臺灣博碩士論文加值系統

電池為電力來源的無線感測器網路，如今廣泛的應用在各式各樣的偵測應用上。
感測器最大的能源消耗是花費在無線傳輸上。
我們設計出高能源效率的演算法來節省花費在無線傳輸上的能源消耗，並且有效的降低系統當事件發生判斷錯誤的機率且假警報亦是在系統容忍的範圍。
然而當系統花在事件發生判斷錯誤的成本比假警報發生的成本越多時，我們的演算法能夠減少系統總共所花的成本。
我們假設每一個感測器只有在觀測值可信賴時才傳送資料，當感測器的觀測值不可信賴時就不傳送資料。
也因為減少傳送資料的次數，所以感測器就節省能源。
另外我們亦假設當感測器處於較差的環境，接收到的訊號強度有所衰減，
這時利用設計出來的演算法可以快速使得系統當事件發生判斷錯誤的機率下降，快速提高系統的正確率。

The Battery-powered wireless sensor network (WSN) is an emerging technology with a wide variety of applications.
One of the major consumptions of energy in WSN is spent on the data communication.
A highly energy-efficient algorithm is designed in this research to save the expenditure in the wireless transmission energy consumption.
In the algorithm, each sensor transmits data only when its observation is reliable, or the sensor remains in a latent status which is defined "mute", and hence power saving is achieved.
The chance that a miss occurs can be highly reduced while the rate of a false alarm is in the rage which the system is able to tolerate with.
In addition, when the cost caused by a miss is significantly greater than that of a false alarm, the total cost of the WSN system can
also be repressed by the proposed mute algorithm.
Furthermore, when the reception of the sensors is attenuated, the error probability of the final decision at the fusion center
is lower that that of the conventional WSN system since the information from the unreliable observation is eliminated through the mute algorithm.

1. 簡介
1.1 簡介無線感測器網路
1.2 簡介 Distributed Detection 的歷史
1.3 簡介在無線感測器網路上的 Distributed Detection
1.4 本文架構
2. 系統架構
2.1 初步架構
2.2 Optimal LR-based Fusion Rule
2.3 Suboptimal Fusion Rule
2.3.1 Two-stage Chair-Varshney Fusion Rule
2.3.2 MAJORITY Fusion Rule
2.3.3 Maximum Ratio Combining Fusion Rule
2.3.4 Equal Gain Combining Fusion Rule
2.4 三種 Suboptimal Fusion Rules 使用
3. Mute 機制
3.1 問題陳述
3.2 mute 機制的建立
3.3 調整感測器對應的 FC 的 decision rule
3.3.1 AWGN channel 的 decision rule
3.3.2 Rayleigh Fading channel 的 decision rule
3.4 使用 mute 機制對系統效能的影響
3.5 調整 H1 出現的機率
4. mute 機制的延伸
4.1 問題探討
4.2 mute 區間的比例
4.3 cost criteria
4.4 改變 likelihood ratio 的 threshold
4.5 感測器接收到的訊號有所衰減
4.6 當感測器接收到的訊號強度有所衰減分別去模擬六種方法
4.6.1 第一種情況:當感測器接收到的訊號強度衰減比較大
4.6.2 第二種情況:當感測器接收到的訊號強度衰減只有稍微衰減
5. 結論

[1] S. Appadwedula, Venugopal V. Veeravalli, and Douglas L. Jones, “Engergy-efficient detection in sensor networks,” vol. 23, no. 4, pp. 693–702, APRIL 2005.
[2] Tsang-Yi Wang, Yunghsiang S. Han, Pramod K. Varshney, and Po-Ning Chen,“Distributed fuault-tolerant classification in wireless sensor networks,” vol. 23,no. 4, pp. 724–734, APRIL 2005.
[3] V. Raghunathan, C. Schurgers, S. Park, and M. B. Srivastava, “Energy-aware wireless microsensor networks,” pp. 40–50, March 2002.
[4] J. Chen, “Distributed energy-efficient decision fusion in wireless sensor networks,”Master’s thesis, NCNU, July 2005.
[5] R. R. Tenney and N. R. Sandell Jr., “Detection with distributed sensors,” pp.501–510, July 1981.
[6] ——, “Strategies for distributed decision making,” pp. 527–538, August 1981.
[7] Z. Chair and P. K. Varshney, “Optimal data fusion in multiple sensor detection systems,” pp. 98–101, January 1986.
[8] ——, “Neyman-pearson hypothesis testing in distributed networks,” Proceedings of the 26th IEEE Conference on Decision and Control, pp. 1842–1843, January 1987.
[9] ——, “Distributed bayesian hypothesis testing with distributed data fusion,” IEEE Trans. Syst. Man., pp. 695–699, Sep.-Oct. 1988.
[10] I. Y. Hoballah and P. K. Varshney, “Neyman-pearson detection with distributed sensors,” in Proceedings of the 25th IEEE Conference on Decision and Control, pp. 237–241, 1986.
[11] ——, “An information theoretic appraoch to the distributed detection problem,”pp. 988–994, September 1989.
[12] ——, “Distributed bayesian signal detection,” pp. 995–1000, September 1989.
[13] A. R. Reibman and L. W. Nolte, “Optimal detection and performance of dis-tributed sensor systems,” IEEE Trans. Aerosp. Electron. Syst., pp. 24–30, January 1987.
[14] ——, “Design and performance comparison of distributed detection networks,”IEEE Trans. Aerosp. Electron. Syst., pp. 789–797, November 1987.
[15] R. Srinivasan, “Theory of distributed detection,” Signal Processing, vol. 11, no. 4, pp. 319–327, December 1986.
[16] P. K. Varshney, Distributed Detection and Data Fusion. New York: Springer-Verlag, 1997.
[17] R. Viswanathan and P. K. Varshney, “Distributed detection with multiple sensors-part i: Fundamentals,” in Proc. IEEE, vol. 85, no. 1, January 1997, pp. 54–63.
[18] R. S. Blum, S. A. Kassam, and H. V. Poor, “Distributed detection with multiple sensors- part ii: Advanced topics,” in Proc. IEEE, vol. 85, no. 1, January 1997,
pp. 64–79.
[19] J. N. Tsitsiklis, “Decentralized detection,” in Advances in Statistical Signal Processing , vol. 2, 1993, pp. 297–344.
[20] Y. Lin, “Decision fusion rules in multi-hop wireless sensor networks,” IEEE Transactiona on Aerospace and Electronic Systems, vol. 41, no. 2, APRIL 2005.
[21] Rohit U. Nabar, H. Bolcskei, and Felix W. Kneubuhler, “Fading relay channels: Performance limits and spacetime signal design,” vol. 22, no. 6, pp. 1099–1109,
AUGUST 2004.
[22] Jayesh H. Kotecha, V. Ramachandran, and Akbar M. Sayeed, “Distributed mul-titarget classification in wireless sensor networks,” vol. 23, no. 4, pp. 703–713,APRIL 2005.
[23] B. Chen, R. Jiang, T. Kasetkasem, and P. K. Varshney, “Channel aware decision fusion in wireless sensor networks,” vol. 52, no. 12, pp. 3454–3458, December 2004.
[24] C. Rago, P.Willett, and Y. Bar-Shalom, “Censoring sensors:a low-communication-rate scheme for distributed detection,” vol. 32, no. 2, pp. 554–568, April 1996.