research-article

No need to war-drive: unsupervised indoor localization

Authors:

Ahmed Elgohary,

Moustafa Farid,

Moustafa Youssef,

Romit Roy ChoudhuryAuthors Info & Claims

MobiSys '12: Proceedings of the 10th international conference on Mobile systems, applications, and services

Pages 197 - 210

https://doi.org/10.1145/2307636.2307655

Published: 25 June 2012 Publication History

Abstract

We propose UnLoc, an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present identifiable signatures on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone's accelerometer; a corridor-corner may overhear a unique set of WiFi access points; a specific spot may experience an unusual magnetic fluctuation. We hypothesize that these kind of signatures naturally exist in the environment, and can be envisioned as internal landmarks of a building. Mobile devices that "sense" these landmarks can recalibrate their locations, while dead-reckoning schemes can track them between landmarks. Results from 3 different indoor settings, including a shopping mall, demonstrate median location errors of 1:69m. War-driving is not necessary, neither are floorplans the system simultaneously computes the locations of users and landmarks, in a manner that they converge reasonably quickly. We believe this is an unconventional approach to indoor localization, holding promise for real-world deployment.

References

[1]

V. Bahl et al. RADAR: An in-building rf-based user location and tracking system. In INFOCOM, 2000.

[2]

I. Constandache, R. R. Choudhury, and I. Rhee. Towards mobile phone localization without war-driving. In INFOCOM. IEEE, 2010.

Digital Library

[3]

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

Digital Library

[4]

P. Krishnan and others. A system for LEASE: Location estimation assisted by stationary emitters for indoor rf wireless networks. In IEEE Infocom, March 2004.

[5]

Yu-Chung Cheng et al. Accuracy characterization for metropolitan-scale wi-fi localization. In MobiSys, 2005.

Digital Library

[6]

William G. Griswold et al. Activecampus: Experiments in community-oriented ubiquitous computing. Computer, 2004.

Digital Library

[7]

M. Youssef et al. Pinpoint: An asynchronous time-based location determination system. In ACM Mobisys, 2006.

Digital Library

[8]

S. J. Fontana, R. J.and Gunderson. Ultra-wideband precision asset location system. In IEEE Conference on Ultra Wideband Systems and Technologies, May 2002.

[9]

D. Niculescu and others. VOR base stations for indoor 802.11 positioning. In ACM MobiCom, 2004.

Digital Library

[10]

N. B. Priyantha. The cricket indoor location system. PhD thesis, MIT, 2005.

Digital Library

[11]

A. Savvides and M. Han, C. C. Srivastava. Dynamic fine-grained localization in ad-hoc networks of sensors. In ACM MobiCom, 2001.

Digital Library

[12]

J. Xiong and K. Jamieson. SecureAngle: improving wireless security using angle-of-arrival information. In ACM HotNets, 2010.

Digital Library

[13]

S. Sen et al. Precise Indoor Localization using PHY Layer Information. In ACM HotNets, 2011.

Digital Library

[14]

M. Azizyan et al. SurroundSense: Mobile phone localization via ambience fingerprinting. In MOBICOM, 2009.

Digital Library

[15]

M. Youssef, A. Youssef, C. Rieger, U. Shankar, and A. Agrawala. Pinpoint: An asynchronous time-based location determination system. In ACM MobiSys, 2006.

Digital Library

[16]

S. Kumar and D. Shepherd. Sensit: Sensor information technology for the warfighter. In Proc. 4th Int. Conf. on Information Fusion, 2001.

[17]

I. Constandache et al. Did you see bob?: human localization using mobile phones. In ACM MobiCom, 2010.

Digital Library

[18]

L. Bao and S. Intille. Activity recognition from user-annotated acceleration data. Pervasive Computing, pages 1--17, 2004.

[19]

J. Chung et al. Indoor location sensing using geo-magnetism. In MobiSys. ACM, 2011.

Digital Library

[20]

F. Evennou and F. Marx. Advanced integration of wifi and inertial navigation systems for indoor mobile positioning. EURASIP journal on applied signal processing, 2006:164--164, 2006.

Digital Library

[21]

Step size in pedometers. s. http://walking.about.com/cs/pedometers/a/pedometerset.htm.

[22]

M. Alzantot and M. Youssef. Uptime: Ubiquitous pedestrian tracking using mobile phones. In IEEE Wireless Communications and Networking Conference (WCNC 2012). IEEE.

[23]

K. Chintalapudi, A. Iyer, and V. Padmanabhan. Indoor localization without the pain. In MOBICOM, 2010.

Digital Library

[24]

O. Woodman and R. Harle. Pedestrian localisation for indoor environments. In UbiComp. ACM, 2008.

Digital Library

[25]

M. Youssef et al. Gac: Energy-efficient hybrid gps-accelerometer-compass gsm localization. In IEEE GLOBECOM 2010.

[26]

M. Montemerlo et al. Fastslam: A factored solution to the simultaneous localization and mapping problem. In Proceedings of the National conference on Artificial Intelligence, 2002.

Digital Library

[27]

B. Ferris, D. Fox, and N. Lawrence. Wifi-slam using gaussian process latent variable models. In Proceedings of the 20th International Joint Conference on Artificial Intelligence, pages 2480--2485, 2007.

Digital Library

[28]

S. P. Tarzia et al. Indoor localization without infrastructure using the acoustic background spectrum. In ACM MobiSys, 2011.

Digital Library

[29]

J. Lester, T. Choudhury, and G. Borriello. A practical approach to recognizing physical activities. Pervasive Computing, pages 1--16, 2006.

Digital Library

Cited By

Li CZhang CChen BXu SXu LYan B(2024)Ultrasonic Array-Based Multi-Source Fusion Indoor Positioning TechnologySensors10.3390/s2420664124:20(6641)Online publication date: 15-Oct-2024
https://doi.org/10.3390/s24206641
Kim DSuh Y(2024)Automatic Fingerprint Data Labeling Using WiFi Signal and Smartphone Camera for Indoor PositioningWireless Communications and Mobile Computing10.1155/2024/86632462024:1Online publication date: 10-Jun-2024
https://doi.org/10.1155/2024/8663246
Wang CHe WPierson TKotz D(2024)Moat: Adaptive Inside/Outside Detection System for Smart HomesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997518:4(1-31)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699751
Show More Cited By

Index Terms

No need to war-drive: unsupervised indoor localization

Recommendations

Demo: unsupervised indoor localization
MobiSys '12: Proceedings of the 10th international conference on Mobile systems, applications, and services

We propose UnLoc [1], an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present an identifiable signature on one or more sensing dimensions. An ...
Video: Unsupervised indoor localization (UnLoc): beyond the prototype
MobiSys '14: Proceedings of the 12th annual international conference on Mobile systems, applications, and services

This video presents a demo of indoor localization in multiple settings. In the demo, a user walks with a smartphone and the user's location is shown on the phone's screen in real time. Our system, called Unsupervised Indoor Localization (UnLoc) utilizes ...
Corona: Positioning Adjacent Device with Asymmetric Bluetooth Low Energy RSSI Distributions
UIST '15: Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology

We introduce Corona, a novel spatial sensing technique that implicitly locates adjacent mobile devices in the same plane by examining asymmetric Bluetooth Low Energy RSSI distributions. The underlying phenomenon is that the off-center BLE antenna and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

MobiSys '12: Proceedings of the 10th international conference on Mobile systems, applications, and services

June 2012

548 pages

ISBN:9781450313018

DOI:10.1145/2307636

General Chair:
Nigel Davies
Lancaster University, UK
,
Program Chairs:
Srinivasan Seshan
Carnegie Mellon University, USA
,
Lin Zhong
Rice University, USA

Copyright © 2012 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

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

In-Cooperation

SIGOPS: ACM Special Interest Group on Operating Systems

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MobiSys'12

Sponsor:

SIGMOBILE

MobiSys'12: The 10th International Conference on Mobile Systems, Applications, and Services

June 25 - 29, 2012

Lake District, Low Wood Bay, UK

Acceptance Rates

Overall Acceptance Rate 274 of 1,679 submissions, 16%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

706
Total Citations
View Citations
4,554
Total Downloads

Downloads (Last 12 months)122
Downloads (Last 6 weeks)13

Reflects downloads up to 24 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Li CZhang CChen BXu SXu LYan B(2024)Ultrasonic Array-Based Multi-Source Fusion Indoor Positioning TechnologySensors10.3390/s2420664124:20(6641)Online publication date: 15-Oct-2024
https://doi.org/10.3390/s24206641
Kim DSuh Y(2024)Automatic Fingerprint Data Labeling Using WiFi Signal and Smartphone Camera for Indoor PositioningWireless Communications and Mobile Computing10.1155/2024/86632462024:1Online publication date: 10-Jun-2024
https://doi.org/10.1155/2024/8663246
Wang CHe WPierson TKotz D(2024)Moat: Adaptive Inside/Outside Detection System for Smart HomesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997518:4(1-31)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699751
Yonetani RBaba JFurukawa YKostakos VKay JHoang T(2024)RetailOpt: Opt-In, Easy-to-Deploy Trajectory Estimation from Smartphone Motion Data and Retail Facility InformationProceedings of the 2024 ACM International Symposium on Wearable Computers10.1145/3675095.3676623(125-132)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675095.3676623
Sheshadri SHara K(2024)Conversational LocalizationProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314047:4(1-32)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3631404
Li DXu JYang ZTang C(2024)Train Once, Locate Anytime for Anyone: Adversarial Learning-based Wireless LocalizationACM Transactions on Sensor Networks10.1145/361409520:2(1-21)Online publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1145/3614095
Eleftherakis SSantaromita GRea MCosta-Pérez XGiustiniano D(2024)SPRING+: Smartphone Positioning From a Single WiFi Access PointIEEE Transactions on Mobile Computing10.1109/TMC.2024.336724123:10(9549-9566)Online publication date: Oct-2024
https://doi.org/10.1109/TMC.2024.3367241
Yang HXia ZShin JHua JMao YZhong S(2024)A Comprehensive Study of Trajectory Forgery and Detection in Location-Based ServicesIEEE Transactions on Mobile Computing10.1109/TMC.2023.327341123:4(3228-3242)Online publication date: Apr-2024
https://doi.org/10.1109/TMC.2023.3273411
Wang YWong AChan SMow W(2024)Leto: Crowdsourced Radio Map Construction With Learned Topology and a Few LandmarksIEEE Transactions on Mobile Computing10.1109/TMC.2023.326619823:4(2795-2812)Online publication date: Apr-2024
https://doi.org/10.1109/TMC.2023.3266198
Wang MLi LLiu JChen R(2024)Neural Network Aided Factor Graph Optimization for Collaborative Pedestrian NavigationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.330996325:1(303-314)Online publication date: Jan-2024
https://doi.org/10.1109/TITS.2023.3309963
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