臺灣博碩士論文加值系統

近年來基於位置為服務的應用被廣泛的應用在室內場景中。在室內定位技術裡，被動式室內定位技術不需用戶設備主動參與定位的過程即能推導得用戶之位置。先前的被動式定位在室內環境中需使用多個觀測節點去擷取使用者裝置之無線訊號強度並上傳雲端伺服器分析。但為了達到較高的定位精準，需佈設大量觀測節點使得成本上升，此外上傳雲端伺服器的資料量過於龐大的問題將導致伺服器超載的問題。
本論文提出一個改良的使用者足跡預測演算法，當使用者通過觀測節點時， 由觀測節點收集訊號的趨勢資訊，預測使用者的移動路徑。我們的方法除可減少佈建觀測節點的密度外，並可減低雲端伺服器的計算量，但仍可維持定位的精準度。

In recent years, location-based services have been widely used in indoor scenes. In the indoor positioning technology, the passive indoor positioning technology can derive the position of the user without the user equipment actively participating in the positioning process. Previous passive positioning schemes require multiple observation nodes in the indoor environment to capture the wireless signal strength from the user device, and upload the data to the cloud server. However, to achieve high positioning accuracy, a large number of observation nodes need to be installed. Therefore the cost is too high and the amount of data uploaded to the cloud server is too large, which will cause the server to be overloaded.
In this paper, we propose an improved user footprint prediction algorithm. When the user observes the node, the observation node collects the trend information of the signal to predict the user's moving path. In addition to reducing the density of deployed observation nodes, our method can reduce the computational load of the cloud server, but still maintain the accuracy of positioning.

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 v
表目錄 vi
第一章 緒論 1
第二章 相關文獻 5
2.1 基於無線定位技術 5
2.1.1到達時間法(Time of Arrival, TOA) 5
2.1.2 到達時間差法 (Time Difference of Arrival, TDOA) 6
2.1.3 接收訊號角度法 (Angle of Arrival, AOA) 6
2.1.4 接收訊號強度法(Receive Signal Strength, RSS) 6
2.2 相對位置定位法 8
2.3 絕對位置定位法 9
2.4 Pallas 10
2.5 Mobile Agents Location Tracking 11
2.6 Walkie-Markie 12
第三章 基於被動式室內定位技術之使用者路徑預測 13
3.1 觀測節點監測用戶的趨勢變化 13
3.2 觀測節點狀態轉換圖 22
3.3 觀測節點之封包紀錄演算法 25
第四章 實驗方法 30
第五章 結論 35
參考文獻 36

[1] A. Küpper, Location-Based Services: Fundamentals and Operation. New York: Wiley, 2005.
[2] H. Lim, L. C. Kung, J. Hou, and H. Luo, “Zero-Configuration, Robust Indoor Localization: Theory and Experimentation,” In Proceedings of IEEE INFOCOM, 2006, pp. 1–12.
[3] A. Rai, K. K. Chintalapudi, V. N. Padmanabhan, and R. Sen, “Zee: Zero-Effort Crowdsourcing for Indoor Localization,” in Proceedings of the 18th Annual International Conference on Mobile Computing and Networking (Mobicom '12), 2012, pp. 293–304.
[4] Y. C. Tseng, S. P. Kuo, H. W. Lee, and C. F. Huang, “Location Tracking in a Wireless Sensor Network by Mobile Agents and Its Data Fusion Strategies,” The Computer Journal, Vol. 47, No. 4, 2004, pp. 448–460.
[5] G. Shen, Z. Chen, P. Zhang, T. Moscibroda, and Y. Zhang, “Walkie-Markie: Indoor Pathway Mapping Made Easy,” In Proceedings of USENIX Conference on Networked Systems Design and Implementation, 2013, pp. 85–98.
[6] T. Pering, Y. Agarwal, R. Gupta, and R. Want, “Coolspots: Reducing the Power Consumption of Wireless Mobile Devices with Multiple Radio Interfaces,” in Proceedings 4th International Conference on Mobile Systems, Applications and Services, 2006, pp. 220–232.
[7] A. Musa and J. Eriksson, “Tracking Unmodified Smartphones Using Wi-Fi Monitors,” in Proceedings of the 10th Conference on Embedded Network Sensor Systems, ACM, 2012.
[8] M. V. Barbera, A. Epasto, A. Mei, V. C. Perta, and J. Stefa, “Signals From the Crowd: Uncovering Social Relationships Through Smartphone Probes,” in Proceedings of ACM IMC, 2013.
[9] Y. H. Kho, N. S. Chong, and R. C. Kizilirmak, “A Passive Wireless Tracking System for Indoor Assistive Monitoring,” in Proceedings of the 9th IEEE International Conference on Sensing Technology, Auckland, New Zealand, 2015,pp. 138–142.
[10]劉邦良, 蔡孟軒, 羅嘉寧, 楊明豪“基於室內定位技術之使用者移動足跡分析,” TANET2016 臺灣網際網路研討會, 2016.
[11]C. Luo, L. Cheng, M. C. Chan, T. Gu, J. Li and Z. Ming, “Pallas: Self- Bootstrapping Fine-Grained Passive Indoor Localization Using Wi-Fi Monitors,” IEEE Transactions on Mobile Computing , Vol. 16, No. 2, 2017, pp. 466–481.
[12] J. J. Caffery and G. L. Stuber, “Overview of Radiolocation in CDMA Cellular System,” IEEE Communications Magazine, Vol. 36, No. 4, 1998, pp. 38–45.
[13]A.H. Sayed, A. Taroghat, and N. Khajehnouri, “Network-Based Wireless Location,’’ IEEE Signal Processing Magazine, Vol. 22, No. 4, 2005, pp. 24–40.
[14] P. Prasithsangaree , P. Krishnamurthy and P. K. Chrysanthis, “On Indoor Position Location with Wireless LANs” , Proceedings of the 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communication, Vol. 2, 2002, pp.720–724.
[15] M. Sugano, T. Kawazoe, Y. Ohta, and M. Murata, “Indoor Localization System Using RSSI Measurement of Wireless Sensor Network Based on Zigbee Standard,” in Conference Proceedings of the 6th International Multi-Conference on Wireless and Optical Communications, 2006.
[16] S. Capkun, M. Hamdi, and J.P. Hubaux, “GPS-Free Positioning in Mobile Ad-hoc Networks,’’ in Proceedings 34th IEEE Hawaii International Conference System Sciences (HICSS-34), 2001.
[17] C. Rabaey and J.M. Beutel, “Locationing in Distributed Ad-Hoc Wireless Sensor Networks,” in Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 4, 2001, pp. 2037–2040.
[18]Y. Ohta, M. Sugano and M. Murata, “Autonomous Localization Method in Wireless Sensor Networks,” Proceedings of 3rd IEEE International Conference on Pervasive Computing and Communications Workshops 2005 (PerCom 2005), pp. 379–384.