Device-free indoor localisation with small numbers of anchors

Device-free indoor localisation with small numbers of anchors

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Device-free indoor localisation based on received signal strength (RSS) is unobtrusive and cheap. In a world where most environments are rich in ubiquitous small radio transmitters, it has the potential of being used in a ‘parasitic’ way, by exploiting the transmissions for localisation purposes without any need for additional hardware installation. Starting from state of the art, several steps are needed to reach this aim, the first of which are tackled in this study. The most promising algorithms from the literature are used to experiment in a real-world environment and with a rigorous measurement and analysis framework. Their positioning error performance is analysed versus number and position of devices. The original results obtained show that the currently available RSS-based device-free indoor localisation methods may be well suited to serve as a basis for providing a cheap localisation service in smart environments rich in Internet of things radio devices.


    1. 1)
      • 1. Dohr, A., Modre Opsrian, R., Drobics, M., et al: ‘The Internet of things for ambient assisted living’. 2010 Seventh Int. Conf. Information Technology: New Generations (ITNG), 2010, pp. 804809.
    2. 2)
      • 2. Wu, P., Wu, X., Chen, G., et al: ‘A few bits are enough: energy efficient device-free localization’, Comput. Commun., 2016, 83, pp. 7280.
    3. 3)
      • 3. Wu, P., Chen, G., Zhu, X., et al: ‘Minimizing receivers under link coverage model for device-free surveillance’, Comput. Commun., 2015, 63, pp. 5364.
    4. 4)
      • 4. Ossi, K., Bocca, M., Patwari, N.: ‘Enhancing the accuracy of radio tomographic imaging using channel diversity’. IEEE Int. Conf. MASS., Las Vegas, NV, 2012, pp. 19.
    5. 5)
      • 5. Viani, F., Martinelli, M., Ioriatti, L., et al: ‘Passive real-time localization through wireless sensor networks’. Proc. IEEE Int. Conf. IGARSS., Cape Town, South Africa, 2009, pp. 718721.
    6. 6)
      • 6. Wilson, J., Patwari, N.: ‘Radio tomographic imaging with wireless networks’, IEEE Trans. Mob. Comput., 2010, 9, (5), pp. 621632.
    7. 7)
      • 7. Cassarà, P., Potortì, F., Barsocchi, P., et al: ‘Choosing an RSS device-free localization algorithm for ambient assisted living’. Proc. Int. Conf. Indoor Positioning and Indoor Navigation (IPIN), 2015.
    8. 8)
      • 8. Valtonen, M., Maentausta, J., Vanhala, J.: ‘Tiletrack: capacitive human tracking using floor tiles’. IEEE Int. Conf. Pervasive Computing and Communications 2009 PerCom 2009, 2009, pp. 110.
    9. 9)
      • 9. Krumm, J., Harris, S., Meyers, B., et al: ‘Multi-camera multi-person tracking for easy living’. Proc. Third IEEE Int. Workshop on Visual Surveillance 2000, 2000, pp. 310.
    10. 10)
      • 10. Patel, S.N., Reynolds, M.S., Abowd, G.D.: ‘Detecting human movement by differential air pressure sensing in HVAC system ductwork: an exploration in infrastructure mediated sensing’ (Springer Berlin Heidelberg, Berlin, Heidelberg, 2008), pp. 118.
    11. 11)
      • 11. Barsocchi, P., Potortì, F., Nepa, P.: ‘Device-free indoor localization for AAL applications’. Int. Conf. Wireless Mobile Communication and Healthcare, 2012, pp. 361368.
    12. 12)
      • 12. Fink, A., Beikirch, H.: ‘Device-free localization using redundant 2.4 GHz radio signal strength readings’. 2013 Int. Conf. Indoor Positioning and Indoor Navigation (IPIN), 2013, pp. 17.
    13. 13)
      • 13. Savazzi, S., Nicoli, M., Carminati, F., et al: ‘A Bayesian approach to device-free localization: modeling and experimental assessment’, IEEE J. Sel. Top. Signal Process., 2014, 8, (1), pp. 1629.
    14. 14)
      • 14. Savazzi, S., Nicoli, M., Riva, M.: ‘Radio imaging by cooperative wireless network: localization algorithms and experiments’. Proc. IEEE Int. Conf. WCNC, Paris, France, 2012, pp. 15.
    15. 15)
      • 15. Morelli, C., Nicolini, M., Rampa, V., et al: ‘Hidden Markov models for radio localization in mixed LOS/NLOS conditions’, IEEE Trans. Signal Process., 2007, 5, (4), pp. 15251542.
    16. 16)
      • 16. Wagner, B., Striebing, B., Timmermann, D.: ‘A system for live localization in smart environments’. Proc. IEEE Int. Conf. ICNSC, Evry, France, 2013, pp. 684689.
    17. 17)
      • 17. Wagner, B., Patwari, N., Timmermann, D.: ‘Passive RFID tomographic imaging for device-free user localization’. Proc. IEEE Int. Conf. WPMC, Dresden, Germany, 2012, pp. 120125.
    18. 18)
      • 18. Nannuru, S., Li, Y., Zeng, Y., et al: ‘Radio-frequency tomography for passive indoor multitarget tracking’, IEEE Trans. Mob. Comput., 2013, 12, (12), pp. 23222333.
    19. 19)
      • 19. Wang, J., Gao, Q., Yu, Y., et al: ‘Robust device-free wireless localization based on differential RSS measurements’, IEEE Trans. Ind. Electron., 2013, 60, (12), pp. 59435952.
    20. 20)
      • 20. Men, A., Xue, J., Liu, J., et al: ‘Applying background learning algorithms to radio tomographic imaging’. Proc. IEEE Int. Conf. WPMC, Atlantic City, NJ, 2013, pp. 15.
    21. 21)
      • 21. Maas, D., Wilson, J., Patwari, N.: ‘Toward a rapidly deployable radio tomographic imaging system for tactical operations’. Proc. IEEE Int. Workshop SenseApp, Sydney, Australia, 2013, pp. 18.
    22. 22)
      • 22. Zhao, Y., Patwari, N., Suresh, J.M.P.: ‘Radio tomographic imaging and tracking of stationary radio tomographic imaging and tracking of stationary and moving people via kernel distance’. Proc. ACM Int. Conf. IPSN, Philadelphia, PA, USA, 2013, pp. 112.
    23. 23)
      • 23. Crossbow Technology: ‘IRIS datasheet’, 2013. Available at, accessed 15/3/2018.
    24. 24)
      • 24. Wilson, J., Patwari, N.: ‘Spin: a token ring protocol for RSS collection’. Available at, accessed 15/3/2018.
    25. 25)
      • 25. Wilson, J., Patwari, N.: ‘See-through walls: motion tracking using variance-based radio tomography networks’, IEEE Trans. Mob. Comput., 2011, 10, (5), pp. 612621.
    26. 26)
      • 26. Press, W.H., Teukolsky, S.A., Vetterling, W.T., et al: ‘Numerical recipes’ (Cambridge University Press, Cambridge, 2007).
    27. 27)
      • 27. Vapnik, V.: ‘Statistical learning theory’ (Wiley, New York, 1998).
    28. 28)
      • 28. Massa, A., Boni, A., Donelli, M.: ‘A classification approach based on SVM for electromagnetic subsurface sensing’, IEEE Trans. Geosci. Remote Sens., 2005, 43, (9), pp. 20842093.
    29. 29)
      • 29. Platt, J.: ‘Probabilistic outputs for support vector machines and comparison to regularized likelihood methods’, in Alexander, J.S., Peter, B., Bernhard, S., et al (Eds.): ‘Advances in large margin classifiers’ (MIT Press, Cambridge, MA, 1999).
    30. 30)
      • 30. Wilson, J., Patwari, N., Vasquez, F.G.: ‘Regularization methods for radio tomographic imaging’. Proc. Virginia Tech Wireless Symp., VA, USA, 2009, pp. 19.
    31. 31)
      • 31. Engl, H.W., Hanke, M., Neubauer, A.: ‘Regularization of inverse problems’ (Springer, The Netherlands, 2004).
    32. 32)
      • 32. Barsocchi, P., Chessa, S., Furfari, F., et al: ‘Evaluating AAL solutions through competitive benchmarking: the localization competition’, IEEE Pervasive Comput. Mag., 2013, 12, (4), pp. 7279.

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