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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 朱浩華 | |
| dc.contributor.author | Ji-Rung Chiang | en |
| dc.contributor.author | 蔣季融 | zh_TW |
| dc.date.accessioned | 2021-06-13T04:42:13Z | - |
| dc.date.available | 2006-07-21 | |
| dc.date.copyright | 2006-07-21 | |
| dc.date.issued | 2006 | |
| dc.date.submitted | 2006-07-18 | |
| dc.identifier.citation | [1] Paramvir Bahl and Venkata N. Padmanabhan, “RADAR: An in-building RF-based user location and tracking system, “ IEEE Conference on Computer Communications (INFOCOM 2000), Mar. 2000, pp. 775-784.
[2] ITRI, http://www.itri.org.tw. [3] Paramvir Bahl, A. Balachandran, and V. N. Padmanabhan, “Enhancements to the RADAR user location and tracking system, “ Technical report of Microsoft Research, Feb. 2000. [4] Ekahau. http://www.ekahau.com . [5] V. Seshadri, G. V. Zaruba, and M. Huber, “A Bayesian Sampling Approach to In-door Local-ization of Wireless Devices Using Received Signal Strength Indication, “ IEEE Conference on Pervasive Computing and Communications (PerCom 2005), Mar. 2005. [6] J. Hightower, and G. Borriello, “Particle Filters for Location Estimation in Ubiquitous Computing: A Case Study, “ International Conference on Ubiquitous Computing (Ubicomp 2005), Sept. 2005. [7] D. Schulz, D. Fox, and J. Hightower, “People Tracking with Anonymous and ID-Sensors using Rao-Blackwellised Particle Filters, “ International Joint Conference on Artificial In-telligence, Aug. 2003, pp. 921-926. [8] D. Fox, J. Hightower, L. Liao, D. Schulz, and G. Borriello, “Bayesian Filtering for Location Estimation, ” IEEE Pervasive Computing, vol. 2, no. 3, July-Sept. 2003, pp. 24-33. [9] David Graumann, Jeffrey Hightower, Walter Lara, and GaeTano Borriello, “Real-world Implementation of the Location Stack: The Universal Location Framework, “ IEEE Workshop on Mobile Computing Systems & Applications (WMCSA 2003), Oct. 2003. [10] Youngjune Gwon, Ravi Jain, and Toshiro Kawahara, “ Robust Indoor Location Estimation of Stationary and Mobile Users, “IEEE Conference on Computer Communications , (INFOCOM 2004), Mar. 2004. [11] J. Yie, Q. Yang, L. Ni, “ Adaptive Temporal Radio Maps for Indoor Location Estimation, “ International Conference on Pervasive Computing (Pervasive 2005) ,May. 2005. [12] Y.C. Chen, J.R. Chiang, H.H. Chu, Polly Huang, and A. W. Tsui, “Sensor-Assisted Wi-Fi Indoor Location System for Adapting to Environmental Dynamics, “ International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM 2005), Oct. 2005. [13] Masateru Minami, Yasuhiro Fukuju, Kazuki Hirasawa, Shigeaki Yokoyama, Moriyuki Mizumachi, Hiroyuki Morikawa, and Tomonori Aoyama, “ DOLPHIN: A Practical Approach for Implementing a Fully Distributed Indoor Ultrasonic Positioning System, “ International Conference on Ubiquitous Computing (UbiCom 2004), Sep. 2004, pp. 347-365 [14] Lingxuan Hu and David Evans, ” Localization for Mobile Sensor Networks, “ International Conference on Mobile Computing and Networking (MobiCom 2004) [15] T. S. Eugene Ng and Hui Zhang, “ Predicting Internet Network Distance with Coordinates-Based Approaches, “IEEE Conference on Computer Communications (INFOCOM 2002), pp. 170-179 [16] Frank Dabek, Russ Cox, Frans Kaashoek, and Robert Morris, “ Vivaldi: A Decentralized Network Coordinate System, “ ACM Special Interest Group on Data Communications, (SIGCOMM 2004). [17] Tian He, Chengdu Huang, Brian M. Blum, John A. Stankovic and Tarek Abdelzaher, “ Range-Free Localization Schemes for Large Scale Sensor Networks, “ ACM International Conference on Mobile Computing and Networking (MOBICOM 2003) pp. 81-95. [18] Nissanka B. Priyantha, Hari Balakrishnan, Erik Demaine, and Seth Teller, “ Anchor-Free Distributed Localization in Sensor Networks, “ ACM International Conference on Embedded Networked Sensor Systems (SenSys 2003), pp. 340-341. [19] Telos: http://www.moteiv.org/. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/33468 | - |
| dc.description.abstract | 位置感知服務可藉由準確及可靠的室內定位系統獲得幫助。現今普遍設置的802.11x無線網路架構使得利用此無線網路作為定位的服務僅需要極小的額外花費。雖然近來各項研究已發表出準確性及效能令人滿意的結果，但事實上，當人群擁擠或移動時，定位系統的誤差卻會急遽地增加。這篇論文提出了合作定位的方式，利用在同一人群中，鄰近且定位較精準的其他使用者的資訊，來增進定位系統的效能。我們利用了 Zigbee 無線電來偵測鄰近的使用者。這篇論文介紹了合作定位的基本模型及演算法。我們在各種不同形式所組成的人群中進行實驗。實驗結果表示，在將我們的方法套用至一個商業的定位系統 Ekahau 之後，使得定位系統的準確性增加了28.2% 到 56% 的效能。 | zh_TW |
| dc.description.abstract | Location-aware services can benefit from accurate and reliable indoor location tracking. The widespread adoption of 802.11x wireless LAN as the network infrastructure creates the opportunity to deploy WiFi-based location services with few additional hardware costs. While recent research has demonstrated adequate performance, localization error increases significantly in crowded and dynamic situations due to electromagnetic interferences. This paper proposes collaborative localization as an approach to enhance position estimation by leveraging more accurate location information from nearby neighbors within the same cluster. The current implementation utilizes ZigBee radio as the neighbor-detection sensor. This paper introduces the basic model and algorithm for collaborative localization. We also report experiments to evaluate its performance under a variety of clustering scenarios. Our results have shown 28.2-56% accuracy improvement over the baseline system Ekahau, a commercial WiFi localization system. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T04:42:13Z (GMT). No. of bitstreams: 1 ntu-95-R93922102-1.pdf: 859855 bytes, checksum: 49c29d23d491f28c364351d0d39d1d9e (MD5) Previous issue date: 2006 | en |
| dc.description.tableofcontents | Chapter 1 Introduction 1
Chapter 2 Clustering 3 Chapter 3 Design And Implementation 6 3.1 Neighborhood Detection 7 3.2 Confidence Estimation 8 3.3 Collaborative Error Correction 10 Chapter 4 Experimental Results 12 4.1 Neighborhood Sensing 12 4.2 Performance Evaluation 13 4.2.1 Stationary Clusters (Scenario I) 14 4.2.2 Mobile Cluster (Scenario II) 16 4.2.3 Impact of Space Geometry 18 4.3 Evaluation of Confidence Estimator 19 Chapter 5 Related Work 21 Chapter 6 Conclusion and Future Work 24 | |
| dc.language.iso | en | |
| dc.title | 合作定位：利用人群間鄰近連結改善無線網路定位系統之準確性 | zh_TW |
| dc.title | Collaborative Localization: Enhancing WiFi-Based Position Estimation with Neighborhood Links in Clusters | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 94-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 許永真,傅立成,斯國峰,李旺謙 | |
| dc.subject.keyword | 感測器網路,室內定位系統,效能評估,環境感知系統, | zh_TW |
| dc.subject.keyword | sensor network,adaptive system,collaborative system,indoor location system,performance evaluation, | en |
| dc.relation.page | 27 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2006-07-18 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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