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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 周承復(Cheng-Fu Chou) | |
| dc.contributor.author | Yu-Yi Chen | en |
| dc.contributor.author | 陳佑逸 | zh_TW |
| dc.date.accessioned | 2021-06-08T07:31:44Z | - |
| dc.date.copyright | 2011-08-16 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-08-09 | |
| dc.identifier.citation | [1] M. Adler, H. R‥acke, N. Sivadasan, C. Sohler, and B. V‥ocking. Randomized pursuit-
evasion in graphs. Comb. Probab. Comput., 12:225–244, May 2003. [2] P. Balister, Z. Zheng, S. Kumar, and P. Sinha. Trap coverage: Allowing coverage holes of bounded diameter in wireless sensor networks. In INFOCOM 2009, IEEE, pages 136 –144, april 2009. [3] J.-C. Chin, Y. Dong, W.-K. Hon, C.-T. Ma, and D. Yau. Detection of intelligent mobile target in a mobile sensor network. Networking, IEEE/ACM Transactions on, 18(1):41 –52, feb. 2010. [4] J. christophe Zufferey, A. Klaptocz, A. Beyeler, J. daniel Nicoud, and D. Floreano. A 10-gram microflyer for vision-based indoor navigation. In International Conference on Intelligent RObots and Systems - IROS, pages 3267–3272, 2006. [5] B. P. Gerkey, S. Thrun, and G. Gordon. Visibility-based pursuit-evasion with limited field of view. Int. J. Rob. Res., 25:299–315, April 2006. [6] W. Green and P. Oh. Autonomous hovering of a fixed-wing micro air vehicle. In Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on, pages 2164 –2169, may 2006. [7] L. J. Guibas, J.-C. Latombe, S. M. LaValle, D. Lin, and R. Motwani. Visibility-based pursuit-evasion in a polygonal environment. In Proceedings of the 5th International Workshop on Algorithms and Data Structures, WADS ’97, pages 17–30, London, UK, 1997. Springer-Verlag. [8] N. Heo and P. Varshney. A distributed self spreading algorithm for mobile wireless sensor networks. In Wireless Communications and Networking, 2003. WCNC 2003. 2003 IEEE, volume 3, pages 1597 –1602 vol.3, march 2003. [9] G. Hollinger, A. Kehagias, and S. Singh. Probabilistic strategies for pursuit in clut- tered environments with multiple robots. In Robotics and Automation, 2007 IEEE International Conference on, pages 3870 –3876, april 2007. [10] A. Howard, M. J. Mataric, and G. S. Sukhatme. Mobile sensor network deployment using potential fields: A distributed, scalable solution to the area coverage problem. In Proc. of the 6th Int. Symp. on Distributed Autonomous Robotics Systems, pages 299–308, 2002. [11] V. Isler, S. Kannan, and S. Khanna. Randomized pursuit-evasion with limited vis- ibility. In Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms, SODA ’04, pages 1060–1069, Philadelphia, PA, USA, 2004. Society for Industrial and Applied Mathematics. [12] Y. Keung, B. Li, and Q. Zhang. The intrusion detection in mobile sensor network. In Proceedings of the eleventh ACM international symposium on Mobile ad hoc networking and computing, MobiHoc ’10, pages 11–20, New York, NY, USA, 2010. ACM. [13] S. Kumar, T. H. Lai, and A. Arora. Barrier coverage with wireless sensors. In Proceedings of the 11th annual international conference on Mobile computing and networking, MobiCom ’05, pages 284–298, New York, NY, USA, 2005. ACM. [14] S. Kumar, T. H. Lai, and J. Balogh. On k-coverage in a mostly sleeping sensor network. In Proceedings of the 10th annual international conference on Mobile computing and networking, MobiCom ’04, pages 144–158, New York, NY, USA, 2004. ACM. [15] S. LaValle, D. Lin, L. Guibas, J. Latombe, and R. Motwani. Finding an unpre- dictable target in a workspace with obstacles. In Robotics and Automation, 1997. Proceedings., 1997 IEEE International Conference on, volume 1, pages 737 –742 vol.1, apr 1997. [16] B. Liu, P. Brass, O. Dousse, P. Nain, and D. Towsley. Mobility improves coverage of sensor networks. In Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing, MobiHoc ’05, pages 300–308, New York, NY, USA, 2005. ACM. [17] J. Roberts, J.-C. Zufferey, and D. Floreano. Energy management for indoor hovering robots. In Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on, pages 1242 –1247, sept. 2008. [18] R. Vidal, O. Shakernia, H. J. Kim, D. H. Shim, and S. Sastry. Probabilistic pursuit- evasion games: Theory, implementation and experimental evaluation. IEEE Trans- action on Robotics and Automation, 18(5):662–669, 2002. [19] G. Wang, G. Cao, and T. La Porta. Movement-assisted sensor deployment. In INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies, volume 4, pages 2469 – 2479 vol.4, march 2004. [20] G. Wang, G. Cao, T. La Porta, and W. Zhang. Sensor relocation in mobile sensor networks. In INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE, volume 4, pages 2302 –2312 vol. 4, march 2005. [21] Y. Zou and K. Chakrabarty. Sensor deployment and target localization based on virtual forces. In INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies, volume 2, pages 1293 – 1303 vol.2, march-3 april 2003. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/26907 | - |
| dc.description.abstract | 無線感測網路已經被用在使用移動式感測器來偵測移動式目標。考 慮在一個封閉的區域空間內，一些移動式感測器會持續不斷地進行巡邏，並且嘗試去發現在此空間內的移動式目標。由於目標會在一定時間內離開這塊空間， 感測器必須要在給定的時間之內發現目標。本論文探討如何在給定時間限制之下去找出需要的感測器數量，以符合應用的要求。我們使用馬可夫模型來計算目標出現的機率，並且根據此機率推算出期望的偵測時間，再利用這些關係來推論出所需要的感測器數量。另外本論文也提供感測器在合作模式之下對時間的影響，並且提出一個讓感測器在分散式的環境交換資訊的演算法。 最後我們使用模擬來驗證模型的準確性、在不同的環境底下對於偵測時間的影響，並且成功推算出所需要的感測器數量。 | zh_TW |
| dc.description.abstract | Wireless sensor networks have been widely studied in arious applications such as environmental monitoring , battlefield surveillance, and intrusion detection recently. One of the important applications is to detect mobile targets (e.g., invaders) by using mobile sensor nodes. Consider a closed region where there are several mobile sensors patrolling the area and some mobile targets in the region. The mobile sensors need to find the targets before a time period because the mobile targets may escape from the monitored region after a certain period of time. In order to meet the deadline, many mobile sensors should be deployed to reduce the detection time. This work aims at finding the number of sensors required in the region through realizing relations among expected detection time, detection strategies, and the number of sensors. Thus, given the number of sensors and detecting robability, we propose a Markov model to derive the expected detection time. Our proposed model can then help determine how many sensors are sufficient such that sensors are able to detect the targets before a given deadline. We also provided a cooperative scheme for sensors that can reduce expected detection time and a distributed protocol for sensors with limited transmission range. The simulation results show that the model can predict the expected detection time precisely, and the number of sensors required derived from the model is adequate. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T07:31:44Z (GMT). No. of bitstreams: 1 ntu-100-R98944007-1.pdf: 1057052 bytes, checksum: 5a1442d4060af4fa1ec00984a4e4779b (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 致謝 ii
Abstract iii 中文摘要 v 1 Introduction 1 2 Related Work 4 3 Prediction Model 8 3.1 Prediction Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.1.1 Evolution of Sensor Position . . . . . . . . . . . . . . . . . . . . 9 3.1.2 Evolution of Target Presence Probability . . . . . . . . . . . . . 10 3.2 Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.3 Method of evaluating detection time . . . . . . . . . . . . . . . . . . . . 16 3.4 Finding number of sensors required . . . . . . . . . . . . . . . . . . . . 17 4 Other Issues 19 4.1 Cooperative Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.2 Distributed Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2.1 Confidence Index . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.2.2 Information Fusion . . . . . . . . . . . . . . . . . . . . . . . . . 21 5 Simulation 23 5.1 Model Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 5.1.1 Expected Detecting Time . . . . . . . . . . . . . . . . . . . . . . 25 5.1.2 Detecting Probability . . . . . . . . . . . . . . . . . . . . . . . . 25 5.2 Number of Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 5.3 Cooperative Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 5.4 Distributed Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 6 Conclusion 33 Bibliography 34 | |
| dc.language.iso | en | |
| dc.title | 利用移動式感測網路在時間限制下之目標物偵測 | zh_TW |
| dc.title | Deadline-Constrained Mobile Target Detection in Mobile Sensor
Networks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蔡子傑(Tzu-Chieh Tsai),吳曉光(Hsiao-Kuang Wu),王協源(Shie-Yuan Wang),林靖茹(Ching-Ju Lin) | |
| dc.subject.keyword | 入侵者偵測,追逃問題,模型,時間限制, | zh_TW |
| dc.subject.keyword | Intrusion Detection,Pursuit-Evasion,Model,Time Constraint, | en |
| dc.relation.page | 37 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2011-08-09 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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| ntu-100-1.pdf 目前未授權公開取用 | 1.03 MB | Adobe PDF |
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