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CRISP: cooperation among smartphones to improve indoor position information


Accurate indoor location information remains a challenge without incorporating extensive fingerprinting approaches or sophisticated infrastructures within buildings. Nevertheless, modern smartphones are equipped with sensors and radios that can detect movement and can be used to predict location. Dead reckoning applications on a smartphone may attempt to track a person’s movement or locate a person within an indoor environment. Nevertheless, smartphone positioning applications continue to be inaccurate. We propose a new approach, CRISP—cooperating to improve smartphone positioning, which assumes that dead reckoning has inaccuracies, but leverages opportunities of the interaction of multiple smartphones. Each smartphone computes its own position, and then shares it with other nearby smartphones. The signal strengths of multiple radios that are used on smartphones estimate distances between the devices. While individual smartphones may provide some positioning (possibly inaccurate) information, accuracy may improve when several smartphones cooperate and share position information through multiple iterations. Via indoor experimentation and simulation, we evaluate our approach and believe it is promising as an inexpensive and passive means to improve position information without complex data training and fusion. The accuracy of CRISP is within a meter. In addition, CRISP possibly leads to better results for a number of applications, including exercise profiling.

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  1. 1.

    Küpper, A. (2005). Location-based services: Fundamentals and operation. Hobeken, NJ: Wiley.

  2. 2.

    Misra, P., & Enge, P. (2006). Global positioning system: Signals, measurements and performance (2nd ed.). Lincoln, MA: Ganga-Jamuna Press.

  3. 3.

    Fukuju, Y., Minami, M., Morikawa, H., & Aoyama, T. (2003). Dolphin: An autonomous indoor positioning system in ubiquitous computing environment. In WSTFEUS (pp. 53–56).

  4. 4.

    Priyantha, N. B. (2005). The cricket indoor location system. Ph.D. dissertation. Cambridge, MA: Massachusetts Institute of Technology.

  5. 5.

    Ni, L., Liu, Y., Lau, Y., & Patil, A. (2004). Landmarc: Indoor location sensing using active RFID. Springer Wireless Networks, 10(6), 701–710.

  6. 6.

    Yang, L., Chen, Y., Li, X., Xiao, C., Li, M., & Liu, Y. (2014). Tagoram: Real-time tracking of mobile rfid tags to high precision using cots devices. In Proceedings of the ACM MobiCom (pp. 237–248).

  7. 7.

    Liu, T., Yang, L., Lin, Q., Guo, Y., & Liu, Y. (2014). Anchor-free backscatter positioning for RFID tags with high accuracy. In Proceedings of the IEEE INFOCOM (pp. 379–387).

  8. 8.

    Youssef, M., Mah, M., & Agrawala, A. (2007). Challenges: Device-free passive localization for wireless environments. In Proceedings of the ACM MobiCom (pp. 222–229).

  9. 9.

    Bahl, P., & Padmanabhan, V. (2000). Radar: An in-building rfbased user location and tracking system. In Proceedings of the IEEE INFOCOM (pp. 775–784).

  10. 10.

    Yang, Z., Zhou, Z., & Liu, Y. (2013). From RSSI to CSI: Indoor localization via channel response. ACM Computing Surveys, 46(2), 25–32.

  11. 11.

    Xu, C., Firner, B., Moore, R., Zhang, Y., Trappe, W., Howard, R., et al. (2013). SCPL: Indoor devicefree multi-subject counting and localization using radio signal strength. In ‘Proceedings of the IEEE/ACM IPSN (pp. 79–90).

  12. 12.

    Steinhoff, U., & Schiele, B. (2010). Dead reckoning from the pocket-an experimental study. In Proceedings of the IEEE PerCom (pp. 162–170).

  13. 13.

    UM6 sensor.

  14. 14.

    Kuo, Y., Pannuto, P., Hsiao, K., & Dutta, P. (2014). Luxapose: Indoor positioning with mobile phones and visible light. In Proceedings of the ACM MobiCom (pp. 447–458).

  15. 15.

    Xu, Q., Zheng, R., & Hranilovic, S. (2015). Idyll: Indoor localization using inertial and light sensors on smartphones. In Proceedings of the ACM UbiComp (pp. 307–318).

  16. 16.

    Tung, Y., & Shin, K. (2015). Echotag: Accurate infrastructure-free indoor location tagging with smartphones. In Proceedings of the ACM MobiCom (pp. 525–536).

  17. 17.

    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.

  18. 18.

    Banerjee, N., Agarwal, S., Bahll, P., & Chandra, R. (2010). Virtual compass: Relative positioning to sense mobile social interactions. In Proceedings of the ACM pervasive (pp. 1–21).

  19. 19.

    Liu, H., et al. (2014). Accurate WiFi based localization for smartphones using peer assistance. IEEE Transactions on Mobile Computing, 13(10), 2199–2214.

  20. 20.

    Tversky, A., & Gati, I. (1982). Similarity, separability, and the triangle inequality. American Psychological Association Psychological Review, 89(2), 123.

  21. 21.

    Wang, L., Hu, W., & Tan, T. (2003). Recent developments in human motion analysis. Elsevier Pattern Recognition, 36(3), 585–601.

  22. 22.

    Bluetooth, S. (2010). Bluetooth specification.

  23. 23.

    Baronti, P., Pillai, P., Chook, V., Chessa, S., Gotta, A., & Hu, Y. (2007). Wireless sensor networks: A survey on the state of the art and the 802.15. 4 and ZigBee standards. Elsevier Computer Communications, 30(7), 1655–1695.

  24. 24.

    Inoue, S., & Hattori, Y. (2011). Toward high-level activity recognition from accelerometers on mobile phones. In Proceedings of the IEEE iThings/CPSCom (pp. 225–231).

  25. 25.

    Li, F., Zhao, C., Ding, G., Gong, J., Liu, C., & Zhao, F. (2012). A reliable and accurate indoor localization method using phone inertial sensors. In Proceedings of the ACM UbiComp (pp. 421–430).

  26. 26.

    Hilsenbeck, S., Bobkov, D., Schroth, G., Huitl, R., & Steinbach, E. (2014). Graph-based data fusion of pedometer and WiFi measurements for mobile indoor positioning. In Proceedings of the ACM UbiComp (pp. 147–158).

  27. 27.

    Levis, P., Madden, S., Polastre, J., Szewczyk, R., Whitehouse, K., Woo, A., et al. (2005). TinyOS: An operating system for sensor networks. In W. Weber, J. Rabaey, & E. H. Aarts (Eds.), Ambient intelligence (pp. 115–148). Berlin: Springer.

  28. 28.

    Maghdid, H. S., Lami, I. A., Ghafoor, K. Z., & Lloret, J. (2016). Seamless outdoors–indoors localization solutions on smartphones: Implementation and challenges. ACM Computing Surveys, 48(4), article. 53.

  29. 29.

    Qiu, C., & Mutka, M. W. (2016). iFrame: Dynamic indoor map construction through automatic mobile sensing. In Proceedings of the IEEE PerCom (pp. 1–9).

  30. 30.

    Noom Walk.

  31. 31.


  32. 32.

    Battery Widget Pro.

  33. 33.

    Chon, Y., Talipov, E., Shin, H., & Cha. H. (2011). Mobility prediction-based smartphone energy optimization for everyday location monitoring. In Proceedings of the ACM SenSys (pp. 82–95).

  34. 34.

    Dissanayake, M., Newman, P., Clark, S., Durrant, H., & Csorba, M. (2001). A solution to the simultaneous localization and map building (slam) problem. IEEE Transactions on Robotics and Automation, 17(3), 229–241.

  35. 35.

    Suger, B., Tipaldi, G., Spinello, L., & Burgard, W. (2014). An approach to solving large-scale slam problems with a small memory footprint. In Proceedings of the IEEE ICRA (pp. 3632–3637).

  36. 36.

    Want, R., Hopper, A., Falcao, V., & Gibbons, J. (1992). The active badge location system. ACM Transactions on Information System, 10(1), 91–102.

  37. 37.

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

  38. 38.

    Bellavista, P., Corradi, A., & Giannelli, C. (2008). A layered infrastructure for mobility-aware best connectivity in theheterogeneous wireless internet. In Proceedings of the ACM MobilWare (vol. 25).

  39. 39.

    Bellavista, P., Corradi, A., & Giannelli, C. (2005). Efficiently managing location information with privacy requirements in wi-fi networks: A middleware approach. In Proceedings of the IEEE ISWCS (pp. 91–95).

  40. 40.

    Seifeldin, M., Saeed, A., Kosba, A., El-Keyi, A., & Youssef, M. (2013). Nuzzer: A large-scale device-free passive localization system for wireless environments. IEEE Transactions Mobile Computing, 12(7), 1321–1334.

  41. 41.

    Chintalapudi, K., Lyer, A., & Padmanabhan, V. (2010). Indoor localization without the pain. In Proceedings of the ACM MobiCom (pp. 173–184).

  42. 42.

    Kotaru, M., Joshi, K., Bharadia, D., & Katti, S. (2015). SpotFi: Decimeter level localization using WiFi. In Proceedings of the ACM SIGCOMM (pp. 269–282).

  43. 43.

    Vasisht, D., Kumar, S., & Katabi, D. (2016). Decimeter-level localization with a single WiFi access point. In Proceedings of the USENIX NSDI (pp. 165–178).

  44. 44.

    Azizyan, M., Constandache, I., & Choudhury, R. R. (2009). Surroundsense: Mobile phone localization using ambient sound and light. In Proceedings of the ACM MobiCom (pp. 261–272).

  45. 45.

    Chon, J., & Cha, H. (2011). Lifemap: A smartphone-based context provider for location-based services. IEEE Pervasive Computing, 2, 58–67.

  46. 46.

    Wang, H., Sen, S., Elgohary, A., Farid, M., Youssef, M., & Choudhury, R. R. (2012). No need to war-drive: Unsupervised indoor localization. In Proceedings of the ACM MobiSys (pp. 197–210).

  47. 47.

    Mariakakis, A., Sen, S., Lee, J., & Kim, K. (2014). Sail: Single access point-based indoor localization. In Proceedings of the ACM MobiSys (pp. 315–328).

  48. 48.

    Yang, Z., Wang, Z., Zhang, J., Huang, C., & Zhang, Q. (2015). Wearables can afford: Light-weight indoor positioning with visible light. In Proceedings of the ACM MobiSys (pp. 317–330).

  49. 49.

    Roy, N., Wang, H., & Choudhury, R. R. (2014). I am a smartphone and i can tell my user’s walking direction. In Proceedings of the ACM MobiSys (pp. 329–342).

  50. 50.

    Rai, A., Chintalapudi, K., Padmanabhan, V., & Sen, R. (2012). Zee: Zero-effort crowdsourcing for indoor localization. In Proceedings of the ACM MobiCom (pp. 293–304).

  51. 51.

    Liu, K., Liu, X., & Li, X. (2013). Guoguo: Enabling fine-grained indoor localization via smartphone. In Proceedings of the ACM MobiSys (pp. 235–248).

  52. 52.

    Sen, S., Lee, J., Kim, K., & Congdon, P. (2013). Avoiding multipath to revive inbuilding WiFi localization. In Proceedings of the ACM MobiSys (pp. 249–262).

  53. 53.

    Hongman, W., Xiaocheng, Z., & Jiangbo, C (2011). Acceleration and orientation multisensor pedometer application design and implementation on the android platform. In Proceedings of the IEEE IMCCC (pp. 249–253).

  54. 54.

    S Health.

  55. 55.


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Correspondence to Chen Qiu.

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Qiu, C., Mutka, M.W. CRISP: cooperation among smartphones to improve indoor position information. Wireless Netw 24, 867–884 (2018).

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  • Indoor positioning
  • Smartphone
  • Pedometer