Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/4854
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dc.contributor郭耀文zh_TW
dc.contributor廖俊睿zh_TW
dc.contributor.advisor溫志煜zh_TW
dc.contributor.author蕭宇成zh_TW
dc.contributor.authorHsiao, Yu-Chengen_US
dc.contributor.other中興大學zh_TW
dc.date2009zh_TW
dc.date.accessioned2014-06-06T06:30:24Z-
dc.date.available2014-06-06T06:30:24Z-
dc.identifierU0005-2207200814140200zh_TW
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Scholten, and P. Havinga, “An Iterative Quality-Based Localization Algorithm for Ad Hoc Networks,” Proceedings of the International Conference on Pervasive Computing, August 2002, pp. 5561. [18] K. Chintalapudi, A. Dhariwal, et al., “Ad-hoc localization using ranging and sectoring,” - INFOCOM 2004 Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies, pp. 2662-2672, April, 2003. [19] A. Savvides, M. Srivastava, L. Girod, and D. Estrin, “Localization in sensor networks,” in C. R. Raghavendra, K. M. Sivalingam, and T. Znati, ed. Wireless Sensor Networks, Kluwer Academic Publishers, 2004. [20] R. Iyengar and B. Sikdar, “Scalable and Distributed GPS free Positioning for Sensor Networks,” IEEE International Conference on Communications, Volume: 1, Pages:338-342, 2003. [21] H. Wang, J. Elson, L. Girod, D. Estrin, K. Yao, and L. 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Sethares, “Distance Estimation Using Bidirectional Communications Without Synchronous Clocking,” in IEEE Transactions on Signal Processing, vol. 55, no. 5, Part I, pp. 1927-1939, May 2007. [27] S. Chib and E. Greenberg, “Understanding the Metropolis-Hastings algorithm,” The American Statistician 49: 327-335, 1995. [28] K.-T. Chu, C.-Y. Wen, Y.-C. Ouyang, and W. A. Sethares, “Adaptive Distributed Topology Control for Wireless Ad-Hoc Sensor Networks,” in Proceedings of the 2007 International Conference on Sensor Technologies and Applications - SensorComm07, Valencia, Spain, October 2007. [29] J. Bachrach and C. Taylor, “Localization in sensor networks,” in Handbooks of Sensor Networks: Algorithms and Architectures, Ivan Stojmenovic, editor, John Wiley & Sons, New Jersey, 2005. [30] B. K. P. Horn, H. Hilden, and S. Negahdaripour, “Closed-form solution of absolute orientation using orthonormal matrices,” in Journal of the Optical Society of America A, 5(7), 1988. [31] Roberts, G.O., Gelman, A., and Gilks, W.R., “Weak convergence and optimal scaling of random walk metropolis algorithms,” Technical Report, University of Cambridge, 1994. [32] Koen Langendoen , Niels Reijers, “Distributed localization in wireless sensor networks: a quantitative comparison,” Networks: The International Journal of Computer and Telecommunications Networking, 43(4), November 2003. [33] Ihler, A.T., Fisher, J.W., III, Moses, R.L., Willsky, A.S., “Nonparametric Belief Propagation for Self-Localization of Sensor Networks,” in IEEE Journal on Selected Areas in Communications, vol. 23, no. 4, pp. 809-819, April 2005.zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/4854-
dc.description.abstract如何在一個無線感測網路的環境中,建立網路連線並且估測出感測器(sensor)的座標位置是一項非常重要的研究。本篇論文提出了一個無線隨意感測網路之分散式自我定位演算法,利用網路之間訊息的傳播,將接收到的資訊作處理和運算以達成定位估測。當估測的程序和通訊協定開始執行時,我們首先會先針對區域網路做初始的定位,接下來,當所有的區域感測網路群(sensor clusters)的區域座標系統(local coordinate system)完成後, 我們應用了一個精確校正系統(refinement schemes)來降低初始估測的錯誤率,使得誤差擴散(propagation error)可以有效率的被抑制。最後再利用座標系統整合的演算法來將各區域座標系統整合成一個整體的網路座標系統(single global coordinate system) 。運算過程中,只需要利用感測器彼此之間的距離資訊而不需要使用到含有GPS功能的感測器。這篇論文詳述了定位方式的建立並且探討在設計演算法所必須要做的取捨,最後我們藉由程式模擬和數值分析,來驗證這個演算法的可行性。zh_TW
dc.description.abstractThis thesis proposes algorithms for establishing connectivity and location estimation in wireless sensor networks. The algorithms exploit the information flow while coping with distributed signal processing and the requirements of network scalability. Once the estimation procedure and communication protocol are performed, sensor clusters can be merged to establish a single global coordinate system without GPS sensors using only distance information. In order to adjust the sensor positions, the refinement schemes are applied to reduce the estimation error such that the propagation error can be suppressed. This thesis outlines the technical foundations of the localization techniques and presents the tradeoffs in algorithm design. The feasibility of the proposed schemes is shown to be effective under certain assumptions and the analysis is supported by simulation and numerical studies.en_US
dc.description.tableofcontents1. Introduction 1 2. Literature Review 3 3. Distributed Anchor-Free Positioning Algorithm (DAPA) 5 3.1. Phase I: Initial Local Position Estimation 5 3.2. Phase II: Sensor Location Adjustment 11 3.3. Phase III: Relative Global Localization 15 3.3.1 The Information Flow 15 3.3.2 Relative Global Coordinate System 16 4. Simulation and Numerical Results 23 4.1. Result of The Initial Local Position Estimation 23 4.2. Comparison between Min-max and particle filter 29 4.3. Results of Gradient Algorithm 30 4.4. Results of The Metropolis-Hastings Algorithm 32 4.5. An Example of Merging Two Clusters 38 5. Conclusion 41 6. References 42en_US
dc.language.isoen_USzh_TW
dc.publisher通訊工程研究所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2207200814140200en_US
dc.subjectWireless Sensor Networksen_US
dc.subject無線感測網路zh_TW
dc.subjectSelf-Localizationen_US
dc.subjectParticle Filteringen_US
dc.subject自我定位zh_TW
dc.subject隨機粒子濾波器zh_TW
dc.title分散式自我定位演算法在無線隨意感測網路上之研究zh_TW
dc.titleDistributed Self-Localization for Wireless Ad-Hoc Sensor Networksen_US
dc.typeThesis and Dissertationzh_TW
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en_US-
item.openairetypeThesis and Dissertation-
item.grantfulltextnone-
item.fulltextno fulltext-
item.cerifentitytypePublications-
Appears in Collections:通訊工程研究所
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