臺灣博碩士論文加值系統

隨著資通訊技術快速的發展，各式多元化應用與智慧手持裝置服務已逐漸為世人所重視，室內位置導向服務(Indoor Location Based Services, Indoor LBS)之應用也與日俱增，使得提升室內定位準確度成為一重要研究議題。近年來，許多室內定位系統藉由使用環境中之無線射頻信號作為定位計算依據，然無線射頻信號容易受到環境干擾而降低定位準確度，因此對於定位準確度之議題上需進行研究與改良。

室內定位系統通常只採用一種訊號發射器及接收器來取得定位參考點的資料，傳統之定位系統使用最近鄰居法(K-Nearest Neighbors, KNN)或多點定位法(Multilateration)做為定位演算方法，因此造成定位準確度受限於單一參考點的資訊及所使用的定位演算方法而無法使準確度獲得提升。本研究之整合異質網路(Integrated Heterogeneous Networks, IHN)室內定位系統，藉由採用低功耗之藍牙發射器(Beacon)獲取額外的定位訊號，並且本研究透過結合指紋比對法(Fingerprinting)以修正多點定位法與定義模糊區域，以提升子區域選擇的準確度，最後提出門檻值機制，用以選取訊號發射器，達到提升目前室內定位系統的準確度。

根據本研究之效能分析結果顯示，整合異質網路室內定位系統所求得之平均誤差距離為1.21公尺，其優於使用單一訊號發射器(Wi-Fi AP)的混合演算法平均誤差距離1.29公尺與單一訊號發射器(Beacon)的混合演算法平均誤差距離1.33公尺。研究結果顯示，本研究所提出之IHN室內定位系統可有效的提升定位準確度。

Applications and services for smart handheld devices have garnered considerable attention worldwide, as have applications for Indoor Location-based Services (Indoor LBS). Improving their indoor positioning accuracy is a significant challenge. In recent years, many indoor positioning studies have used radio frequency for positioning; however, a signal’s susceptibility to environmental interference can decrease positioning accuracy. Increasing positioning accuracy is a complex problem and remains to be solved.

Indoor positioning systems typically use only a signal transmitter (Wi-Fi AP) and receiver to acquire reference point data. The systems then use the K-Nearest Neighbor algorithm (KNN) or a Multilateration algorithm for positioning. Consequently, positioning accuracy is limited by both the positioning algorithm and the use of a single source for acquisition of reference point data. This study utilizes the novel Integrated Heterogeneous Networks Indoor Positioning System (IHNIPS) with low-energy Bluetooth Beacon to obtain positioning data. A fingerprint algorithm is then applied to revise the multilateration algorithm and define the fuzzy area to improve the accuracy of subspace selection. Last, novel threshold mechanisms are used to choose the transmitter for positioning and thereby improve current positioning accuracy.

Experimental results show that the average error distance is 1.29m for Wi-Fi AP and 1.33m for Beacon. And the average error distance is 1.21m in the proposed Integrated Heterogeneous Networks Indoor Positioning System. The IHNIPS outperforms both Wi-Fi AP and Beacon. Thus, positioning accuracy is improved.

摘要 III
Abstract IV
致謝 V
Contents VI
List of Figures VIII
List of Tables IX
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Contributions 2
1.3 Organization 3
Chapter 2 Background Knowledge 4
2.1 Localization Concept 4
2.2 Positioning Methods 5
2.3 Positioning Techniques 9
2.4 LBS Applications 12
2.5 Bluetooth Low Energy 14
Chapter 3 IHNIPS Architecture and Positioning Mechanisms 20
3.1 IHNIPS Overview 21
3.2 Offline Phase 23
3.2.1 AP’s RSSI Threshold Mechanism 25
3.3 Online Phase 26
3.3.1 Subspace Selection Method 30
3.3.2 Fuzzy Area 32
3.3.3 Awake Beacon Method 35
3.4 Positioning Mechanisms 37
Chapter 4 Performance Analysis 42
4.1 Field Trial 42
4.2 System Specification 44
4.3 Performance Analysis 47
4.3.1 KNN Algorithm Analysis 49
4.3.2 Threshold Value Analysis 51
4.3.3 Different Case Comparison 53
4.4 Summary 57
Chapter 5 Conclusion and Future Work 59
5.1 Conclusion 59
5.2 Future Work 60
References 62

[1]V. Pierlot and M.V. Droogenbroeck, “A New Three Object Triangulation Algorithm for Mobile Robot Positioning,” IEEE Transactions On Robotics, Vol. 30, No. 3, pp.566-577, 2014.
[2]R. Faragher and R. Harle, “Location Fingerprinting with Bluetooth Low Energy Beacons,” IEEE Journal on Selected Areas in Communications, 2015. (DOI:10.1109/JSAC.2015.2430281)
[3]F. Yaghoubi, A.A. Abbasfar, and B. Maham, “Energy-Efficient RSSI-Based Localization for Wireless Sensor Networks,” IEEE Communications Letters, Vol. 18, No. 6, pp. 973-976, 2014.
[4]T. Stoyanova, F. Kerasiotis, C. Antonopoulos and G. Papadopoulos, “RSS-based Localization for Wireless Sensor Networks in Practice,” Proceedings of the International Symposium on Communication Systems, Networks & Digital Sign, pp. 134-139, 2014.
[5]N. Chuku and A. Nasipuri, “Performance Evaluation of an RSSI Based Localization Scheme for Wireless Sensor Networks to Mitigate Shadowing Effects,” Proceedings of the IEEE WCNC'14 Track 3, pp.3124-3129, 2014.
[6]J.X.,Qin Qin and K. Zeng, “A Distance Measurement Wireless Localization Correction Algorithm Based On RSSI,” Proceedings of the International Symposium on Computational Intelligence and Design, pp. 276-278, 2014.
[7]Y.H. Geng, J. He, H.K. Deng and K. Pahlavan, “Modeling the Effect of Human Body on TOA Ranging for Indoor Human Tracking with Wrist Mounted Sensor,” Proceedings of the International Symposium on Wireless Personal Multimedia Communications, pp. 1-6, 2013.
[8]R. Kaune, “Accuracy Studies for TDOA and TOA Localization,” Proceedings of the International Conference on Information Fusion, pp. 408-415, 2012.
[9]S. Uebayashi, M. Shimizu and T. Fujiwara, “A Study of TDOA Positioning Using UWB Reflected Waves,” Proceedings of the IEEE Vehicular Technology Conference, pp. 1-5, 2013.
[10]J.Y Yoon, J.W. Kim, W.H Lee and D.S. Eom, “A TDoA-based Localization Using Precise Time-Synchronization,” Proceedings of the International Conference on Advanced Communication Technology, pp. 1266-1271, 2012.
[11]J.R. Jiang, C.M. Lin, F.Y. Lin and S.T. Huang, “ALRD: AoA Localization with RSSI Differences of Directional Antennas for Wireless Sensor Networks,” Proceedings of the International Conference on Information Society, pp. 304-309, 2012.
[12]B.D.S. Muswieck, J.L. Russi and M.V.T. Heckler, “Hybrid Method Uses RSS and AoA to Establish a Low-Cost Localization System,” Proceedings of the Argentine Symposium and Conference on Embedded Systems, pp. 1-6, 2013.
[13]S.Y. Nam, “Localization of Access Points Based on Signal Strength Measured by a Mobile User Node,” IEEE Communications Letters, Vol. 18, No. 8, pp. 1407-1410, 2014.
[14]O. Artemenko, A. Rubina, O. Golokolenko and A. Mitschele-Thiel, “How Different Trajectories of Moving Beacons Influence the Localization of Nodes in Disaster Scenarios Using Wireless Communication,” Proceedings of the International Symposium on Wireless Personal Multimedia Communications, pp.367-372, 2014.
[15]M. Choi, W.K. Park and I. Lee, “Smart Office Energy Management System Using Bluetooth Low Energy Based Beacons and a Mobile App,” Proceedings of the IEEE International Conference on Consumer Electronics, pp.501-502, 2015.
[16]S. Kajioka, T. Mori, T. Uchiya, I. Takumi and H. Matsuo, “Experiment of Indoor Position Presumption Based on RSSI of Bluetooth LE Beacon,” Proceedings of the IEEE 3rd Global Conference on Consumer Electronics, pp.337-339, 2014.
[17]A.A. Wahab, A. Khattab and Y.A. Fahmy, “Two-Way TOA with Limited Dead Reckoning for GPS-Free Vehicle Localization Using Single RSU,” Proceedings of the International Conference on ITS Telecommunications, pp. 244-249, 2013.
[18]Y. Cho, M. Ji, Y. Lee and S. Park, “WiFi AP Position Estimation Using Contribution from Heterogeneous Mobile Devices,” Proceedings of the IEEE/ION Position Location and Navigation Symposium, pp. 562-567, 2012.
[19]N. Miwa, S. Tagashira, H. Matsuda, T. Tsutsui, Y. Arakawa and A. Fukuda,, “A Multilateration-based Localization Scheme for Adhoc Wireless Positioning Networks Used in Information-oriented Construction,” Proceedings of the IEEE International Conference on Advanced Information Networking and Applications, pp. 690-695, 2013.
[20]Y.F. Zhou, J. Li and L. Lamont, “Multilateration Localization in the Presence of Anchor Location Uncertainties,” Proceedings of the IEEE Global Communications Conference, pp. 309-314, 2012.
[21]J.L. Liu, Y.H. Wan, B.G. Xu, S.L. Tang, X.K. Ding, Q. Wan, “A Novel Indoor Positioning Method Based on Location Fingerprinting,” Proceedings of the International Conference on Communications, Circuits and Systems, pp. 239-242, 2013.
[22]J.S. Leu and H.J. Tzeng, “Received Signal Strength Fingerprint and Footprint Assisted Indoor Positioning Based on Ambient Wi-Fi Signals,” Proceedings of the IEEE Vehicular Technology Conference, pp. 1-5, 2012.
[23]H.H. Liu and Y.N. Yang, “WiFi-Based Indoor Positioning for Multi-Floor Environment,” Proceedings of the IEEE Region 10 Conference TENCON, pp. 597-601, 2011.
[24]S. Asano, Y. Wakuda, N. Koshizuka and K. Sakamura, “A Robust Pedestrian Dead-Reckoning Positioning Based on Pedestrian Behavior and Sensor Validity,” Proceedings of the IEEE/ION Position Location and Navigation Symposium, pp. 328-333, 2012.
[25]L. Li and X. Lin, “Apply Pedestrian Dead Reckoning to Indoor Wi-Fi Positioning Based on Fingerprinting,” Proceedings of the IEEE International Conference on Communication Technology, pp. 206-210, 2013.
[26]X. Liu, Q. Man, H. Lu and X. Lin, “Wi-Fi/MARG/GPS Integrated System for Seamless Mobile Positioning,” Proceedings of the IEEE Wireless Communications and Networking Conference, pp. 2323-2328, 2013.
[27]M.H. Park, H.C. Kim, S.J. Lee and K.S. Bae, “Performance Evaluation of Android Location Service at the Urban Canyon,” Proceedings of the International Conference on Advanced Communication Technology, pp. 662-665, 2014.
[28]J. Park, D. Lee and J. Park, “Digital Map Based Pose Improvement for Outdoor Augmented Reality,” Proceedings of the IEEE International Symposium on Mixed and Augmented Reality, pp. 309-310, 2012.
[29]A. Chandra, S. Jain and M.A. Qadeer, “GPS Locator: An Application for Location Tracking and Sharing Using GPS for Java Enabled Handhelds,” Proceedings of the International Conference on Computational Intelligence and Communication Networks, pp. 406-410, 2011.
[30]M.H. Park, W.H. Kim, S.J. Lee and H.C. Kim, “GPS-Tag for Indoor Location Information and Additional User Information Providing,” Proceedings of the International Conference on Advanced Communication Technology, pp. 894-897, 2012.
[31]O.A. Hammadi, A.A. Hebsi, M.J. Zemerly and J.W.P. Ng, “Indoor Localization and Guidance Using Portable Smartphones,” Proceedings of the IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, pp. 337-341, 2012.
[32]T. Reinsch, Y. Wang, M. Knechtel, M. Ameling and P. Herzig, “CINA - A Crowdsourced Indoor Navigation Assistant,” Proceedings of the IEEE/ACM International Conference on Utility and Cloud Computing, pp. 500-505, 2013.
 
[33]H. Wang, L. Zhu and A. Chin, “An Indoor Location-Based Social Network for Managing Office Resource and Connecting People,” Proceedings of the International Conference on Ubiquitous Intelligence & Computing and International Conference on Autonomic & Trusted Computing, pp. 481-483, 2010.
[34]W.U.L.J.R. Perera and M.S. Karunarathne, “Enhancing and Speeding-Up Real-Time-Shopping Using an Indoor Map, Intelligent Suggestions and Calculations, Built Upon a Smart Phone Application,” Proceedings of the IEEE International Conference on Industrial and Information Systems, pp. 583-588, 2013.
[35]C.H. Lu, H.H. Kuo, C.W. Hsiao, Y.L. Ho, Y.H. Lin and H.P. Ma, “Localization with WLAN on Smartphones in Hospitals,” Proceedings of the IEEE International Conference on e-Health Networking, Applications & Services, pp. 534-538, 2013.