在這篇論文中，我們考慮無線感測網路的分散式估測問題。主要討論審查機制與融合中心是否已知感測器個數，並比較其效能，其中，使用審查機制能有效減少感測器欲傳送的感測值。在一個無線感測網路系統中，包含數個融合中心及無線感測器，融合中心會接收由感測器傳送到的測量值作估測；而感測器節點的能源、傳輸範圍以及傳輸頻寬都會
受到限制，為了要充分地利用感測器之處理及儲存訊號的能力，同時又要延長電池的使用壽命並且更有效地利用傳輸資源，因此我們設計一個審查機制能在本論文所提出的非相同區間有效的提升其效能。所使用的審查機制只讓部份感測器的數據從無線感測網路傳送到融合中心，而融合中心接收審查後之資料再以最大概似估測法估測感興趣的訊號參
數。因此，本論文所討論的參數估測法稱為審查機制下之最大概似估測法。我們比較兩種審查機制：一為每個感測器有著相同審查區間；二為每個感測器有著非相同審查區間。此外，由電腦模擬結果驗證，在融合中心未知感測器個數時，非相同區間之審查機制下比相同審查區間更能有效地改善系統效能，進而節省更多的能源。

In this thesis, we consider the distributed estimation problem in wireless sensor networks (WSNs). This work focuses on the censoring scheme, which has the ability of reducing the number of active sensor. We also discuss the issue regarding if the number of sensors is known to the fusion center. A WSN usually consists of a fusion center and some sensor nodes, in which the fusion center estimates the measurement transmitted from the nearby sensor nodes. The sensor nodes are battery powered devices, and hence they have limited power, limited radio communication range, and limited transmission bandwidth. In order to use the local processing and storage capabilities at the sensor nodes while extending battery life as well as finding more efficient ways of utilizing transmission resources, we employ the censoring scheme to reduce communication data when designing the distributed estimation method in WSNs. We compare the performance of the censoring scheme utilizing the identical censoring interval with the non-identical censoring intervals among sensors. Numerical simulations shows that utilizing the non-identical censoring interval can significantly improve the estimation performance. In addition, the improvement of the system performance where the number of sensors is known to FC is greater than the system where the number is unknown to FC when the non-identical censoring interval approach is utilized.

目錄
摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
第一章 序論 1
1.1 引言 1
1.2 本文架構 4
第二章 系統模型 5
2.1 無線感測網路系統 6
2.2 審查機制 7
第三章 審查機制下之最大概似估測器 11
3.1 融合中心已知感測器個數 11
3.2 融合中心未知感測器個數 13
第四章 參數設定與模擬結果 14
4.1 模擬參數設定 14
4.2 模擬結果 17
第五章 結論 27
參考文獻 28
附錄 31
附錄A 31
附錄B 32
附錄C 33

[1] T. Wark, P. Corke, P. Sikka, L. Klingbeil, Y. Guo, C. Crossman, P. Valencia, D. Swain, and G. Bishop-Hurley, “Transforming agriculture through pervasive wireless sensor networks,” IEEE Pervasive Computing, vol. 6, no. 2, pp. 50–57, June 2007.
[2] J. P. Lynch and K. J. Loh, “A summary review of wireless sensors and sensor networks for structural health monitoring,” Shock and Vibration Digest, vol. 38, no. 2, pp. 91–130, March 2006.
[3] A. Mainwaring, D. Culler, J. Polastre, R. Szewczyk, and J. Anderson, “Wireless sensor networks for habitat monitoring,” in Proc. the 1st ACM International Workshop on Wireless Sensor Networks and Applications, New York, September 2002, pp. 88–97.
[4] T. Arampatzis, J. Lygeros, and S. Manesis, “A survey of applications of wireless sensors and wireless sensor networks,” in Proc. IEEE International Symposium on Intelligent Control, Limassol, Cyprus, June 2005, pp. 719–724.
[5] K. Lorincz, D. J. Malan, T. R. F. Fulford-Jones, A. Nawoj, A. Clavel, V. Shnayder, G. Mainland, M. Welsh, and S. Moulton, “Sensor networks for emergency response: Challenges and opportunities,” IEEE Pervasive Computing, vol. 3, no. 4, pp. 16–23, July 2004.
[6] C. Otto, A. Milenkovia, C. Sanders, and E. Jovanov, “System architecture of a wireless body area sensor network for ubiquitous health monitoring,” Mobile Multimedia, vol. 1, no. 4, pp. 307–326, April 2006.
[7] K. Gill, S.-H. Yang, and W. Wang, “Scheme for preventing low-level denial-ofservice attacks on wireless sensor network-based home automation systems,” IET Wireless Sensor Systems, vol. 2, no. 4, pp. 361–368, December 2012.
[8] S. Coleri and P. Varaiya, “Fault tolerance and energy efficiency of data aggregation schemes for sensor networks,” in Proc. IEEE 60th Vehicular Technology Conference, vol. 4, July 2004, pp. 2925–2929.
[9] E. J. Msechu and G. B. Giannakis, “Distributed measurement censoring for estimation with wireless sensor networks,” in Proc. IEEE 12th International Workshop on Signal Processing Advances in Wireless Communications, San Francisco, CA, June 2011, pp. 176–180.
[10] A. Krause, A. Singh, and C. Guestrin, “Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies,” Machine Learning Research, vol. 9, pp. 235–284, February 2008.
[11] D. J. C. MacKay, “Information-based objective functions for active data selection,” Neural Computation, vol. 4, no. 4, pp. 590–604, July 1992.
[12] J. A. Gubner, “Distributed estimation and quantization,” IEEE Transactions on Information Theory, vol. 39, no. 4, pp. 1456–1459, July 1993.
[13] A. Ribeiro and G. B. Giannakis, “Bandwidth-constrained distributed estimation for wireless sensor networks–Part I: Gaussian case,” IEEE Transactions on Signal Processing, vol. 54, no. 3, pp. 1131–1143, March 2006.
[14] E. J. Msechu and G. B. Giannakis, “Sensor-centric data reduction for estimation with WSNs via censoring and quantization,” IEEE Transactions on Signal Processing, vol. 60, no. 1, pp. 400–414, January 2012.
[15] P. Addesso, S. Marano, and V. Matta, “Sequential sampling in sensor networks for detection with censoring nodes,” IEEE Transactions on Signal Processing, vol. 55, no. 11, pp. 5497–5505, November 2007.
[16] S. Chaudhari, J. Lunden, V. Koivunen, and H. V. Poor, “Cooperative sensing with imperfect reporting channels: Hard decisions or soft deisions?” IEEE Transactions on Signal Processing, vol. 60, no. 1, pp. 18–28, January 2012.
[17] C. Rago, P. Willett, and Y. Bar-Shalom, “Censoring sensors: A lowcommunication-rate scheme for distributed detection,” IEEE Transactions on Aerospace Electronic Systems, vol. 32, no. 2, pp. 554–568, April 1996.
[18] D. P. Bertsekas, Nonlinear Programming. Belmont, MA: Athena Scientific, 1999.