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Clustering Wi-Fi fingerprints for indoor–outdoor detection

  • Guy Shtar
  • Bracha Shapira
  • Lior Rokach
Article
  • 142 Downloads

Abstract

This paper presents a method for continuous indoor–outdoor environment detection on mobile devices based solely on Wi-Fi fingerprints. Detection of indoor–outdoor switching is an important part of identifying a user’s context, and it provides important information for upper layer context aware mobile applications such as recommender systems, navigation tools, etc. Moreover, future indoor positioning systems are likely to use Wi-Fi fingerprints, and therefore Wi-Fi receivers will be on most of the time. In contrast to existing research, we believe that these fingerprints should be leveraged, and they serve as the basis of the proposed method. Using various machine learning algorithms, we train a supervised classifier based on features extracted from the raw fingerprints, clusters, and cluster transition graph. The contribution of each of the features to the method is assessed. Our method assumes no prior knowledge of the environment, and a training set consisting of the data collected for just a few hours on a single device is sufficient in order to provide indoor–outdoor classification, even in an unknown location or when using new devices. We evaluate our method in an experiment involving 12 participants during their daily routine, with a total of 828 h’ worth of data collected by the participants. We report a predictive performance of the AUC (area under the curve) of 0.94 using the gradient boosting machine ensemble learning method. We show that our method can be used for other context detection tasks such as learning and recognizing a given building or room.

Keywords

Indoor positioning Indoor localization Mobile computing Context Clustering algorithms 

References

  1. 1.
    Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Context aware computing for the internet of things: A survey. IEEE Communications Surveys and Tutorials, 16(1), 414–454.  https://doi.org/10.1109/SURV.2013.042313.00197.CrossRefGoogle Scholar
  2. 2.
    Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140–150.  https://doi.org/10.1109/MCOM.2010.5560598.CrossRefGoogle Scholar
  3. 3.
    Zou, H., Jiang, H., Luo, Y., Zhu, J., Lu, X., & Xie, L. (2016). Bluedetect: An ibeacon-enabled scheme for accurate and energy-efficient indoor–outdoor detection and seamless location-based service. Sensors, 16(2), 268.  https://doi.org/10.3390/s16020268.CrossRefGoogle Scholar
  4. 4.
    Wang, W., Chang, Q., Li, Q., Shi, Z., & Chen, W. (2016). Indoor–outdoor detection using a smart phone. Sensor Sensors, 16(10), 1563.  https://doi.org/10.3390/s16101563.CrossRefGoogle Scholar
  5. 5.
    Ravindranath, L., Newport, C., Balakrishnan, H., & Madden, S. (2011). Improving wireless network performance using sensor hints. In Proceedings of the 8th USENIX conference on networked systems design and implementation (NSDI’11) (pp. 281–294).Google Scholar
  6. 6.
    Cho, H., Song, J., Park, H., & Hwang, C. (2014). Deterministic indoor detection from dispersions of GPS satellites on the celestial sphere. In The 11th international symposium on location based services.Google Scholar
  7. 7.
    Zhou, P., Zheng, Y., Li, Z., Li, M., & Shen, G. (2012). IODetector: A generic service for indoor outdo or detection. In Proceedings of the 10th ACM conference on embedded network sensor systems (113–126).  https://doi.org/10.1145/2426656.2426668.
  8. 8.
    Anagnostopoulos, T., Garcia, J. C., Goncalves, J., Ferreira, D., Hosio, S., & Kostakos, V. (2017). Environmental exposure assessment using indoor/outdoor detection on smartphones. Personal and Ubiquitous Computing, 21(4), 761–773.  https://doi.org/10.1007/s00779-017-1028-y.CrossRefGoogle Scholar
  9. 9.
    Radu, V., Katsikouli, P., Sarkar, R., & Marina, M. K. (2014). A semi-supervised learning approach for robust indoor–outdoor detection with smartphones. In Proceedings of the 12th ACM conference on embedded network sensor systems (SenSys’14) (pp. 280–294).  https://doi.org/10.1145/2668332.2668347.
  10. 10.
    Canovas, O., Lopez-de-Teruel, P., & Ruiz, A. (2014). WiFiBoost: A terminal-based method for detection of indoor/outdoor places. In Proceedings of the 11th international conference on mobile and ubiquitous systems: Computing, networking and services (MobiQuitous ‘14) (pp. 352–353).  https://doi.org/10.4108/icst.mobiquitous.2014.258063.
  11. 11.
    Edelev, S., Prasad, S. N., Karnal, H., & Hogrefe, D. (2015). Knowledge-assisted location-adaptive technique for indoor–outdoor detection in e-learning. In IEEE international conference on pervasive computing and communication workshops (PerCom Workshops).  https://doi.org/10.1109/percomw.2015.7133985.
  12. 12.
    He, S., Tan, J., & Gary Chan, S. H. (2016). Towards area classification for large-scale fingerprint-based system. In Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing (UbiComp ‘16) (pp. 232–243).  https://doi.org/10.1145/2971648.2971689.
  13. 13.
    Anagnostopoulos, G. G., & Deriaz, M. (2015). Automatic switching between indoor and outdoor position providers. In International conference on indoor positioning and indoor navigation (IPIN).  https://doi.org/10.1109/ipin.2015.7346948.
  14. 14.
    He, H., & Garcia, A. E. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284.  https://doi.org/10.1109/TKDE.2008.239.CrossRefGoogle Scholar
  15. 15.
    Rokach, L., & Maimon, O. (2014). Data mining with decision trees (pp. 34–43). Singapore: World Scientific.CrossRefzbMATHGoogle Scholar
  16. 16.
    Huang, J., & Ling, C. X. (2005). Using AUC and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 17(3), 299–310.  https://doi.org/10.1109/TKDE.2005.50.CrossRefGoogle Scholar
  17. 17.
    Bahl, P., & Padmanabhan, V. N. (2000). RADAR: An in-building RF-based user location and tracking system. In Proceedings of the 19th annual joint conference of the IEEE computer and communications societies (INFOCOM 2000) (pp. 775–784).  https://doi.org/10.1109/infcom.2000.832252.
  18. 18.
    Chintalapudi, K., Padmanabha Iyer, A., & Padmanabhan, V. N. (2010). Indoor localization without the pain. In Proceedings of the 16th annual international conference on mobile computing and networking (MobiCom’10) (pp. 173–184).  https://doi.org/10.1145/1859995.1860016.
  19. 19.
    Rai, A., Chintalapudi, K. K., Padmanabhan, V. N., & Sen, R. (2012). Zee: Zero-effort crowdsourcing for indoor localization. In Proceedings of the 18th annual international conference on mobile computing and networking (Mobicom’12) (pp. 293–304).  https://doi.org/10.1145/2348543.2348580.
  20. 20.
    Wang, H., Elgohary, A., & Choudhury, R. R. (2012). No need to war-drive: Unsupervised indoor localization. In Proceedings of the 10th international conference on mobile systems, applications, and services (MobiSys’12) (pp. 197–210).  https://doi.org/10.1145/2307636.2307655.
  21. 21.
    Teng, X., Guo, D., Guo, Y., Zhou, X., Ding, Z., & Liu, Z. (2017). IONavi: An indoor–outdoor navigation service via mobile crowdsensing. ACM Transactions on Sensor Networks (TOSN).  https://doi.org/10.1145/3043948.Google Scholar
  22. 22.
    Bhargava, P., Krishnamoorthy, S., Karkada Nakshathri, A., Mah, M., & Agrawala, A. (2012). Locus: An indoor localization, tracking and navigation system for multi-story buildings using heuristics derived from Wi-Fi signal strength. In Proceedings of the 9th international conference on mobile and ubiquitous systems: Computing, networking and services (MobiQuitous ‘12) (pp. 212–223).  https://doi.org/10.1007/978-3-642-40238-8_18.
  23. 23.
    Wu, C., Yang, Z., Liu, Y., & Xi, W. (2013). WILL: Wireless indoor localization without site survey. IEEE Transactions on Parallel and Distributed Systems, 24(4), 839–848.  https://doi.org/10.1109/TPDS.2012.179.CrossRefGoogle Scholar
  24. 24.
    Bisio, I., Lavagetto, F., Marchese, M., & Sciarrone, A. (2013). GPS/HPS-and Wi-Fi fingerprint-based location recognition for check-in applications over smartphones in cloud-based LBSs. IEEE Transactions on Multimedia, 15(4), 858–869.  https://doi.org/10.1109/TMM.2013.2239631.CrossRefGoogle Scholar
  25. 25.
    Dousse, O., Eberle, J., & Mertens, M. (2012). Place learning via direct Wi-Fi fingerprint clustering. In IEEE 13th international conference on mobile data management (MDM) (pp. 282–287).  https://doi.org/10.1109/mdm.2012.46.
  26. 26.
    Pulkkinen, T., & Nurmi, P. (2012). AWESOM: Automatic discrete partitioning of indoor spaces for WiFi fingerprinting. In Proceedings of the 10th international conference on pervasive computing (Pervasive’12) (pp. 271–288).  https://doi.org/10.1007/978-3-642-31205-2_17.
  27. 27.
    Kim, H. D., Kim, Y., Estrin, D., & Srivastava, M. B. (2010). SensLoc: Sensing everyday places and paths using less energy. In Proceedings of the 8th ACM conference on embedded networked sensor systems (SenSys’10) (pp. 43–56).  https://doi.org/10.1145/1869983.1869989.
  28. 28.
    Yang, Z., Wu, C., & Liu. Y. (2012). Locating in fingerprint space: Wireless indoor localization with little human intervention. In Proceedings of the 18th annual international conference on mobile computing and networking (Mobicom’12) (pp. 269–280).  https://doi.org/10.1145/2348543.2348578.
  29. 29.
    Machaj, J., Brida, P., Piché, R. (2012). Rank based fingerprinting algorithm for indoor positioning. In 2012 international conference on indoor positioning and indoor navigation (IPIN).  https://doi.org/10.1109/ipin.2011.6071929.
  30. 30.
    Myers, J. L., Well, A. D., & Lorch, R. F., Jr. (2013). Research design and statistical analysis. Mahwah: Lawrence Erlbaum Associates.Google Scholar
  31. 31.
    Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd international conference on knowledge discovery and data mining (KDD’96) (pp. 226–231).Google Scholar
  32. 32.
    Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.  https://doi.org/10.1214/aos/1013203451.MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.  https://doi.org/10.1023/A:1010933404324.CrossRefzbMATHGoogle Scholar
  34. 34.
    Rodríguez, J. J., Kuncheva, L. I., & Alonso, C. J. (2006). Rotation forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(10), 1619–1630.  https://doi.org/10.1109/TPAMI.2006.211.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Software and Information System EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael

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