research-article

TransitLabel: A Crowd-Sensing System for Automatic Labeling of Transit Stations Semantics

Authors:

Moustafa Elhamshary,

Moustafa Youssef,

Akira Uchiyama,

Hirozumi Yamaguchi,

Teruo HigashinoAuthors Info & Claims

MobiSys '16: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services

Pages 193 - 206

https://doi.org/10.1145/2906388.2906395

Published: 20 June 2016 Publication History

Abstract

We present TransitLabel, a crowd-sensing system for automatic enrichment of transit stations indoor floorplans with different semantics like ticket vending machines, entrance gates, drink vending machines, platforms, cars' waiting lines, restrooms, lockers, waiting (sitting) areas, among others. Our key observations show that certain passengers' activities (e.g., purchasing tickets, crossing entrance gates, etc) present identifiable signatures on one or more cell-phone sensors. TransitLabel leverages this fact to automatically and unobtrusively recognize different passengers' activities, which in turn are mined to infer their uniquely associated stations semantics. Furthermore, the locations of the discovered semantics are automatically estimated from the inaccurate passengers' positions when these semantics are identified. We evaluate TransitLabel through a field experiment in eight different train stations in Japan. Our results show that TransitLabel can detect the fine-grained stations semantics accurately with 7.7% false positive rate and 7.5% false negative rate on average. In addition, it can consistently detect the location of discovered semantics accurately, achieving an error within 2.5m on average for all semantics. Finally, we show that TransitLabel has a small energy footprint on cell-phones, could be generalized to other stations, and is robust to different phone placements; highlighting its promise as a ubiquitous indoor maps enriching service.

References

[1]

A. M. AbdelAziz and M. Youssef. The diversity and scale matter: Ubiquitous transportation mode detection using single cell tower information. In Proceedings of the 81st Vehicular Technology Conference (VTC Spring), pages 1--5. IEEE, 2015.

[2]

H. Abdelnasser, K. A. Harras, and M. Youssef. UbiBreathe: A ubiquitous non-invasive WiFi-based breathing estimator. Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), pages 277--286, 2015.

Digital Library

[3]

H. Abdelnasser, R. Mohamed, A. Elgohary, M. Farid, H. Wang, S. Sen, R. Choudhury, and M. Youssef. SemanticSLAM: Using environment landmarks for unsupervised indoor localization. IEEE Transactions on Mobile Computing, (1):1--1.

[4]

H. Aly, A. Basalamah, and M. Youssef. Map++: A crowd-sensing system for automatic map semantics identification. In Proceedings of the Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pages 546--554. IEEE, 2014.

[5]

H. Aly and M. Youssef. Dejavu: An accurate energy-efficient outdoor localization system. In Proceedings of the 21st ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL), pages 154--163. ACM, 2013.

Digital Library

[6]

H. Aly and M. Youssef. Zephyr: Ubiquitous accurate multi-sensor fusion-based respiratory rate estimation using smartphones. In Proceedings of IEEE Conference on Computer Communications (INFOCOM). IEEE, 2016.

[7]

M. Alzantot and M. Youssef. CrowdInside: Automatic construction of indoor floorplans. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems (SIGSPATIAL), pages 99--108. ACM, 2012.

Digital Library

[8]

M. Alzantot and M. Youssef. UPTIME: Ubiquitous pedestrian tracking using mobile phones. In Proceedings of Wireless Communications and Networking Conference (WCNC), pages 3204--3209. IEEE, 2012.

[9]

Y. Chon, N. D. Lane, Y. Kim, F. Zhao, and H. Cha. Understanding the coverage and scalability of place-centric crowdsensing. In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing (UbiComp), pages 3--12. ACM, 2013.

Digital Library

[10]

W. S. Cleveland and S. J. Devlin. Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association, 83(403), 1988.

[11]

S. Dernbach, B. Das, N. C. Krishnan, B. L. Thomas, and D. J. Cook. Simple and complex activity recognition through smart phones. In Proceedings of 8th International Conference on Intelligent Environments (IE), pages 214--221. IEEE, 2012.

Digital Library

[12]

K. El-Kafrawy, M. Youssef, A. El-Keyi, and A. Naguib. Propagation modeling for accurate indoor WLAN RSS-based localization. In Proceedings of the 72nd Vehicular Technology Conference Fall (VTC -Fall), pages 1--5. IEEE, 2010.

[13]

R. Elbakly and M. Youssef. A robust zero-calibration RF-based localization system for realistic environments. In Proceedings of the Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). IEEE, 2016.

Digital Library

[14]

A. Eleryan, M. Elsabagh, and M. Youssef. Synthetic generation of radio maps for device-free passive localization. In Proceedings of the Global Telecommunications Conference (GLOBECOM), pages 1--5. IEEE, 2011.

[15]

M. Elhamshary, A. Uchiyama, H. Yamaguchi, and T. Higashino. LandmarkSense: A Mobile Sensing System for Automatic Detection of Railway Stations Landmarks. Technical report, IPSJ SIG, 12 2015.

[16]

M. Elhamshary and M. Youssef. CheckInside: A fine-grained indoor location-based social network. In Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), pages 607--618. ACM, 2014.

Digital Library

[17]

M. Elhamshary and M. Youssef. SemSense: Automatic construction of semantic indoor floorplans. In Proceedings of the 6th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pages 1--11. IEEE, 2015.

[18]

M. Elhamshary, M. Youssef, A. Uchiyama, H. Yamaguchi, and T. Higashino. Activity recognition of railway passengers by fusion of low-power sensors in mobile phones. In Proceedings of the 23rd International Conference on Advances in Geographic Information Systems (SIGSPATIAL), page 57. ACM, 2015.

Digital Library

[19]

M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, volume 96, pages 226--231, 1996.

Digital Library

[20]

M. Fan, A. T. Adams, and K. N. Truong. Public restroom detection on mobile phone via active probing. In Proceedings of the 2014 ACM International Symposium on Wearable Computers (ISWC), pages 27--34. ACM, 2014.

Digital Library

[21]

R. Gao, M. Zhao, T. Ye, F. Ye, Y. Wang, K. Bian, T. Wang, and X. Li. Jigsaw: Indoor floor plan reconstruction via mobile crowdsensing. In Proceedings of the 20th annual international conference on Mobile computing and networking (MobiCom), pages 249--260. ACM, 2014.

Digital Library

[22]

M. Gordon et al. PowerTutor: a power monitor for android-based mobile platforms, 2013.

[23]

S. Hemminki, P. Nurmi, and S. Tarkoma. Accelerometer-based transportation mode detection on smartphones. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys), page 13. ACM, 2013.

Digital Library

[24]

M. Ibrahim and M. Youssef. A hidden markov model for localization using low-end GSM cell phones. In Proceedings of IEEE International Conference on Communications (ICC), pages 1--5. IEEE, 2011.

[25]

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 Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing (UbiComp), pages 315--324. ACM, 2013.

Digital Library

[26]

Y. Jin, H.-S. Toh, W.-S. Soh, and W.-C. Wong. A robust dead-reckoning pedestrian tracking system with low cost sensors. In Proceedings of IEEE International Conference on Pervasive Computing and Communications (PerCom), pages 222--230. IEEE, 2011.

Digital Library

[27]

A. E. Kosba, A. Abdelkader, and M. Youssef. Analysis of a device-free passive tracking system in typical wireless environments. In Proceedings of the 3rd International Conference on New Technologies, Mobility and Security (NTMS), pages 1--5. IEEE, 2009.

Digital Library

[28]

J. Krumm and E. Horvitz. LOCADIO: Inferring motion and location from Wi-Fi signal strengths. In Proceedings of the Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous), pages 4--13, 2004.

[29]

J. R. Kwapisz, G. M. Weiss, and S. A. Moore. Activity recognition using cell phone accelerometers. ACM SigKDD Explorations Newsletter, 12(2):74--82, 2011.

Digital Library

[30]

S. Mazilu and G. Troster. A study on using ambient sensors from smartphones for indoor location detection. In Proceedings of 12th Workshop On positioning, navigation and communication (WPNC). IEEE, 2015.

[31]

P. Mohan, V. N. Padmanabhan, and R. Ramjee. Nericell: Rich monitoring of road and traffic conditions using mobile smartphones. In Proceedings of the 6th ACM conference on Embedded network sensor systems (SenSys), pages 323--336. ACM, 2008.

Digital Library

[32]

N. Mohssen, R. Momtaz, H. Aly, and M. Youssef. It's the human that matters: Accurate user orientation estimation for mobile computing applications. In Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous), pages 70--79. ICST, 2014.

Digital Library

[33]

K. Muralidharan, A. J. Khan, A. Misra, R. K. Balan, and S. Agarwal. Barometric phone sensors: More hype than hope!. In Proceedings of the 15th Workshop on Mobile Computing Systems and Applications (HotMobile), pages 12--18. ACM, 2014.

Digital Library

[34]

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), pages 293--304. ACM, 2012.

Digital Library

[35]

N. Ravi, N. Dandekar, P. Mysore, and M. L. Littman. Activity recognition from accelerometer data. In AAAI, volume 5, pages 1541--1546, 2005.

Digital Library

[36]

S. Reddy, M. Mun, J. Burke, D. Estrin, M. Hansen, and M. Srivastava. Using mobile phones to determine transportation modes. ACM Transactions on Sensor Networks (TOSN), 6(2):13, 2010.

Digital Library

[37]

M. Rossi, S. Feese, O. Amft, N. Braune, S. Martis, and G. Troster. AmbientSense: A real-time ambient sound recognition system for smartphones. In Proceedings of IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pages 230--235. IEEE, 2013.

[38]

M. Rossi, J. Seiter, O. Amft, S. Buchmeier, and G. Tröster. RoomSense: An indoor positioning system for smartphones using active sound probing. In Proceedings of the 4th Augmented Human International Conference (AH), pages 89--95. ACM, 2013.

Digital Library

[39]

M. Seifeldin and M. Youssef. A deterministic large-scale device-free passive localization system for wireless environments. In Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments (PETRA), page 51. ACM, 2010.

Digital Library

[40]

H. Shin, Y. Chon, and H. Cha. Unsupervised construction of an indoor floor plan using a smartphone. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42(6):889--898, 2012.

Digital Library

[41]

T. Sohn, A. Varshavsky, A. LaMarca, M. Y. Chen, T. Choudhury, I. Smith, S. Consolvo, J. Hightower, W. G. Griswold, and E. De Lara. Mobility detection using everyday GSM traces. In Proceedings of international conference on Ubiquitous Computing (UbiComp), pages 212--224. Springer, 2006.

Digital Library

[42]

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), pages 197--210. ACM, 2012.

Digital Library

[43]

M. Youssef. Towards truly ubiquitous indoor localization on a worldwide scale. In Proceedings of the 23rd International Conference on Advances in Geographic Information Systems (SIGSPATIAL), page 12. ACM, 2015.

Digital Library

[44]

M. Youssef. A Decade Later - Challenges: Device-free passive localization for wireless environments. In Proceedings of the Fifth IEEE COSDEO Workshop, IEEE PerCom, 2016.

[45]

M. Youssef, M. Abdallah, and A. Agrawala. Multivariate analysis for probabilistic WLAN location determination systems. In Proceedings of the Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous), pages 353--362. IEEE, 2005.

Digital Library

[46]

M. Youssef and A. Agrawala. The Horus WLAN location determination system. In Proceedings of the 3rd international conference on Mobile systems, applications, and services (MobiSys), pages 205--218. ACM, 2005.

Digital Library

[47]

M. Youssef and A. Agrawala. Location-clustering techniques for WLAN location determination systems. International Journal of Computers and Applications, 28(3):278--284, 2006.

[48]

M. Youssef and A. Agrawala. The Horus location determination system. Wireless Networks, 2008

Digital Library

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Index Terms

TransitLabel: A Crowd-Sensing System for Automatic Labeling of Transit Stations Semantics
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Location based services

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cover image ACM Conferences

MobiSys '16: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services

June 2016

440 pages

ISBN:9781450342698

DOI:10.1145/2906388

General Chairs:
Rajesh Balan
Singapore Management University
,
Archan Misra
Singapore Management University
,
Program Chairs:
Sharad Agarwal
Microsoft
,
Cecilia Mascolo
University of Cambridge

Copyright © 2016 ACM.

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Published: 20 June 2016

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MobiSys'16: The 14th Annual International Conference on Mobile Systems, Applications, and Services

June 26 - 30, 2016

Singapore, Singapore

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MobiSys '16 Paper Acceptance Rate 31 of 197 submissions, 16%;

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

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Cited By

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https://doi.org/10.3390/math11030648
Kim JNam YLee JSuh YHwang I(2023)ProxiFitProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36109207:3(1-32)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1145/3610920
Kim JNam YLee JSuh YHwang I(2023)Demonstrating ProxiFit: Proximal Magnetic Sensing using a Single Commodity Mobile toward Holistic Weight Exercise MonitoringAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610710(151-156)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610710
Tran NDao MNguyen TDinh TNguyen KNguyen P(2023)A Deep Reinforcement Learning-Based Multi-objective Optimization for Crowdsensing-Based Air Quality Monitoring SystemsRecent Challenges in Intelligent Information and Database Systems10.1007/978-3-031-42430-4_36(436-448)Online publication date: 29-Sep-2023
https://doi.org/10.1007/978-3-031-42430-4_36
Ouyang GAbed-Meraim K(2022)Analysis of Magnetic Field Measurements for Indoor PositioningSensors10.3390/s2211401422:11(4014)Online publication date: 25-May-2022
https://doi.org/10.3390/s22114014
Ouyang GAbed-Meraim K(2022)A Survey of Magnetic-Field-Based Indoor LocalizationElectronics10.3390/electronics1106086411:6(864)Online publication date: 9-Mar-2022
https://doi.org/10.3390/electronics11060864
Huang JKong LCheng LDai HQiu MChen GLiu XHuang G(2022)BlockSense: Towards Trustworthy Mobile Crowdsensing via Proof-of-Data BlockchainIEEE Transactions on Mobile Computing10.1109/TMC.2022.3230758(1-17)Online publication date: 2022
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Elbakly RYoussef M(2022)Robust Low-Overhead RF-Based Localization for Realistic EnvironmentsIEEE Transactions on Mobile Computing10.1109/TMC.2020.303462021:6(2168-2179)Online publication date: 1-Jun-2022
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Dissanayake TMaekawa THara TMiyanishi TKawanabe M(2022)IndoLabel: Predicting Indoor Location Class by Discovering Location-Specific Sensor Data MotifsIEEE Sensors Journal10.1109/JSEN.2021.310291622:6(5372-5385)Online publication date: 15-Mar-2022
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Shokry AElhamshary MYoussef M(2021)DynamicSLAM: Leveraging Human Anchors for Ubiquitous Low-Overhead Indoor LocalizationIEEE Transactions on Mobile Computing10.1109/TMC.2020.298432020:8(2563-2575)Online publication date: 1-Aug-2021
https://doi.org/10.1109/TMC.2020.2984320
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