LoCo: boosting for indoor location classification combining Wi-Fi and BLE

Abstract

In recent years, there has been an explosion of services that leverage location to provide users novel and engaging experiences. However, many applications fail to realize their full potential because of limitations in current location technologies. Current frameworks work well outdoors but fare poorly indoors. In this paper, we present LoCo, a new framework that can provide highly accurate room-level indoor location. LoCo does not require users to carry specialized location hardware—it uses radios that are present in most contemporary devices and, combined with a boosting classification technique, provides a significant runtime performance improvement. We provide experiments that show the combined radio technique can achieve accuracy that improves on current state-of-the-art Wi-Fi-only techniques. LoCo is designed to be easily deployed within an environment and readily leveraged by application developers. We believe LoCo’s high accuracy and accessibility can drive a new wave of location-driven applications and services.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Notes

  1. 1.

    http://support.apple.com/en-us/HT202880.

  2. 2.

    http://www.todhq.com/.

  3. 3.

    http://www.estimote.com/.

  4. 4.

    RSSI is measured on a logarithmic scale.

  5. 5.

    Multiple BSSIDs are associated with a single Wi-Fi access point. Here, we include and withhold BSSIDs by access point.

References

  1. 1.

    Aalto L, Göthlin N, Korhonen J, Ojala T (2004) Bluetooth and WAP push based location-aware mobile advertising system. In: Proceedings of the 2nd international conference on mobile systems, applications, and services, ACM, pp 49–58

  2. 2.

    An X, Wang J, Prasad RV, Niemegeers I (2006) OPT: online person tracking system for context-awareness in wireless personal network. In: Proceedings of the 2nd international workshop on Multi-hop ad hoc networks: from theory to reality, ACM, pp 47–54

  3. 3.

    Andoni A, Indyk P (2006) Efficient algorithms for substring near neighbor problem. In: Proceedings of the seventeenth annual ACM-SIAM symposium on discrete algorithm, SODA ’06, ACM, New York, NY, USA, pp 1203–1212. doi:10.1145/1109557.1109690

  4. 4.

    Aparicio S, Perez J, Bernardos A, Casar J (2008) A fusion method based on Bluetooth and WLAN technologies for indoor location. In: IEEE international conference on multisensor fusion and integration for intelligent systems, 2008. MFI 2008, pp 487–491

  5. 5.

    Biehl JT, Cooper M, Filby G, Kratz S (2014) Loco: a ready-to-deploy framework for efficient room localization using Wi-Fi. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing, UbiComp ’14, ACM, New York, NY, USA, pp 183–187. doi:10.1145/2632048.2636083

  6. 6.

    Bolliger P (2008) Redpin-adaptive, zero-configuration indoor localization through user collaboration. In: Proceedings of the first ACM international workshop on mobile entity localization and tracking in GPS-less environments, MELT ’08, ACM, New York, NY, USA, pp 55–60. doi:10.1145/1410012.1410025

  7. 7.

    Chintalapudi K, Padmanabha Iyer A, Padmanabhan VN (2010) Indoor localization without the pain. In: Proceedings of the sixteenth annual international conference on mobile computing and networking, MobiCom ’10, ACM, New York, NY, USA, pp 173–184. doi:10.1145/1859995.1860016

  8. 8.

    Cong L, Zhuang W (2002) Hybrid tdoa/aoa mobile user location for wideband cdma cellular systems. IEEE Trans Wirel Commun 1(3):439–447. doi:10.1109/TWC.2002.800542

    Article  Google Scholar 

  9. 9.

    Dempster A, Li B, Quader I (2008) Errors in deterministic wireless fingerprinting systems for localization. In: ISWPC 2008, pp 111–115

  10. 10.

    Ferdinand P, Müller S, Ritschel T, Wechselberger U (2005) The eduventure—a new approach of digital game based learning combining virtual and mobile augmented reality games episodes. In: Pre-conference workshop? Game based Learning? of DeLFI 2005 and GMW 2005 conference, vol. 13. Rostock

  11. 11.

    Freund Y, Schapire R (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139. doi:10.1006/jcss.1997.1504

    Article  MathSciNet  MATH  Google Scholar 

  12. 12.

    Gross HM, Böhme HJ, Schröter C, Müller S, König A, Martin C, Merten M, Bley A (2008) Shopbot: progress in developing an interactive mobile shopping assistant for everyday use. In: IEEE international conference on systems, man and cybernetics, 2008. SMC 2008. pp 3471–3478

  13. 13.

    Harter A, Hopper A, Steggles P, Ward A, Webster P (2002) The anatomy of a context-aware application. Wirel Netw 8(2/3):187–197

    Article  MATH  Google Scholar 

  14. 14.

    Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning. Springer, New York

    Google Scholar 

  15. 15.

    Heller F (2008) Corona: realizing an interactive experience in visually untouchable rooms using continuous virtual audio spaces. Master’s thesis, RWTH Aachen University

  16. 16.

    Hightower J, Want R, Borriello G (2000) Spoton: An indoor 3D location sensing technology based on RF signal strength. UW CSE 00–02-02, University of Washington, Department of Computer Science and Engineering, Seattle, WA 1

  17. 17.

    Li P (2010) Robust logitboost and adaptive base class (abc) logitboost. In: UAI 2010, proceedings of the twenty-sixth conference on uncertainty in artificial intelligence, pp 302–311. arXiv:1203.3491

  18. 18.

    Lin H, Zhang Y, Griss M, Landa I (2009) Enhanced indoor locationing in a congested Wi-Fi environment. Tech. Rep. MRC-TR-2009-04, Carnegie Mellon Silicon Valley

  19. 19.

    Liu H, Darabi H, Banerjee P, Liu J (2007) Survey of wireless indoor positioning techniques and systems. IEEE Trans Syst Man Cybern Part C Appl Rev 37(6):1067–1080

    Article  Google Scholar 

  20. 20.

    Madigan D, Elnahrawy E, Martin RP, Ju WH, Krishnan P, Krishnakumar AS (2005) Bayesian indoor positioning systems. In: Proceedings of the 24th joint conference of the IEEE Computer and Communication Societies (INFOCOM 2005)

  21. 21.

    Martin E, Vinyals O, Friedland G, Bajcsy R (2010) Precise indoor localization using smart phones. In: Proceedings of the international conference on Multimedia, MM ’10, ACM, New York, NY, USA, pp 787–790. doi:10.1145/1873951.1874078

  22. 22.

    Mukherjee I, Schapire R (2010) A theory of multiclass boosting. In: Lafferty J, Williams CKI, Shawe-Taylor J, Zemel R, Culotta A (eds) Advances in Neural Information Processing Systems, vol. 23., pp 1714–1722

  23. 23.

    Ni LM, Liu Y, Lau YC, Patil AP (2004) Landmarc: indoor location sensing using active RFID. Wirel Netw 10(6):701–710

    Article  Google Scholar 

  24. 24.

    Niculescu D, Nath B (2003) Ad hoc positioning system (APS) using AOA. In: INFOCOM 2003. Twenty-second annual joint conference of the IEEE computer and communications. IEEE Societies, vol. 3., pp 1734–1743. doi:10.1109/INFCOM.2003.1209196

  25. 25.

    Olguín DO, Waber BN, Kim T, Mohan A, Ara K, Pentland A (2009) Sensible organizations: technology and methodology for automatically measuring organizational behavior. Trans. Syst. Man Cybern. Part B 39(1):43–55. doi:10.1109/TSMCB.2008.2006638

    Article  Google Scholar 

  26. 26.

    Ouyang RW, Wong AKS, Lea CT, Chiang M (2011) Indoor location estimation with reduced calibration exploiting unlabeled data via hybrid generative/discriminative learning. In: IEEE transactions on mobile computing, vol. 99. (PrePrints). doi:10.1109/TMC.2011.193

  27. 27.

    Pan JJ, Pan SJ, Yin J, Ni LM, Yang Q (2012) Tracking mobile users in wireless networks via semi-supervised colocalization. In: IEEE transactions on pattern analysis and machine intelligence, pp 587–600

  28. 28.

    Priyantha NB, Chakraborty A, Balakrishnan H (2000) The cricket location-support system. In: Proceedings of the 6th annual international conference on Mobile computing and networking, ACM, pp 32–43

  29. 29.

    Rishabh I, Kimber D, Adcock J (2012) Indoor localization using controlled ambient sounds. In: International conference on indoor positioning and indoor navigation (IPIN), pp 1–10. doi:10.1109/IPIN.2012.6418905

  30. 30.

    Rodrigues ML, Vieira LFM, Campos MF (2011) Fingerprinting-based radio localization in indoor environments using multiple wireless technologies. In: IEEE 22nd international symposium on personal indoor and mobile radio communications (PIMRC), pp 1203–1207

  31. 31.

    Roxin A, Gaber J, Wack M, Nait-Sidi-Moh A (2007) Survey of wireless geolocation techniques. In: IEEE Globecom

  32. 32.

    Sen S, Lee J, Kim KH, Congdon P (2013) Avoiding multipath to revive inbuilding wifi localization. In: Proceeding of the 11th annual international conference on mobile systems, applications, and services, MobiSys ’13, ACM, New York, NY, USA, pp 249–262. doi:10.1145/2462456.2464463

  33. 33.

    Sukthankar G (2002) The dynadoom visualization agent: A handheld interface for live action gaming. In: Workshop on ubiquitous agents on embedded, wearable, and mobile devices (conference on intelligent agents and multiagent systems), Bologna, Italy

  34. 34.

    Terrenghi L, Zimmermann A (2004) Tailored audio augmented environments for museums. In: Proceedings of the 9th international conference on Intelligent user interfaces, ACM, pp 334–336

  35. 35.

    Tod beacons (2014) http://www.todhq.com/

  36. 36.

    Wakkary R, Newby K, Hatala M, Evernden D, Droumeva M (2004) Interactive audio content: an approach to audio content for a dynamic museum experience through augmented audio reality and adaptive information retrieval. In: Museums and the web conference

  37. 37.

    Want R, Hopper A, Falcão V, Gibbons J (1992) The active badge location system. ACM Trans Inf Syst 10(1):91–102

    Article  Google Scholar 

  38. 38.

    Wiese J, Biehl JT, Turner T, van Melle W, Girgensohn A (2011) Beyond ’yesterday’s tomorrow’: towards the design of awareness technologies for the contemporary worker. In: Proceedings of the 13th international conference on human computer interaction with mobile devices and services, MobileHCI ’11, ACM, New York, NY, USA, pp 455–464. doi:10.1145/2037373.2037441

  39. 39.

    Xiong J, Jamieson K (2012) Towards fine-grained radio-based indoor location. In: Proceedings of the twelfth workshop on mobile computing systems and applications, HotMobile ’12, ACM, New York, NY, USA, pp 13:1–13:6. doi:10.1145/2162081.2162100

  40. 40.

    Youssef M, Agrawala A (2005) The horus wlan location determination system. In: Proceedings of the 3rd international conference on Mobile systems, applications, and services (MobiSys ’05), pp 205–218

  41. 41.

    Zhu J, Zou H, Rosset S, Hastie T (2009) Multi-class adaboost. Stat Interface 2:349–360

    Article  MathSciNet  MATH  Google Scholar 

  42. 42.

    Zhu W, Owen CB, Li H, Lee JH (2004) Personalized in-store e-commerce with the promopad: an augmented reality shopping assistant. Electron J E-commer Tools Appl 1(3):1–19

    MATH  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Matthew Cooper.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cooper, M., Biehl, J., Filby, G. et al. LoCo: boosting for indoor location classification combining Wi-Fi and BLE. Pers Ubiquit Comput 20, 83–96 (2016). https://doi.org/10.1007/s00779-015-0899-z

Download citation

Keywords

  • Indoor location detection
  • Multi-radio indoor positioning
  • Location-aware application frameworks