跳到主要內容

臺灣博碩士論文加值系統

(3.236.110.106) 您好!臺灣時間:2021/07/24 07:04
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:黃奕塵
研究生(外文):Huang, Yichen
論文名稱:在無線感測網路中以可靠的接收訊號為基礎 之合作定位系統開發
論文名稱(外文):Development of a Collaborative Localization Systemin Wireless Sensor Networks Based on Dependable RSSI
指導教授:陳永昇陳永昇引用關係
指導教授(外文):Chen, Yeongsheng
口試委員:金台齡李炯三
口試委員(外文):Chin, TailinLi, Chiungsan
口試日期:2012-07-11
學位類別:碩士
校院名稱:國立臺北教育大學
系所名稱:資訊科學系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:36
中文關鍵詞:定位無線感測網路RSSI
外文關鍵詞:LocalizationWireless sensor networkRSSI
相關次數:
  • 被引用被引用:0
  • 點閱點閱:171
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
在無線感測網路中,定位技術是一項熱門的研究主題,許多定位演算法已經被提出。在這些演算法中,由於RSS (Received Signal Strength)測量距離容易且無需額外硬體,因此,許多研究利用RSS作為定位的基礎。然而,RSS容易被環境所干擾,為此,根據實際的實驗,本論文提出RSS閾值以挑選可靠的RSS作為距離預測的基礎。在定位的過程中,盲節點(blind node)會定期的廣播封包至鄰近節點,在收到封包後,鄰近節點便會測得封包的RSSI (Received Signal Strength Indicator)值。接著,可靠的RSSI值會被用做距離預測,並且,利用這些距離資訊,藉由最短路徑演算法便能計算出所有參考節點(reference node)到盲節點之間的距離。在得到盲節點到至少三個參考節點之間的距離後,盲節點的座標便能利用最小均方誤差法計算之。本論文中的實驗是採用德州儀器所製造之CC2430/2431晶片,實驗結果顯示,利用本論文所提出之方法,在室內環境下,當RSSI閾值為-59.26(dBm)定位座標與實際座標之間的誤差可達到0.76公尺以內; 在室外環境下,當RSSI閾值為-79.77(dBm)定位座標與實際座標之間的誤差可達到5.91公尺以內。
Localization in WSNs (Wireless Sensor Networks) is an active research topic, and many localization algorithms in WSNs have been proposed in the literature. Among them, approaches based on RSS (Received Signal Strength) are popular and useful for distance estimation since it is simple and needs no extra hardware. However, RSS is greatly affected by the environment and cannot be precisely measured. To tackle this problem, this study proposes a mechanism for distance estimation by using dependable RSSI (Received Signal Strength Indicator) values. The threshold for selecting dependable RSSI values is determined by practical experiments. In the proposed approach, the blind node periodically broadcasts packets to its one-hop neighboring nodes and the neighboring nodes measure the RSSI values of the received packets. The dependable RSSI value is then used to estimate the distance between them. By using the shortest path algorithm, distances between the blind node and the reference nodes can be derived with high accuracy. Thus, for a blind node, with distance information to at least three reference nodes, its location can be computed by a Minimum Mean Square Error algorithm. The proposed localization scheme is implemented with TI CC2430/2431 chips. Practical experiment results show that, with the proposed scheme, the average location error of the blind node is 0.76 meters in an indoor environment under the condition of the dependable RSSI threshold being -59.26 dBm, and 5.91 meters in an outdoor environment under the condition of the dependable RSSI threshold being -79.77 dBm.
摘要 i
Abstract ii
List of Tables v
List of Figures vi
Chapter 1 Introduction 1
Chapter 2 Related Work 4
Chapter 3 Collaborative Localization Mechanism 7
I. Collection of RSSI Values Between Any Two Nodes 9
II. Distance Estimation 10
III. Deriving Distance Between a Blind Node and a Reference Node 17
IV. Computation of the Coordinates of Blind Nodes 17
Chapter 4 System Design and Experiments 20
I. Collection of RSSI Values Between any Two Nodes 23
II. Distance Estimation of any Two Nodes 23
III. Computation of Distance Between Reference Nodes and Blind Node C96C 23
IV. Computation of the Coordinates of Blind Nodes 24
Chapter 5 Conclusions 29
References 30
Appendix 35

List of Tables
Table 4.1 Location error (m) for each blind node in Experiment 1 27
Table 4.2 Location error (m) for each blind node in Experiment 2 28

List of Figures
Fig. 3.1. IEEE 802.15.4 log-distance path loss model [21] 9
Fig. 3.2 RSSI versus distance (Experiment 1) 12
Fig. 3.4 Empirical RSSI standard deviation and modal deviation at different distances (indoor) 16
Fig. 3.5 Empirical RSSI standard deviation and modal deviation at different distances (outdoor) 16
Fig. 4.1 CC2430/2431 DB 21
Fig. 4.2 CC2430/2431 EB 21
Fig. 4.3 The administration software 22
Fig. 4.4 A Localization example (Experiment 1) 25
Fig. 4.5 A localization example (Experiment 2) 26
Fig. 4.6 Location error versus calibration factor c (Experiment 1) 26
Fig. 4.7 Location error versus calibration factor c (Experiment 2) 27

[1]G.J. Pottie, and W.J. Kaiser, “Wireless Integrate Network Sensors,” Communications of the ACM, vol. 43, no. 5, pp. 551-558, May 2002.
[2]D. Estrin, L. Girod, G. Pottie, and M. Strivastava, “Instrumenting the World with Wireless Sensor Networks,” International Conference of Acoustics, Speech, and Signal Processing (ICASSP), vol. 4, pp. 2033-2036, May 2001.
[3]D. Estrin, R. Govindan, J. Heidemann, and S. Kumar, “Next century challenges: Scalable coordination in sensor networks,” ACM MobiCom, pp. 263-270, August 1999.
[4]N. Bulusu, J. Heidemann, and D. Estrin, “GPS-less low cost outdoor localization for very small devices,” IEEE Personal Communications Magazine, vol. 7, no. 5, pp. 28–34, October 2000.
[5]D. Niculescu, and B. Nath, “Ad-hoc positioning system (APS) using AOA,” Proc. of IEEE INFCOM, 2003.
[6]P. Bahl, and V.N. Padmanabhan, “RADAR: an in-building RF-based user location and tracking system,” Proc. of IEEE INFCOM, 2000.
[7]K. Chintalapudi, A. Dhariwal, R. Govindan, and G.Sukhatme, “Ad-hoc localization using ranging and sectoring,” Proc. of IEEE INFCOM, 2004.
[8]Sarfraz Nawaz, and Sanjay Jha, “COLLABORATIVE LOCALIZATION FOR WIRELESS SENSOR NETWORKS,” IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’07).
[9]N. Patwari, A.O. Hero, M. Perkins, N.S. Correal, and R.J. O’Dea, “Relative location estimation in wireless sensor networks,” IEEE Trans. Signal Processing, vol. 51, pp. 2137–2148, 2003.
[10]Y. Shang, W. Ruml, Y. Zhang and M. Fromherz, “Localization from Mere Connectivity,” Proc. ACM MobiHoc, pp. 201-212, 2003.
[11]Xinrong Li, “Collaborative Localization With Received-Signal Strength in Wireless Sensor Networks,” IEEE Trans. Vehicular Technology, vol. 56, pp. 3807-3817, 2007.
[12]Y. Shang, W. Ruml, and Y. Zhang, “Improved MDS-based localization,” Proc. of IEEE INFCOM, 2004.
[13]Q. Yao, F. Wang, H. Gao, K. Wang, and H. Zhao, “Location Estimation in ZigBee Network Based on Fingerprinting,” Proc. of IEEE International Conference on Vehicular Electronics and Safety, 2007.
[14]N. Patwari, J. Ash, S. Kyperountas, A. O. Hero, R. M. Moses, and N. S. Correal, “Locating the nodes: Cooperative localization in wireless sensor networks,” IEEE Signal Process. Mag., vol. 22, no. 4, pp. 54–69, 2005.
[15]E.-E-L Lau, B.-G Lee, S.-C Lee and W.-Y Chung, “Enhanced RSSI-Based High Accuracy Real-Time User Location Tracking System for Indoor and Outdoor Environments,” International Journal on Smart Sensing and Intelligent Systems, vol. 1, No. 2, 2008.
[16]H.-S. Ahn and W. Yu, “Environmental-adaptive RSSI-based indoor localization,” IEEE Trans. on Automation Science and Engineering, vol. 6, no. 4, pp. 626-633, 2009.
[17]Y.-T. Chen, C.-L. Yang, Y.-K. Chang and C.-P. Chu, “A RSSI-based algorithm for indoor localization using ZigBee in wireless sensor network,” Proc. of the 15th International Conference on Distributed Multimedia Systems (DMS 2009), 2009.
[18]D. Niculescu and B. Nath, “DV based positioning in ad hoc networks,” Kluwer J. Telecommun. Syst., vol. 22, no. 1, pp. 267–280, Jan. 2003.
[19]T. He, C. Huang, B. Lum, J. Stankovic, and T. Adelzaher “Range-free localization schemes for large scale sensor networks,” ACM MobiCom, September 2003.
[20]S.-Y. Lau, T.-H. Lin, T.-Y Huang, I.-H. Ng, and P. Huang, "A measurement study of zigbee-based indoor localization systems under rf interference," in WINTECH '09: Proceedings of the 4th ACM International workshop on Experimental evaluation and characterization. New York, NY, USA: ACM, pp. 35-42, 2009.
[21]IEEE, “IEEE Std 802.15.4 Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (WPANs)”.
[22]H. Anton, and C. Rorres, Elementary Linear Algebra: Applications Version, 9th Edition, New York: Wiley, 2005.
[23]Texas Instruments, “Z-Stack Developer’s Guide, Document Number: F8W-2006-0022,” http://olmicrowaves.com/menucontents/designsupport/zigbee/Z-Stack%20Developer's%20Guide%20_F8W-2006-0022_.pdf , May 2011.
[24]ZigBee Alliance, “ZigBeeSpecfication,”http://www.zigbee.org/Specifications.aspx, May 2011.
[25]S. Farahani, ZigBee wireless networks and transceivers: Newnes, 2008.
[26]ZigBee Alliance,http://www.zigbee.org/, May 2011.
[27]K. Aamodt, “CC2431 Location Engine”, Application Note AN042(Rev. 1.0), SWRA095, Texas Instruments.
[28]E. Dijkstra, “A Note on Two Problems in Connexion with Graphs,” NumerischeMathemotik, vol. 1, pp. 269-71, 1959.

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top