More Web Proxy on the site http://driver.im/

research-article

A realistic evaluation and comparison of indoor location technologies: experiences and lessons learned

Authors:

Dimitrios Lymberopoulos,

Romit Roy Choudhury,

Vlado Handziski,

Souvik SenAuthors Info & Claims

IPSN '15: Proceedings of the 14th International Conference on Information Processing in Sensor Networks

Pages 178 - 189

https://doi.org/10.1145/2737095.2737726

Published: 13 April 2015 Publication History

Abstract

We present the results, experiences and lessons learned from comparing a diverse set of technical approaches to indoor localization during the 2014 Microsoft Indoor Localization Competition. 22 different solutions to indoor localization from different teams around the world were put to test in the same unfamiliar space over the course of 2 days, allowing us to directly compare the accuracy and overhead of various technologies. In this paper, we provide a detailed analysis of the evaluation study's results, discuss the current state-of-the-art in indoor localization, and highlight the areas that, based on our experience from organizing this event, need to be improved to enable the adoption of indoor location services.

References

[1]

Microsoft indoor localization competition. http://research.microsoft.com/en-us/events/ipsn2014indoorlocalizatinocompetition/.

[2]

T. E. Abrudan, Z. Xiao, A. Markham, and N. Trigoni. Distortion rejecting magneto-inductive 3-D localization (MagLoc). IEEE Journal on Selected Areas in Communications, pages 1--14, 2015. (to appear).

[3]

S. Adler, S. Schmitt, Y. Yang, Y. Zhao, and M. Kyas. FubLoc: Accurate Range-based Indoor Localization and Tracking. Technical report, Microsoft Indoor Localization Competition, 2014.

[4]

A. Ashok, C. Xu, T. Vu, M. Gruteser, R. Howard, Y. Zhang, N. Mandayam, W. Yuan, and K. Dana. Bifocus: Using radio-optical beacons for an augmented reality search application. In Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys '13, pages 507--508, New York, NY, USA, 2013. ACM.

Digital Library

[5]

P. Bahl and V. N. Padmanabhan. RADAR: An In-Building RF-based User Location and Tracking System. In Proceedings of INFOCOM, 2000.

[6]

C. Beder and M. Klepal. Fingerprinting based localisation revisited - a rigorous approach for comparing RSSI measurements coping with missed access points and differing antenna attenuations. In 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2012.

[7]

R. Bruno and F. Delmastro. Design and analysis of a bluetooth-based indoor localization system. In Personal Wireless Communications, Lecture Notes in Computer Science. 2003.

[8]

Y. Chen, D. Lymberopoulos, J. Liu, and B. Priyantha. Fm-based indoor localization. In The 10th International Conference on Mobile Systems, Applications, and Services, MobiSys'12, 2012.

Digital Library

[9]

Y. Chen, D. Lymberopoulos, J. Liu, and B. Priyantha. Indoor localization using fm signals. IEEE Transactions on Mobile Computing, 12(8): 1502--1517, 2013.

Digital Library

[10]

J. Chung, M. Donahoe, C. Schmandt, I.-J. Kim, P. Razavai, and M. Wiseman. Indoor location sensing using geo-magnetism. In MobiSys, 2011.

Digital Library

[11]

V. Dentamaro, D. Colucci, and P. Ambrosini. Nextome: Indoor Positioning and Navigation System. http://www.nextome.org/index.php.

[12]

S.-H. Fang, J.-C. Chen, H.-R. Huang, and T.-N. Lin. Metropolitan-scale location estimation using fm radio with analysis of measurements. In IWCMC, 2008.

[13]

A. S. Ferraz, A. G. Alvino, L. Q. L. Martins, and P. A. Bello. Ubee. in: An Indoor Location solution for mobile devices. Technical report, Microsoft Indoor Localization Competition, 2014.

[14]

A. Ghose, V. Chandel, A. K. Agrawal, A. Kumar, N. Ahmed, and C. Bhaumik. Smartevactrak: A people counting and coarse-level localization solution for efficient evacuation of large buildings. In Pervasive Computing and Communications Workshops (PERCOM Workshops), 2015 IEEE International Conference on. IEEE, 2015.

[15]

A. Haeberlen, E. Flannery, A. M. Ladd, A. Rudys, D. S. Wallach, and L. E. Kavraki. Practical robust localization over large-scale 802.11 wireless networks. In MobiCom, 2004.

Digital Library

[16]

T. V. Haute, E. D. Poorter, J. Rossey, I. Moerman, V. Handziski, A. Behboodi, F. Lemic, A. Wolisz, N. Wiström, T. Voigt, P. Crombez, P. Verhoeve, and J. J. de las Heras. The EVARILOS Benchmarking Handbook: Evaluation of RF-based Indoor Localization Solutions. In MERMAT 2013, 5 2013.

[17]

Z. Jiang, J. Zhao, J. Han, S. Tang, J. Zhao, and W. Xi. Wi-fi fingerprint based indoor localization without indoor space measurement. In Mobile Ad-Hoc and Sensor Systems (MASS), 2013 IEEE 10th International Conference on, pages 384--392, Oct 2013.

Digital Library

[18]

Z. Jiangy, W. Xiy, X.-Y. Li, J. Zhaoy, and J. Hany. HiLoc: A TDoA-Fingerprint Hybrid Indoor Localization System. Technical report, Microsoft Indoor Localization Competition, 2014.

[19]

J. Krumm, G. Cermak, and E. Horvitz. Rightspot: A novel sense of location for a smart personal object. In UBICOMP, 2003.

[20]

P. Lazik and A. Rowe. Indoor pseudo-ranging of mobile devices using ultrasonic chirps. In Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, SenSys '12, pages 99--112, New York, NY, USA, 2012. ACM.

Digital Library

[21]

F. Lemic, J. Büsch, M. Chwalisz, V. Handziski, and A. Wolisz. Infrastructure for Benchmarking RF-based Indoor Localization under Controlled Interference. In Proceedings of 3rd International Conference on Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS'14), November 2014.

[22]

C.-L. Li, C. Laoudias, G. Larkou, Y.-K. Tsai, D. Zeinalipour-Yazti, and C. G. Panayiotou. Indoor Geolocation on Multi-sensor Smartphones. In Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys '13. ACM, 2013.

Digital Library

[23]

L. Li, P. Hu, C. Peng, G. Shen, and F. Zhao. Epsilon: A Visible Light Based Positioning System. In 11th USENIX Symposium on Networked Systems Design and Implementation (NSDI 14), pages 331--343, Seattle, WA, Apr. 2014. USENIX Association.

Digital Library

[24]

L. Li, C. Zhao, G. Shen, and F. Zhao. Indoor Localization with Multi-modalities. Technical report, Microsoft Indoor Localization Competition, 2014.

[25]

A. Marcaletti, M. Rea, D. Giustiniano, and V. Lenders. WINS: Tracking of Mobile Devices with WiFi Time-Of-Flight. Technical report, Microsoft Indoor Localization Competition, 2014.

[26]

A. Matic, A. Popleteev, V. Osmani, and O. Mayora-Ibarra. Fm radio for indoor localization with spontaneous recalibration. Pervasive Mob. Comput., 6, December 2010.

Digital Library

[27]

V. Moghtadaiee, A. G. Dempster, and B. Li. Accuracy indicator for fingerprinting localization systems. In Proceedings of PLANS, IEEE/ION, 2012.

[28]

V. Moghtadaiee, A. G. Dempster, and S. Lim. Indoor localization using fm radio signals: A fingerprinting approach. In International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2011.

[29]

V. Moghtadaiee, A. G. Dempster, and S. Lim. Indoor positioning based on fm signals and wi-fi signals. In Proceedings of IGNSS Symposium, 2011.

[30]

L. M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil. Landmarc: indoor location sensing using active rfid. Wirel. Netw., 10, November 2004.

Digital Library

[31]

V. Otsason, A. Varshavsky, A. L. Marca, and E. de Lara. Accurate gsm indoor localization. In UbiComp, 2005.

Digital Library

[32]

G. Pirkl and P. Lukowicz. Robust, low cost indoor positioning using magnetic resonant coupling. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing(Ubicomp-2012), pages 431--440. ACM, 2012.

Digital Library

[33]

A. Popleteev. Indoor positioning using FM radio signals. PhD thesis, University of Trento, April 2011.

[34]

A. Popleteev, V. Osmani, and O. Mayora. Investigation of indoor localization with ambient fm radio stations. In Proceedings of PerCom, 2012.

[35]

K. Prieditis, I. Drikis, and L. Selavo. Santarray: passive element array antenna for wireless sensor networks. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, pages 433--434. ACM, 2010.

Digital Library

[36]

N. B. Priyantha, A. Chakraborty, and H. Balakrishnan. The cricket location-support system. In Mobicom, 2000.

Digital Library

[37]

R. Reimann, A. Bestmann, and M. Ernst. Locating Technology for AAL Applications with Direction Finding and Distance Measurement by Narrow Bandwidth Phase Analysis. In Evaluating AAL Systems Through Competitive Benchmarking, volume 362 of Communications in Computer and Information Science, pages 52--62. Springer Berlin Heidelberg, 2013.

[38]

V. Sark and E. Grass. Software Defined Radio for Time of Flight Based Ranging and Localization. Technical report, Microsoft Indoor Localization Competition, 2014.

[39]

T. Schmid and D. Lee. High Resolution Indoor RF Ranging. Technical report, Microsoft Indoor Localization Competition, 2014.

[40]

S. Sen, B. Radunovic, R. R. Choudhury, and T. Minka. Precise indoor localization using phy layer information. In Proceedings of HotNets, 2011.

Digital Library

[41]

S. Sen, B. Radunovic, R. R. Choudhury, and T. Minka. You are facing the mona lisa: spot localization using phy layer information. In Proceedings of MobiSys, 2012.

Digital Library

[42]

S. Szymczak, T. Jankowski, and M. Nikodem. Underground GPS (UGPS) - precondition for location based services underground. In Innowacyjne techniki i technologie dla górnictwa: bezpieczeństwo - efektywność - niezawodność, pages 33--41. Instytut Techniki Górniczej KOMAG, 2014.

[43]

S. P. Tarzia, P. A. Dinda, R. P. Dick, and G. Memik. Indoor localization without infrastructure using the acoustic background spectrum. In MobiSys, 2011.

Digital Library

[44]

A. Varshavsky, E. de Lara, J. Hightower, A. LaMarca, and V. Otsason. Gsm indoor localization. Pervasive Mob. Comput., 3, December 2007.

Digital Library

[45]

H. Wang, S. Sen, A. Elgohary, M. Farid, M. Youssef, and R. R. Choudhury. No need to war-drive: Unsupervised indoor localization. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, MobiSys '12, pages 197--210, New York, NY, USA, 2012. ACM.

Digital Library

[46]

R. Want, A. Hopper, V. Falcão, and J. Gibbons. The active badge location system. ACM Trans. Inf. Syst., 10, January 1992.

Digital Library

[47]

Z. Xiao, H. Wen, A. Markham, and N. Trigoni. Lightweight map matching for indoor localization using conditional random fields. In The International Conference on Information Processing in Sensor Networks (IPSN'14), Berlin, Germany, 2014.

Digital Library

[48]

A. Youssef, J. Krumm, E. Miller, G. Cermak, and E. Horvitz. Computing location from ambient fm radio signals. In IEEE WCNC, 2005.

[49]

M. Youssef and A. Agrawala. The horus wlan location determination system. In MobiSys, 2005.

Digital Library

[50]

C. Zhang, J. Luo, and J. Wu. A Dual-Sensor Enabled Indoor Localization System with Crowdsensing Spot Survey. In Proc. of the 10th IEEE DCOSS, pages 75--82, 2014.

Digital Library

[51]

H. Zou, X. Lu, H. Jiang, and L. Xie. A fast and precise indoor localization algorithm based on an online sequential extreme learning machine. Sensors, 15(1): 1804--1824, 2015.

[52]

H. Zou, L. Xie, Q.-S. Jia, and H. Wang. Platform and algorithm development for a rfid-based indoor positioning system. Unmanned Systems, 2(03): 279--291, 2014.

Cited By

Anagnostopoulos GBarsocchi PCrivello APendão CSilva ITorres-Sospedra J(2025)ORDIP: Principle, practice and guidelines for open research data in indoor positioningInternet of Things10.1016/j.iot.2024.101485(101485)Online publication date: Jan-2025
https://doi.org/10.1016/j.iot.2024.101485
Jederman EStrohmeier MLenders VSchmitt JBalzarotti DXu W(2024)RECORDProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699242(6113-6130)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3699242
Lemic FBartra GLópez AGomez JStruye JDressler FAbadal SPérez X(2024)Insights from the Design Space Exploration of Flow-Guided Nanoscale LocalizationProceedings of the 11th Annual ACM International Conference on Nanoscale Computing and Communication10.1145/3686015.3689359(103-108)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3686015.3689359
Show More Cited By

Index Terms

A realistic evaluation and comparison of indoor location technologies: experiences and lessons learned
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems

Recommendations

Robust, cost-effective and scalable localization in large indoor areas

Indoor location information plays a fundamental role in supporting various interesting location-aware indoor applications. Widely deployed WiFi networks make it feasible to perform indoor localization by first establishing a received signal strength (...
Indoor Localization Using FM Signals

The major challenge for accurate fingerprint-based indoor localization is the design of robust and discriminative wireless signatures. Even though WiFi received signal strength indicator (RSSI) signatures are widely available indoors, they vary ...
Contour-based Trilateration for Indoor Fingerprinting Localization
SenSys '15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

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 ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

IPSN '15: Proceedings of the 14th International Conference on Information Processing in Sensor Networks

April 2015

430 pages

ISBN:9781450334754

DOI:10.1145/2737095

General Chair:
Suman Nath
Microsoft Research
,
Program Chairs:
Bhaskar Krishnamachari
University of Southern California
,
Anthony Rowe
Carnegie Mellon University

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

IEEE-SPS: Signal Processing Society
IEEE
IEEE-CSS: Control Systems Society
ACM: Association for Computing Machinery
SIGBED: ACM Special Interest Group on Embedded Systems
IEEE-CS IRTS: IEEE CS Internet, Real Time Systems

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

IPSN '15

Sponsor:

IEEE-SPS
IEEE-CSS
ACM
SIGBED
IEEE-CS IRTS

IPSN '15: The 14th International Conference on Information Processing in Sensor Networks

April 13 - 16, 2015

Washington, Seattle

Acceptance Rates

Overall Acceptance Rate 143 of 593 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

268
Total Citations
View Citations
2,358
Total Downloads

Downloads (Last 12 months)64
Downloads (Last 6 weeks)7

Reflects downloads up to 07 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Anagnostopoulos GBarsocchi PCrivello APendão CSilva ITorres-Sospedra J(2025)ORDIP: Principle, practice and guidelines for open research data in indoor positioningInternet of Things10.1016/j.iot.2024.101485(101485)Online publication date: Jan-2025
https://doi.org/10.1016/j.iot.2024.101485
Jederman EStrohmeier MLenders VSchmitt JBalzarotti DXu W(2024)RECORDProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699242(6113-6130)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3699242
Lemic FBartra GLópez AGomez JStruye JDressler FAbadal SPérez X(2024)Insights from the Design Space Exploration of Flow-Guided Nanoscale LocalizationProceedings of the 11th Annual ACM International Conference on Nanoscale Computing and Communication10.1145/3686015.3689359(103-108)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3686015.3689359
Li XCheng G(2024)An Efficient Landmarks-based Accuracy Evaluation method for Indoor Pedestrian Localization Using SmartphonesProceedings of the 5th International Conference on Computer Information and Big Data Applications10.1145/3671151.3671248(542-547)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3671151.3671248
Stavrou VGriva ABardaki C(2024)Deploying a Location-Based Coupon Recommendation Service in Retail: Challenges and Lessons LearntIEEE Transactions on Technology and Society10.1109/TTS.2024.34485065:4(368-376)Online publication date: Dec-2024
https://doi.org/10.1109/TTS.2024.3448506
Zhu FLuo KTao XZhang X(2024)Deep Learning Based Vehicle-Mounted Environmental Context Awareness via GNSS SignalIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.335087425:8(9498-9511)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3350874
Calvo Bartra GLemic FPascual GPérez Rodas AStruye JDelgado CCosta Pérez X(2024)Graph Neural Networks as an Enabler of Terahertz-Based Flow-Guided Nanoscale Localization Over Highly Erroneous Raw DataIEEE Journal on Selected Areas in Communications10.1109/JSAC.2024.339925742:8(1992-2008)Online publication date: Aug-2024
https://doi.org/10.1109/JSAC.2024.3399257
Potortì FCrivello ALee SVladimirov BPark SChen YWang LChen RZhao FZhuge YLuo HPerez-Navarro AJiménez AWang HLiang HDe Cock CPlets DCui YXiong ZLi XDing YFranco FPolo FRodríguez FMoreira APendão CSilva IOrtiz MZhu NLi ZRenaudin VWei DJi XZhang WWang YDing LKuang JZhang XDou ZYang CKram SStahlke MMutschler CCoene SLi CVenus ALeitinger ETertinek SWitrisal KWang YWang SJin BZhang FSu CWang ZLi SLi XLi SPan MZheng WLuo KMa ZGao YChang JRen HGuo WTorres-Sospedra J(2024)Offsite Evaluation of Localization Systems: Criteria, Systems, and Results From IPIN 2021 and 2022 CompetitionsIEEE Journal of Indoor and Seamless Positioning and Navigation10.1109/JISPIN.2024.33558402(92-129)Online publication date: 2024
https://doi.org/10.1109/JISPIN.2024.3355840
Royet LTorres-Sospedra JMontoliu RPérez-Navarro A(2024)Analysis of the origin of differences in position among different fingerprinting datasets2024 International Symposium on Sensing and Instrumentation in 5G and IoT Era (ISSI)10.1109/ISSI63632.2024.10720468(1-6)Online publication date: 29-Aug-2024
https://doi.org/10.1109/ISSI63632.2024.10720468
Ayabakan TKerestecioğlu F(2024)Fault Tolerant Indoor Positioning Based on Federated Kalman FilterJournal of Signal Processing Systems10.1007/s11265-024-01913-y96:4-5(273-285)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s11265-024-01913-y
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents