟體方法。接著我們根據該低複雜度的節點錯誤偵測方法設計了一個低成本的硬體架構。
    所有的硬體架構之實現是使用Verilog硬體描述語言，然後電路合成則是利用SYNOPSYS的Design Vision以及TSMC的標準元件庫。依據合成結果，我們的設計在硬體成本與速度皆具極佳的競爭力。

    We propose a collaborative sensor fault detection (CSFD) scheme for elimi-nating unreliable local decisions when performing distributed decision fusion in WSNs. A criterion is proposed to determine a set of pseudo-faulty nodes. Perfor-mance evaluation results also indicate that the fault tolerance capability of the proposed approach employing a CSFD scheme is superior to conventional decision fusion. In addition, we propose the approximated collaborative sensor fault detection (ACSFD) scheme for hardware implementation. Given the low circuit complexity, it is suitable for hardware implementation. Simulation results indicate that ACSFD performs better in fault tolerance than the conventional approach.
    In this dissertation, an collaborative sensor fault detection (CSFD) scheme is proposed for distributed estimation in WSNs. In CSFD, the results of a homogenei-ty test are used to identify the faulty nodes within the network such that their quantized messages can be filtered out when estimating the parameter of interest. The simulation results confirm that the accuracy of the estimate obtained from CSFD scheme is significantly better than that obtained from a conventional estimation scheme when applied in sensor networks characterized by an unknown number of sensor faults of various types. Furthermore, we propose an efficient collaborative sensor fault detection (ECSFD) scheme with low computational complexity. Given the low circuit complexity, it is suitable for hardware implementation.
    The VLSI architectures of the proposed design were implemented by using Verilog HDL. We used SYNOPSYS Design Vision to synthesize the designs with TSMC or UMC cell library. Synthesis results demonstrate that our designs have the advantages of low cost and high performance.

    CHAPTER 1 INTRODUCTION 1 1.1 BACKGROUND 1 1.2 RELATED WORK 2 1.3 ORGANIZATION 6 CHAPTER 2 FAULT-TOLERANT DECISION FUSION VIA COLLABORATIVE SENSOR FAULT DETECTION IN WIRELESS SENSOR NETWORKS 7 2.1 INTRODUCTION 7 2.2 SYSTEM MODELS AND PROBLEM FORMULATION 10 2.2.1 Network operation 10 2.2.2 Sensor fault models 13 2.3 COLLABORATIVE SENSOR FAULT DETECTION 15 2.3.1 Collaborative sensor fault detection design 15 2.3.2 The efficient sensor fault detection rule 22 2.4 SIMULATION RESULTS 24 2.4.1 Example 1- Symmetric Distribution 24 2.4.2 Example 2- Asymmetric Distributions 33 2.4.3 Example 3- The Effect of NF on the Performance of CSFD Rules 37 2.4.4 The False Negative and False Positive of CSFD 39 2.5 CONCLUDING REMARKS 41 A LOW-COST VLSI ARCHITECTURE FOR FAULT-TOLERANT FUSION CENTER IN WIRELESS SENSOR NETWORKS 43 3.1 INTRODUCTION 43 3.2 OVERVIEW OF CSFD 44 3.2.1 Measuring Faulty Weight 47 3.2.2 Determining Faulty Nodes 48 3.2.3 Making Decision Fusion 49 3.3 APPROXIMATED CSFD 50 3.4 CHIP ARCHITECTURE FOR ACSFD 56 3.4.1 Multiplication 58 3.4.2 Logarithm 60 3.4.3 Sorting 62 3.5 IMPLEMENTATION RESULTS AND EVALUATION 64 3.6 CONCLUDING REMARKS 72 4.1 INTRODUCTION 73 4.2 SYSTEM MODEL AND PROBLEM STATEMENT 75 4.2.1 Distributed Bayesian Estimation 75 4.2.2 Sensor Fault Types 78 4.3 ROBUST DISTRIBUTED ESTIMATION SCHEME 79 4.4 THEORETICAL ANALYSIS 84 4.5 LOW-COMPLEXITY ROBUST DISTRIBUTED ESTIMATION SCHEME 95 4.6 PERFORMANCE EVALUATION 98 4.7 CONCLUDING REMARKS 108 CHAPTER 5 A LOW-COST VLSI ARCHITECTURE FOR ROBUST DISTRIBUTED ESTIMATION IN WIRELESS SENSOR NETWORKS 110 5.1 INTRODUCTION 110 5.2 OVERVIEW OF CSFD 111 5.2.1 Measuring Faulty Weight 113 5.2.2 Determining Faulty Nodes 114 5.2.3 Making Distributed Estimation 116 5.3 EFFICIENT CSFD 116 5.3.1 Avoid the logarithm and division operations 117 5.3.2 Simplify the integration 120 5.3.3 Transform the numerical integration 121 5.4 CHIP ARCHITECTURE FOR ECSFD 122 5.4.1 Multiplication 122 5.4.2 Logarithm&Antilogarithm 125 5.4.3 Sorting 127 5.5 IMPLEMENTATION RESULTS AND EVALUATIONS 129 5.6 CONCLUDING REMARKS 136 CHAPTER 6 CONCLUSIONS AND FUTURE WORKS 137 6.1 CONCLUSIONS 137 6.2 FUTURE WORKS 138 REFERENCES 140 PUBLICATION LISTS 147

    [1]I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensor networks,” IEEE Communications Magazine, pp. 102–114, August 2002.
    [2]L. Dan, K. D. Wong, H. H. Yu, and A. M. Sayeed, “Detection, classification, and tracking of targets,” IEEE Signal Processing Magazine, vol. 19, pp. 17–29, March 2002.
    [3]B. Chen, R. Jiang, T. Kasetkasem, and P. K. Varshney, “Channel Aware Decision Fusion in Wireless Sensor Networks,” IEEE Trans. Signal Processing, vol. 52, no. 12, pp. 3454–3458, December 2004.
    [4]Y. Yuan and M. Kam, “Distributed decision fusion with a random-access channel for sensor network applications,” IEEE Transactions on Instrumentation and Meas-urement, vol. 53, no. 4, pp. 1339–1344, August 2004.
    [5]J.-F. Chamberland and V. V. Veeravalli, “Asymptotic results for decentralized de-tection in power constrained wireless sensor networks,” IEEE Journal of Selected Areas in Communications, vol. 22, no. 6, pp. 1007–1015, August 2004.
    [6]R. Niu, B. Chen, and P. K. Varshney, “Decision fusion rules in wireless sensor net-works using fading channel statistics,” in 2003 Conference on Information Sciences and Systems, The Johns Hopkins University, March 2003.
    [7]H. Wang, J. Elson, L. Girod, D. Estrin, and K. Yao, “Target classification and local-ization in habitat monitoring,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2003), Hong Kong, China, April 2003.
    [8]A. D’Costa and A. M. Sayeed, “Data versus decision fusion in sensor networks,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2003), Hong Kong, China, April 2003.
    [9]S. A. Aldosari and J. M. F. Moura, “Detection in decentralized sensor networks,” in IEEE International Conference on Accoustics, Speech, and Signal Processing, Montreal, Canada, May 2004.
    [10]B. Chen and P. K. Willet, “On the optimality of the likelihood-ratio test for local sensor decision rules in the presence of nonideal channels,” IEEE Trans. Inform. Theory, vol. 51, no. 2, pp. 693–699, Feb 2005.
    [11]F. Koushanfar, M. Potkonjak, and A. Sangiovanni-Vincentelli, “Fault tolerance in wireless ad-hoc sensor networks,” IEEE Sensors, vol. 24, no. 3, pp. 1491–1496, 2002.
    [12]T.-Y. Wang, Y. S. Han, P. K. Varshney, and P.-N. Chen, “Distributed fault-tolerant classification in wireless sensor networks,” IEEE Journal of Selected Areas in Communications, vol. 23, no. 4, pp. 724–734, April 2005.
    [13]V. V. Veeravalli, T. Basar, and H. V. Poor, “Decentralized sequential detection with a fusion center performing the sequential test,” IEEE Trans. Inform. Theory, vol. 39, no. 2, pp. 433–442, March 1993.
    [14]P. K. Varshney, Distributed Detection and Data Fusion. New York: Springer, 1997.
    [15]Q. Cheng, P. K. Varshney, K. G. Mehrotra, and C. K. Mohan, “Bandwidth man-agement in distributed sequential detection,” IEEE Trans. Inform. Theory, vol. 51, no. 8, pp. 2954–2961, Aug 2005.
    [16]S. Marano, V. Matta, P. Willett, and L. Tong, “Sprts in sensor networks with mobile agents,” in 2005 IEEE 6th Workshop on Signal Processing Advances in Wireless Communications, June 2005, pp. 920–924.
    [17]A. Wald, Sequential Analysis. John Wiley & Sons, Inc., 1947.
    [18]A. S. Willsky, “A survey of design methods for failure detection in dynamic sys-tems,” Automatica Pergamon Press, vol. 12, pp. 601–611, 1976.
    [19]M. Basseville, “Detecting changes in signals and systems– a survey,” Automatica Pergamon Press, vol. 24, no. 3, pp. 309–326, 1988.
    [20]M. Basseville and I. Nikiforov, Detection of abrupt changes- Theory and applica-tions. Prentice-Hall, 1993.
    [21]Q. Cheng, P. K. Varshney, J. Michels, and C. M. Belcastro, “Distributed fault detec-tion via particle filtering and decision fusion,” in 2005 8th International Conference on Information Fusion, vol. 2, Philadelphia, USA, July 2005, pp. 1239–1246.
    [22]F. koushanfar, M. Potkonjak, and A. Sangiovanni-Vincentelli, “On-line fault detec-tion of sensor measurement,” in Proceedings of IEEE, vol. 2, 2003, pp. 974–979.
    [23]P.-N. Chen and A. Papamarcou, “New asymtotic results in parallel distributed detec-tion,” IEEE Trans. Inform. Theory, vol. 39, no. 6, pp. 1847–1863, November 1993.
    [24]S. M. Ali and S. D. Silvey, “A general class of coefficients of divergence of one dis-tribution from another,” J. Royal Stat. Soc., Series B, vol. 28, pp. 131–143, 1996.
    [25]H. V. Poor, An Introduction to Signal Detection and Estimation. New York: Springer, 1994.
    [26]T. M. Cover and J. A. Thomas, Elements of Information Theory. USA: John Wiley & Sons, Inc., 1991.
    [27]T.-Y. Wang, L.-Y. Chang, D.-R. Duh, and J.-Y. Wu, “Fault-Tolerant Decision Fusion via Collaborative Sensor Fault Detection in Wireless Sensor Networks”, IEEE Trans. Wireless Commun., vol. 7, no 2, pp. 756–768, Feb. 2008.
    [28]S. Diao, Y. Zheng, and C.-H. Heng, “A CMOS Ultra Low-Power and Highly Efficient UWB-IR Transmitter for WPAN Applications,” IEEE. Trans. Circuits Syst. II, Exp. Briefs, vol. 56, no. 3, pp. 200–204, 2009.
    [29]M. Verhelst and W. Dehaene, “Analysis of the QAC IR-UWB Receiver for Low Energy, Low Data-Rate Communications,” IEEE. Trans. Circuits Syst. I, Reg. Papers, vol. 55, no. 8, pp. 2423–2432, 2008.
    [30]C.-S A. Gong, M.-T. Shiue, K.-W. Yao, T.-Y. Chen, Y. Chang, and C.-H. Su, “A Truly Low-Cost High-Efficiency ASK Demodulator Based on Self-Sampling Scheme for Bioimplantable Applications,” IEEE. Trans. Circuits Syst. I, Reg. Papers, vol. 55, no.6, pp. 1464–1477, 2008.
    [31]C. Alippi and C. Galperti, “An Adaptive System for Optimal Solar Energy Harvesting in Wireless Sensor Network Nodes,” IEEE. Trans. Circuits Syst. I, Reg. Papers, vol. 55, no. 6, pp. 1742–1750, 2008.
    [32]R. Aguilar-Ponce, J. Tessier, A. Baker, C. Emmela, J. Das, J. L. Tecpanecatl-Xihuitl, A. Kumar, and M. Bayoumi, “VLSI Architecture for An Object Change Detector for Visual Sensors,” IEEE Workshop on Signal Processing Systems Design and Implementation, pp. 290–295, 2005.
    [33]D. H. Goldberg, A. G. Andreou, P. Julian, P. O. Pouliquen, L. Riddle, and R. Rosasco, “A Wake­Up Detector for an Acoustic Surveillance Sensor Network: Algorithm and VLSI Implementation, IPSN’04, pp. 134–141, 2004.
    [34]R. D. Yates and D. J. Goodman, Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, John Wiley & Sons, 1999.
    [35]G. Casella and R. L. Berger, Statistical Inference. Thomson, 2001.
    [36]W.-M. Lam and A. R. Reibman, “Design of quantizers for decentralized estimation systems,” IEEE Transactions on Communications, vol. 41, no. 11, pp. 1602–1605, November 1993.
    [37]J. A. Gubner, “Distributed estimation and quantization,” IEEE Trans. Inform. Theo-ry, vol. 39, no. 4, pp. 1456–1459, July 1993.
    [38]V. Megalooikonomou and Y. Yesha, “Quantizer design for distributed estimation with communication constraints and unknown observation statistics,” IEEE Trans-actions on Communications, vol. 48, no. 2, pp. 181–184, February 2000.
    [39]D. Blatt and A. Hero, “Distributed maximum likelihood estimation for sensor net-works,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., vol. 3, May 2004, pp. 929–932.
    [40]Y. Zhu, E. Song, J. Zhou, and Z. You, “Optimal dimensionality reduction of sensor data in multisensor estimation fusion,” IEEE Trans. Signal Process., vol. 53, no. 5, pp. 1631–1639, May 2005.
    [41]Z.-Q. Luo, “An isotropic universal decentralized estimation scheme for a bandwidth constrained ad hoc sensor network,” IEEE Journal of Selected Areas in Communi-cations, vol. 23, no. 4, pp. 735–744, April 2005.
    [42]——, “Universal decentralized estimation in a bandwidth constrained sensor net-work,” IEEE Trans. Inform. Theory, vol. 51, no. 6, pp. 2210–2219, June 2005.
    [43]R. Niu and P. K. Varshney, “Target location estimation in sensor networks with quantized data,” IEEE Transactions on signal processing, vol. 54, no. 12, pp. 4519–4528, December 2006.
    [44]A. Ribeiro and G. B. Giannakis, “Bandwidth-constrained distributed estimation for wireless sensor networks– part i: Gaussian case,” IEEE Trans. Signal Processing, vol. 54, no. 7, pp. 2784–2796, July 2006.
    [45]——, “Bandwidth-constrained distributed estimation for wireless sensor networks– part ii: Unknown probability density function,” IEEE Trans. Signal Processing, vol. 54, no. 7, pp. 2784–2796, July 2006.
    [46]C. Shen, C. Srisathapornphat, and C. Jaikaeo, “Sensor information networking ar-chitecture and applications,” IEEE Personal Communications, vol. 8, no. 4, pp. 52–59, August 2001.
    [47]I. Rapoport and Y. Oshman, “A new estimation error lower bound for interruption indicators in systems with uncertain measurements,” IEEE Trans. Inf. Theory, vol. 50, no. 12, pp. 3375–3384, December 2004.
    [48]V. Delouille, R. N. Neelamani, and R. G. Baraniuk, “Robust distributed estimation using the embedded subgraphs algorithm,” IEEE Trans. Signal Processing, vol. 54, no. 8, pp. 2998–3010, August 2006.
    [49]P. Ishwar, R. Puri, K. Ramchandran, and S. S. Pradhan, “On rateconstrained distrib-uted estimation in unreliable sensor networks,” IEEE Journal of Selected Areas in Communications, vol. 23, no. 4, pp. 765–775, April 2005.
    [50]E. Ertin, R. Moses, and L. Potter, “Network parameter estimation with detection failures,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., vol. 2, May 2004, pp. 273–276.
    [51]R. C. Elandt-Johnson, Probability Models and Statistical Methods in Genetics. New York, London, Sydney, Toronto: John Wiley &Sons, Inc., 1971.
    [52]R. L. Plackett, “Karl Pearson and the chi-squared test,” International Statistical Re-view, vol. 59-72, no. 1, pp. 639–654, April 1983.
    [53]H. Cram´er, Mathematical methods of statistics. USA: Princeton University Press, 1999.
    [54]H. Chernoff and E. L. Lehmann, “The use of maximum likelihood estimates in _2 tests for goodness of fit,” Ann. Math. Statist., vol. 25, pp. 579–586, 1954.
    [55]R. J. Larsen and M. L. Marx, An introduction to mathematical statistics and its ap-plications. New Jersey: Prentice Hall, 2001.
    [56]M. D. Srinath, P. K. Rajasekaran, and R. Viswanathan, Introduction to statistical signal processing with applications. New Jersey: Prentice Hall, 1996.
    [57]S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory. New Jersey: Prentice Hall, 1993.
    [58]K. Knopp, Infinite Sequences and Series. New York: Dover Publications, 1956.
    [59]S. P. Lloyd, “Least squares quantization in PCM,” IEEE Trans. Inform. Theory, vol. IT-28, pp. 129–136, March 1982.
    [60]Tsang-Yi Wang, Li-Yuan Chang, and Pi-Yin Chen, “A Collaborative Sensor-Fault Detection Scheme for Robust Distributed Estimation in Sensor Networks,” IEEE Transactions on Communications. vol.57, no.10, pp. 3045-3058, October 2009.

    下載圖示 校內：2016-02-11公開
    校外：2016-02-11公開
    QR CODE