無線感測網路近年來被廣泛地應用於安全、環境監控與目標追蹤及偵測，進而去獲取週遭環境資訊與執行數據計算。而許多研究都是使用靜態感測器來進行偵測，但即使靜態感測器已足夠應付許多偵測上的需求，仍然會碰上不適用的環境，比如在某個廣大區域中佈署大量感測器來進行偵測。在這樣的環境下，行動感測器能用來解決通訊以及偵測覆蓋問題。因此，本論文使用行動感測器來進行目標物偵測。針對入侵者或目標物偵測此一應用，如何有效率地進行即時性偵測，對於目標物偵測應用是個很重要的議題。在過去許多研究中，大多使用單一感測節點進行目標物偵測，並且僅使用單一位元的偵測結果作為最後判斷依據，雖然該方法簡單且容易實作，但容易受到環境中的雜訊干擾，使得誤警機率提高，降低目標物偵測的效能。因此，本篇論文利用多個感測器來進行合作偵測，這樣的偵測機制適應於雜訊干擾的存在，以及訊號會隨著距離衰減之情況，提供了比單一感測節點更加可靠的偵測機制。
在目標物偵測研究中有個重要的議題叫做曝光(Exposure)，用來衡量一個區域是如何的被感測網路所覆蓋，也就是感測器的佈署品質，越高的曝光也代表著越好的佈署品質，也就是越好的偵測效果。然而，傳統的曝光議題大多在靜態感測網路中討論，故這篇論文將會重新定義並且計算行動感測網路中的曝光，並在環境中可能存在障礙物的情形下。針對這個問題本論文提出了一個以資料融合為基礎之偵測機制，發展出一個均勻最強偵測器(Uniformly Most Powerful Detector,UMP)用來在行動感測網路中進行目標物偵測，並且為了與實際環境相符，偵測的進行是在不知道目標物的情況以及移動方向此前提下。同時，由於沒有關於節點位置的固定模式，我們提出一個時間擴張圖的方法來計算曝光，而由於曝光的計算成本高昂，我們同時提出計算曝光的上限(Upper bound)與下限(Lower bound)的演算法。
在實驗模擬中，我們使用一個以數值融合為基礎之偵測演算法作為我們的比較對象，而實驗結果也充分顯示出UMP的結果是優於數值融合的，並能有效的決定出曝光的上限與下限，並適用在任意的感測器路由或是有障礙物的環境下。同時進行實際的實驗模擬一千次並取其平均值當作實驗值，利用實驗值來驗證我們方法理論值的正確性，並比較誤警機率與訊號強度的變化對曝光的影響。

Recently, wireless sensor networks (WSNs) are extensively utilized in security surveillance, environmental monitoring and target tracking/detection. Although a stationary sensor network is often adequate to meet application requirements, it is not suited to many situations. For example, a huge number of nodes are required to monitor a large region. In such situations, mobile sensor networks can be used to resolve the communication and sensing coverage problems. This paper addresses the problem of detecting a target using mobile sensor networks. In target detection, one important issue is to perform detection efficiently and reduce energy consumption in transmission. Most of past studies adopted a disc model to target detection and use only 1‐bit to represent the detection made by the system. Although using the trivial detection strategy is easy to implement, the detection performance may be decreased due to high false alarm probability caused by noise. In this paper, we propose a collaborative sequential detection with multiple sensors. The detection strategy is applied to the environment where noise and signal attenuation coexists. One of the fundamental issues in target detection problems is exposure, which measures how the region is covered by the sensor network. While traditional studies focus on stationary sensor networks, this thesis formally defines and evaluates exposure in mobile sensor networks with the presence of obstacles and noises. We proposed a method for this problem named “Uniformly Most Powerful Detector(UMP)”. To conform with practical situations, detection is conducted without presuming the target’s activities and moving directions. As there is no fixed layout of node positions, a time expansion technique is developed to evaluate exposure. Since determining exposure can be computationally expensive, algorithms to calculate the upper and lower bounds on exposure are developed.
In simulation, we compare our results to a strategy which based on value fusion. Simulation results show that our method gets better performance than value fusion strategy and indicate that the strategy can determine the upper and lower bounds on exposure for any sensor route plan and sensing schedule with and without the presence of obstacles. We also use the simulation result to verify our analytical solution.

[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, “Wireless sensor networks: a survey,” Computer Networks, vol. 38, no. 4, pp. 393-422, Mar. 2002.
[2] D. Estrin, L. Girod, G. Pottie, and M. Srivastava, "Instrumenting the world with wireless sensor networks," in Proc. of ICASSP, vol. 4, pp. 2003-2036, 2001.
[3] V. Phipatanasuphorn, and R. Parameswaran, “Vulnerability of sensor networks to unauthorized traversal and monitoring,” IEEE Transactions on Computers, vol. 53, no. 3, pp. 364-369, Mar. 2004.
[4] P. Juang, H. Oki, Y. Wang, M. Martonosi, L. Peh, and D. Rubenstein, “Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet,” in Proc. of the 10th international conference on Architectural support for programming languages and operating systems, pp. 96-107, 2002.
[5] J. Chen, S. Kher, and A. Somani, “Distributed fault detection of wireless sensor networks,” in Proc. of the 2006 workshop on Dependability issues in wireless ad hoc networks and sensor networks, Los Angeles, CA, USA pp. 65-72, 2006.
[6] P. Santi and D. M. Blough, “The critical transmitting range for connectivity in sparse wireless ad hoc networks,” IEEE Transactions on Mobile Computing, vol. 2, Jan. 2003.
[7] T.-L. Chin, P. Ramanathan, K.K. Saluja and K.C. Wang, "Exposure for Collaborative Detection Using Mobile Sensor Networks," Proc. Int’l Conf. Mobile Ad-Hoc and Sensor Systems,pp. 1-8, Nov. 2005.
[8] S. Kumar, F. Zhao, and D.Shepherd, “Collaborative Signal and Information Processing in Microsensor Networks,” in IEEE Signal Processing Magazine, vol. 19,pp. 13‐14, Mar. 2002.
[9] P. K. Varshney, Distributed Detection and Data Fusion, Springer, New York, NY, USA, 1997.
[10] T.-L. Chin and Y.-H. Hu, “Optimal detector based on data fusion for wireless sensor networks,”in GLOBECOM, Dec. 2011.
[11] S. Meguerdichian, F. Koushanfar, G. Qu, and M. Potkonjak, “Exposure in wireless ad-hoc sensor networks,” in Proc. of MOBICOM, pp. 139–150, July 2001.
[12] O. Dousse, C. Tavoularis, and P. Thiran, “Delay of intrusion detection in wireless sensor networks,” in Proc. of the 7th ACM international symposium on Mobile ad hoc networking and computing, pp. 155-165, May 2006.
[13] Q. Cao, T. Yan, J. Stankovic, J. Stankovic, and T. Abdelzaher, “Analysis of target detection performance for wireless sensor networks,” in Distributed Computing in Sensor Systems, pp. 276‐292, 2005.
[14] G. Xing, R. Tan, B. Liu, J. Wang, X. Jia and C. Yi, “Data Fusion Improves the Coverage of Wireless Sensor Networks,” in Proc. Mobile Computing and Networking, pp. 157‐168, Sep. 2009.49
[15] R. Tan, G. Xing, B. Liu and J. Wang, “Impact of Data Fusion on Real‐Time Detection in Sensor Networks,” in Proc. IEEE Real‐Time Systems Symposium, vol.56, pp. 323‐332, Dec. 2009.
[16] N. Katenka, E. Levina and G. Michailidis, “Local Vote Decision Fusion for Target Detection in Wireless Sensor Networks,” in IEEE Transactions on Signal Processing, vol. 56, no. 1, pp. 329‐338, Jan. 2008.
[17] R. Niu and P. K. Varshney, “Distributed Detection and Fusion in a Large Wireless Sensor Network of Random Size,” EURASIP Journal on Wireless Communications and Networking, vol. 2005, no. 4, pp. 462‐472, Sep. 2005.
[18] L. Yu and A. Ephremides, “Detection Performance and Energy Efficiency of Sequential Detection in a Sensor Network,” in Proc. Hawaii International Conference on System Sciences, vol. 9, Jan. 2006.
[19] L. Yu, L. Yuan, G. Qu and A. Ephremides, “Energy‐Driven Detection Scheme with Guaranteed Accuracy,” in Proc. Information Processing in Sensor networks, pp. 284‐291, 2006.
[20] R. Tan, G. Xing, B. Liu and J. Wang, “Performance Analysis of Real-Time Detection in Fusion-Based Sensor Networks,” IEEE Trans. on Parallel and Distributed Systems , vol. 22, no. 9, pp. 1564-1577, 2011.
[21] G. Xing, X. Chang, C. Lu, J. Wang, K. Shen, R. Pless, and J. O’Sullivan, “Efficient coverage maintenance based on probabilistic distributed detection,” IEEE Trans. on Mobile Computing, vol. 9, no. 9, pp. 1346 –1360, Sep. 2010.
[22] T.-L. Chin and Y. Hu, “Optimal target detection with localized fusion in wireless sensor networks,” in Proc. of Globecom, New Orlean, LA, 2008.
[23] X. Luo, M. Dong, and Y. Huang, “On distributed fault-tolerant detection in wireless sensor networks,” IEEE Trans. on Computers, vol. 55, no. 1, pp. 58–70, Jan. 2006.
[24] T. Clouqueur, V. Phipatanasuphorn, P. Ramanathan, and K. K. Saluja, “Sensor deployment strategy for detection of targets traversing a region,” Journal on Mobile Networks and Applications, pp. 453–461, 2003.
[25] G. Veltri, Q. Huang, G. Qu, and M. Potkonjak, “Minimal and maximal exposure path algorithms for wireless embedded sensor networks,” in Proceedings of the first international conference on Embedded networked sensor systems, Los Angeles, CA, Nov. 2003.
[26] T. Clouqueur, P. Ramanathan, and K. K. Saluja, “Exposure of variable speed targets through a sensor field,” in Proc. of the 6th Ann. Conf. on Information Fusion, Aug. 2003.
[27] K. Chakrabarty, S. S. Iyengar, H. Qi, and E. Cho, “Grid coverage for surveillance and target location in distributed sensor networks,” IEEE Transactions on Computers, vol. 51, Dec. 2002.
[28] R. Shah, S. Roy, S. Jain, and W. Brunette, “Data MULEs: modeling a three-tier architecture for sparse sensor networks,” in Proc. of the first IEEE International Workshop on Sensor Network Protocols and Applications, May 2003, pp. 30–41.
[29] A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson, “Wireless sensor network for habitat monitoring,” in Proceedings of ACM International Conference of Wireless Sensor Networks and Applications, 2002.
[30] D. K. Goldenberg, J. Lin, A. S. Morse, B. E. Rosen, and Y. R. Yang, “Towards mobility as a network control primitive,” in ACM International Symposium on Mobile Ad Hoc Networking and Computing, Roppongi, Japan, May 2004, pp. 163–174.
[31] A. Kansal, A. A. Somasundara, D. D. Jea, M. B. Srivastava, and D. Estrin, “Intelligent fluid infrastructure for embedded networks,” in MobiSys, Boston, MA, June 2004, pp. 111–124.
[32] G. T. Sibley, M. H. Rahimi, and G. S. Sukhatme, “Robomote: A tiny mobile robot platform for large-scale ad-hoc sensor networks,” in Proceedings of the 2002 IEEE International Conference on Robotics and Automation, Washington, DC, May 2002, pp. 1143–1148.
[33] M. Rahimi, H. Shah, G. S. Sukhatme, J. Heidemann, and D. Estrin, “Studying the feasibility of energy harvesting in a mobile sensor network,” in Proceedings of IEEE International Conference on Robotics and Automation, 2003.
[34] T. D. Parsons, “Pursuit-evasion in a graph,” in Theory and Application of Graphs, Y. Alani and D. R. Lick, Eds. Springer-Verlag, 1976, pp.426–441.
[35] J. P. Hespanha, H. J. Kim, and S. Sastry, “Multiple-agent probabilistic pursuit-evasion games,” in Proc. of the 38th Conf. on Decision and Control, Dec. 1999.