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Contour-based Trilateration for Indoor Fingerprinting Localization

Authors:

S.-H. Gary ChanAuthors Info & Claims

SenSys '15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

Pages 225 - 238

https://doi.org/10.1145/2809695.2809703

Published: 01 November 2015 Publication History

Abstract

Trilateration has been widely and successfully employed to locate outdoor mobile devices due to its accuracy. However, it cannot be directly applied for indoor localization due to issues such as non-line-of-sight measurement and multipath fading. Though fingerprinting overcomes these issues, its accuracy is often hampered by signal noise and the choice of similarity metric between signal vectors. We propose INTRI, a novel, simple and effective indoor localization technique combining the strengths of trilateration and fingerprinting.

For a signal level received from an access point (AP) by the target, INTRI first forms a contour consisting of all the reference points (RPs) of the same signal level for that AP, taking into account the signal noise. The target is hence at the juncture of all the contours. With an optimization formulation following the spirit of trilateration, it then finds the target location by minimizing the distance between the position and all the contours. INTRI further uses an online algorithm based on signal correlation to efficiently calibrate heterogeneous mobile devices to achieve higher accuracy. We have implemented INTRI, and our extensive simulation and experiments in an international airport, a shopping mall and our university campus show that it outperforms recent schemes with much lower location error (often by more than 20%).

References

[1]

N. Alsindi, R. Raulefs, and C. Teolis. Geolocation Techniques: Principles and Applications. Springer, 2012.

Digital Library

[2]

P. Bahl and V. N. Padmanabhan. RADAR: An in-building RF-based user location and tracking system. In Proc. IEEE INFOCOM, volume 2, pages 775--784, 2000.

[3]

S. P. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.

Digital Library

[4]

K. Chang and D. Han. Crowdsourcing-based radio map update automation for Wi-Fi positioning systems. In Proc. ACM SIGSPATIAL GeoCrowd, pages 24--31, 2014.

Digital Library

[5]

Y.-C. Chen, J.-R. Chiang, H.-h. Chu, P. Huang, and A. W. Tsui. Sensor-assisted Wi-Fi indoor location system for adapting to environmental dynamics. In Proc. ACM MSWiM, pages 118--125, 2005.

Digital Library

[6]

K. Chintalapudi, A. Padmanabha Iyer, and V. N. Padmanabhan. Indoor localization without the pain. In Proc. ACM MobiCom, pages 173--184, 2010.

Digital Library

[7]

S.-H. Fang, C.-H. Wang, S.-M. Chiou, and P. Lin. Calibration-free approaches for robust Wi-Fi positioning against device diversity: A performance comparison. In Proc. IEEE VTC Spring, pages 1--5, 2012.

[8]

B. Ferris, D. Fox, and N. D. Lawrence. WiFi-SLAM using Gaussian process latent variable models. In Proc. IJCAI, pages 2480--2485, 2007.

Digital Library

[9]

A. Goswami, L. E. Ortiz, and S. R. Das. WiGEM: A learning-based approach for indoor localization. In Proc. ACM CoNEXT, pages 3:1--3:12, 2011.

Digital Library

[10]

X. Guo, D. Zhang, K. Wu, and L. Ni. MODLoc: Localizing multiple objects in dynamic indoor environment. IEEE Trans. Parallel and Distributed Systems, 25(11):2969--2980, Nov. 2014.

[11]

D. Han, S. Jung, M. Lee, and G. Yoon. Building a practical Wi-Fi-based indoor navigation system. IEEE Pervasive Computing, 13(2):72--79, Apr. 2014.

[12]

S. Han, C. Zhao, W. Meng, and C. Li. Cosine similarity based fingerprinting algorithm in WLAN indoor positioning against device diversity. In Proc. IEEE ICC, pages 4313--4317, 2015.

[13]

S. He and S.-H. Chan. Sectjunction: Wi-Fi indoor localization based on junction of signal sectors. In Proc. IEEE ICC, pages 2605--2610, June 2014.

[14]

S. He and S.-H. G. Chan. Wi-Fi fingerprint-based indoor positioning: Recent advances and comparisons. IEEE Communications Surveys and Tutorials, to appear.

[15]

S. He, S.-H. G. Chan, L. Yu, and N. Liu. Calibration-free fusion of step counter and wireless fingerprints for indoor localization. In Proc. ACM UbiComp, 2015.

Digital Library

[16]

S. He, S.-H. G. Chan, L. Yu, and N. Liu. Fusing noisy fingerprints with distance bounds for indoor localization. In Proc. IEEE INFOCOM, pages 2506--2514, 2015.

[17]

S. Hilsenbeck, D. Bobkov, G. Schroth, R. Huitl, and E. Steinbach. Graph-based data fusion of pedometer and WiFi measurements for mobile indoor positioning. In Proc. ACM UbiComp, pages 147--158, 2014.

Digital Library

[18]

Y. Jiang, Y. Xiang, X. Pan, K. Li, Q. Lv, R. P. Dick, L. Shang, and M. Hannigan. Hallway based automatic indoor floorplan construction using room fingerprints. In Proc. ACM UbiComp, pages 315--324, 2013.

Digital Library

[19]

J. Jun, Y. Gu, L. Cheng, B. Lu, J. Sun, T. Zhu, and J. Niu. Social-Loc: Improving indoor localization with social sensing. In Proc. ACM SenSys, pages 14:1--14:14, 2013.

Digital Library

[20]

Y. Kim, H. Shin, and H. Cha. Smartphone-based Wi-Fi pedestrian-tracking system tolerating the RSS variance problem. In Proc. IEEE PerCom, pages 11--19, Mar. 2012.

[21]

S. Kumar, S. Gil, D. Katabi, and D. Rus. Accurate indoor localization with zero start-up cost. In Proc. ACM MobiCom, pages 483--494, 2014.

Digital Library

[22]

A. Kushki, K. N. Plataniotis, and A. N. Venetsanopoulos. Kernel-based positioning in wireless local area networks. IEEE Trans. Mobile Computing, 6(6):689--705, 2007.

Digital Library

[23]

L. Li, G. Shen, C. Zhao, T. Moscibroda, J.-H. Lin, and F. Zhao. Experiencing and handling the diversity in data density and environmental locality in an indoor positioning service. In Proc. ACM MobiCom, pages 459--470, 2014.

Digital Library

[24]

H. Liu, J. Yang, S. Sidhom, Y. Wang, Y. Chen, and F. Ye. Accurate WiFi based localization for smartphones using peer assistance. IEEE Trans. Mobile Computing, 13(10):2199--2214, Oct 2014.

[25]

A. Mahtab Hossain, Y. Jin, W.-S. Soh, and H. N. Van. SSD: A robust RF location fingerprint addressing mobile devices' heterogeneity. IEEE Trans. Mobile Computing, 12(1):65--77, Jan. 2013.

Digital Library

[26]

A. T. Mariakakis, S. Sen, J. Lee, and K.-H. Kim. SAIL: Single access point-based indoor localization. In Proc. ACM MobiSys, pages 315--328, 2014.

Digital Library

[27]

E. Martin, O. Vinyals, G. Friedland, and R. Bajcsy. Precise indoor localization using smart phones. In Proc. ACM MM, pages 787--790, 2010.

Digital Library

[28]

Mikkel Baun Kjaergaard. Indoor location fingerprinting with heterogeneous clients. Pervasive and Mobile Computing, 7:31 -- 43, 2011.

Digital Library

[29]

P. Mirowski, D. Milioris, P. Whiting, and T. Kam Ho. Probabilistic radio-frequency fingerprinting and localization on the run. Bell Labs Technical Journal, 18(4):111--133, 2014.

Digital Library

[30]

P. Mirowski, P. Whiting, H. Steck, R. Palaniappan, M. MacDonald, D. Hartmann, and T. K. Ho. Probability kernel regression for WiFi localisation. Journal of Location Based Services, 6(2):81--100, 2012.

Digital Library

[31]

R. Nandakumar, K. K. Chintalapudi, and V. N. Padmanabhan. Centaur: Locating devices in an office environment. In Proc. ACM MobiCom, pages 281--292, 2012.

Digital Library

[32]

J.-g. Park, D. Curtis, S. Teller, and J. Ledlie. Implications of device diversity for organic localization. In Proc. IEEE INFOCOM, pages 3182--3190, Apr. 2011.

[33]

G. Shen, Z. Chen, P. Zhang, T. Moscibroda, and Y. Zhang. Walkie-Markie: Indoor pathway mapping made easy. In Proc. USENIX NSDI, pages 85--98, 2013.

Digital Library

[34]

H. Shin, Y. Chon, Y. Kim, and H. Cha. MRI: Model-based radio interpolation for indoor war-walking. IEEE Trans. Mobile Computing, 14(6):1231--1244, June 2015.

[35]

S. S. Skiena. The Algorithm Design Manual. Springer Science & Business Media, 2008.

Digital Library

[36]

W. Sun, J. Liu, C. Wu, Z. Yang, X. Zhang, and Y. Liu. MoLoc: On distinguishing fingerprint twins. In Proc. IEEE ICDCS, pages 226--235, July 2013.

Digital Library

[37]

S.-H. Tsai, S.-Y. Lau, and P. Huang. WSN-based real-time indoor location system at the Taipei World Trade Center: Implementation, deployment, measurement, and experience. In Proc. IEEE Sensors, pages 1--4, Oct 2012.

[38]

H. Wang, S. Sen, A. Elgohary, M. Farid, M. Youssef, and R. R. Choudhury. No need to war-drive: Unsupervised indoor localization. In Proc. ACM MobiSys, pages 197--210, 2012.

Digital Library

[39]

K. Wu, J. Xiao, Y. Yi, D. Chen, X. Luo, and L. M. Ni. CSI-based indoor localization. IEEE Trans. Parallel and Distributed Systems, 24(7):1300--1309, Jul. 2013.

Digital Library

[40]

Z. Yang, Z. Wang, J. Zhang, C. Huang, and Q. Zhang. Wearables can afford: Light-weight indoor positioning with visible light. In Proc. ACM MobiSys, pages 317--330, 2015.

Digital Library

[41]

M. Youssef and A. Agrawala. Handling samples correlation in the Horus system. In Proc. IEEE INFOCOM, pages 1023--1031, 2004.

[42]

M. Youssef and A. Agrawala. The Horus location determination system. Wireless Networks, 14(3):357--374, 2008.

Digital Library

[43]

D. Zhou and T.-H. Lai. A compatible and scalable clock synchronization protocol in IEEE 802.11 ad hoc networks. In Proc. ICPP, pages 295--302, 2005.

Digital Library

Cited By

Jiang JWang JLiu YChen YLiu Y(2024)WiCloak: Protect Location Privacy of WiFi Devices2024 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)10.1109/IPSN61024.2024.00013(101-112)Online publication date: 13-May-2024
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Liu YYang ZLiu YYang Z(2024)Robust Indoor LocalizationLocation, Localization, and Localizability10.1007/978-981-97-3176-3_8(131-162)Online publication date: 12-Jul-2024
https://doi.org/10.1007/978-981-97-3176-3_8
Hu YFan XYin ZQian FJi ZShu YHan YXu QLiu JBahl PCosta XAl Hassanieh HAsadi ACox LPerino DWidmer JGiustiniano D(2023)The Wisdom of 1,170 Teams: Lessons and Experiences from a Large Indoor Localization CompetitionProceedings of the 29th Annual International Conference on Mobile Computing and Networking10.1145/3570361.3592507(1-15)Online publication date: 2-Oct-2023
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Index Terms

Contour-based Trilateration for Indoor Fingerprinting Localization
1. Networks
  1. Network types
    1. Mobile networks
    2. Wireless access networks

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Published In

cover image ACM Conferences

SenSys '15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

November 2015

526 pages

ISBN:9781450336314

DOI:10.1145/2809695

General Chair:
Junehwa Song
KAIST, South Korea
,
Program Chairs:
Tarek Abdelzaher
University of Illinois at Urbana-Champaign, USA
,
Cecilia Mascolo
University of Cambridge, UK

Copyright © 2015 ACM.

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Publication History

Published: 01 November 2015

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The Hong Kong R&D Center for Logistics and Supply Chain Management Enabling Technologies
Hong Kong Research Grant Council (RGC) General Research Fund

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SenSys '15

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SenSys '15: The 13th ACM Conference on Embedded Network Sensor Systems

November 1 - 4, 2015

Seoul, South Korea

Acceptance Rates

SenSys '15 Paper Acceptance Rate 27 of 132 submissions, 20%;

Overall Acceptance Rate 174 of 867 submissions, 20%

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Cited By

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https://doi.org/10.1109/IPSN61024.2024.00013
Liu YYang ZLiu YYang Z(2024)Robust Indoor LocalizationLocation, Localization, and Localizability10.1007/978-981-97-3176-3_8(131-162)Online publication date: 12-Jul-2024
https://doi.org/10.1007/978-981-97-3176-3_8
Hu YFan XYin ZQian FJi ZShu YHan YXu QLiu JBahl PCosta XAl Hassanieh HAsadi ACox LPerino DWidmer JGiustiniano D(2023)The Wisdom of 1,170 Teams: Lessons and Experiences from a Large Indoor Localization CompetitionProceedings of the 29th Annual International Conference on Mobile Computing and Networking10.1145/3570361.3592507(1-15)Online publication date: 2-Oct-2023
https://dl.acm.org/doi/10.1145/3570361.3592507
Li YBarthelemy JSun SPerez PMoran B(2022)Urban Vehicle Localization in Public LoRaWan NetworkIEEE Internet of Things Journal10.1109/JIOT.2021.31217789:12(10283-10294)Online publication date: 15-Jun-2022
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Yang XHe SWang BTabatabaie M(2021)Spatio-Temporal Graph Attention Embedding for Joint Crowd Flow and Transition PredictionsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34950035:4(1-24)Online publication date: 30-Dec-2021
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Li DXu JYang ZWu CLi JLane N(2021)Wireless Localization with Spatial-Temporal Robust FingerprintsACM Transactions on Sensor Networks10.1145/348828118:1(1-23)Online publication date: 22-Oct-2021
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Laoudias CNikitin AKarras PYoussef MZeinalipour-Yazti D(2021)Indoor Quality-of-position Visual Assessment Using Crowdsourced Fingerprint MapsACM Transactions on Spatial Algorithms and Systems10.1145/34330267:2(1-32)Online publication date: 30-Jan-2021
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Yang LYu YLi B(2021)Multi-Floor Indoor Localization Based on RBF Network With Initialization, Calibration, and UpdateIEEE Transactions on Wireless Communications10.1109/TWC.2021.308920220:12(7977-7991)Online publication date: Dec-2021
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Zhao YZhang ZFeng TWong WGarg H(2021)GraphIPS: Calibration-Free and Map-Free Indoor Positioning Using Smartphone Crowdsourced DataIEEE Internet of Things Journal10.1109/JIOT.2020.30047038:1(393-406)Online publication date: 1-Jan-2021
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