User Movements Forecasting by Reservoir Computing Using Signal Streams Produced by Mote-Class Sensors

  • Claudio Gallicchio
  • Alessio Micheli
  • Paolo Barsocchi
  • Stefano Chessa
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 81)


Real-time, indoor user localization, although limited to the current user position, is of great practical importance in many Ambient Assisted Living (AAL) applications. Moreover, an accurate prediction of the user next position (even with a short advice) may open a number of new AAL applications that could timely provide the right services in the right place even before the user request them. However, the problem of forecasting the user position is complicated due to the intrinsic difficulty of localization in indoor environments, and to the fact that different paths of the user may intersect at a given point, but they may end in different places. We tackle with this problem by modeling the localization information stream obtained from a Wireless Sensor Network (WSN) using Recurrent Neural Networks implemented as efficient Echo State Networks (ESNs), within the Reservoir Computing paradigm. In particular, we have set up an experimental test-bed in which the WSN produces localization information of a user that moves along a number of different paths, and in which the ESN collects localization information to predict a future position of the user at some given mark points. Our results show that, with an appropriate configuration of the ESN, the system reaches a good accuracy of the prediction also with a small WSN, and that the accuracy scales well with the WSN size. Furthermore, the accuracy is also reasonably robust to variations in the deployment of the sensors. For these reasons our solution can be configured to meet the desired trade-off between cost and accuracy.


Movement Forecasting Sensor Stream Analysis Received Signal Strength Echo State Networks Wireless Sensor Networks Ambient Assisted Living 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Crossbow technology inc.,
  2. 2.
  3. 3.
    Antonelo, E.A., Schrauwen, B., Stroobandt, D.: Modeling multiple autonomous robot behaviors and behavior switching with a single reservoir computing network. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 1843–1848 (October 2008)Google Scholar
  4. 4.
    Antonelo, E.A., Schrauwen, B., Campenhout, J.M.V.: Generative modeling of autonomous robots and their environments using reservoir computing. Neural Processing Letters 26(3), 233–249 (2007)CrossRefGoogle Scholar
  5. 5.
    Antonelo, E.A., Schrauwen, B., Stroobandt, D.: Event detection and localization for small mobile robots using reservoir computing. Neural Networks 21(6), 862–871 (2008)CrossRefGoogle Scholar
  6. 6.
    Bahl, P., Padmanabhan, V.: Radar: an in-building rf-based user location and tracking system. In: Proceedings of the Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2000, vol. 2, pp. 775–784. IEEE (2000)Google Scholar
  7. 7.
    Baronti, P., Pillai, P., Chook, V.W.C., Chessa, S., Gotta, A., Hu, Y.F.: Wireless sensor networks: A survey on the state of the art and the 802.15.4 and zigbee standards. Comput. Commun. 30(7), 1655–1695 (2007)CrossRefGoogle Scholar
  8. 8.
    Barsocchi, P., Lenzi, S., Chessa, S., Giunta, G.: A novel approach to indoor rssi localization by automatic calibration of the wireless propagation model. In: IEEE 69th Vehicular Technology Conference, VTC Spring 2009, pp. 1–5 (April 2009)Google Scholar
  9. 9.
    Barsocchi, P., Lenzi, S., Chessa, S., Giunta, G.: Virtual calibration for rssi-based indoor localization with ieee 802.15.4. In: IEEE International Conference on Communications, ICC 2009, pp. 1–5 (June 2009)Google Scholar
  10. 10.
    Buehner, M., Young, P.: A tighter bound for the echo state property. IEEE Transactions on Neural Networks 17(3), 820–824 (2006)CrossRefGoogle Scholar
  11. 11.
    Cui, S., Goldsmith, A., Bahai, A.: Energy-efficiency of mimo and cooperative mimo techniques in sensor networks. IEEE Journal on Selected Areas in Communications 22(6), 1089–1098 (2004)CrossRefGoogle Scholar
  12. 12.
    Ducatel, K., Bogdanowicz, M., Scapolo, F., Leijten, J., Burgelman, J.C.: Scenarios for Ambient Intelligence in 2010. Tech. rep., IST Advisory Group (February 2001)Google Scholar
  13. 13.
    Gallicchio, C., Micheli, A.: A markovian characterization of redundancy in echo state networks by PCA. In: Proceedings of the ESANN 2010, pp. 321–326. d-side (2010)Google Scholar
  14. 14.
    Gallicchio, C., Micheli, A.: Architectural and markovian factors of echo state networks. Neural Networks 24(5), 440–456 (2011)CrossRefGoogle Scholar
  15. 15.
    Gärtner, T.: A survey of kernels for structured data. SIGKDD Explorations Newsletter 5, 49–58 (2003)CrossRefGoogle Scholar
  16. 16.
    Hartland, C., Bredeche, N.: Using echo state networks for robot navigation behavior acquisition. In: IEEE International Conference on Robotics and Biomimetics (ROBIO 2007), pp. 201–206. IEEE Computer Society Press (2007)Google Scholar
  17. 17.
    Jaeger, H.: The ”echo state” approach to analysing and training recurrent neural networks. Tech. rep., GMD - German National Research Institute for Computer Science (2001)Google Scholar
  18. 18.
    Jaeger, H., Haas, H.: Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78–80 (2004)CrossRefGoogle Scholar
  19. 19.
    Jaeger, H., Lukosevicius, M., Popovici, D., Siewert, U.: Optimization and applications of echo state networks with leaky- integrator neurons. Neural Networks 20(3), 335–352 (2007)CrossRefzbMATHGoogle Scholar
  20. 20.
    Kaemarungsi, K., Krishnamurthy, P.: Modeling of indoor positioning systems based on location fingerprinting. In: Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2004, vol. 2, pp. 1012–1022 (March 2004)Google Scholar
  21. 21.
    Kjærgaard, M.B.: A Taxonomy for Radio Location Fingerprinting. In: Hightower, J., Schiele, B., Strang, T. (eds.) LoCA 2007. LNCS, vol. 4718, pp. 139–156. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  22. 22.
    Kolen, J., Kremer, S. (eds.): A Field Guide to Dynamical Recurrent Networks. IEEE Press (2001)Google Scholar
  23. 23.
    Kushki, A., Plataniotis, K.N., Venetsanopoulos, A.N.: Kernel-based positioning in wireless local area networks. IEEE Transactions on Mobile Computing 6(6), 689–705 (2007)CrossRefGoogle Scholar
  24. 24.
    Legenstein, R.A., Maass, W.: Edge of chaos and prediction of computational performance for neural circuit models. Neural Networks 20(3), 323–334 (2007)CrossRefzbMATHGoogle Scholar
  25. 25.
    Liu, W., Li, X., Chen, M.: Energy efficiency of mimo transmissions in wireless sensor networks with diversity and multiplexing gains. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), vol. 4, pp. iv/897–iv/900 (March 2005)Google Scholar
  26. 26.
    Lukosevicius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Computer Science Review 3(3), 127–149 (2009)CrossRefzbMATHGoogle Scholar
  27. 27.
    Nakagmi, M.: The m-distribution. a general formula of intensity distribution of rapid fading. Statistical methods in radio wave propagation. Pergamon, Oxford (1960)Google Scholar
  28. 28.
    Madigan, D., Einahrawy, E., Martin, R., Ju, W.H., Krishnan, P., Krishnakumar, A.: Bayesian indoor positioning systems. In: INFOCOM 2005, vol. 2, pp. 1217–1227 (March 2005)Google Scholar
  29. 29.
    Martínez, E.A., Cruz, R., Favela, J.: Estimating User Location in a WLAN Using Backpropagation Neural Networks. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 737–746. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  30. 30.
    Oubbati, M., Kord, B., Palm, G.: Learning Robot-Environment Interaction Using Echo State Networks. In: Doncieux, S., Girard, B., Guillot, A., Hallam, J., Meyer, J.-A., Mouret, J.-B. (eds.) SAB 2010. LNCS, vol. 6226, pp. 501–510. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  31. 31.
    Pan, J.J., Kwok, J., Yang, Q., Chen, Y.: Multidimensional vector regression for accurate and low-cost location estimation in pervasive computing. IEEE Transactions on Knowledge and Data Engineering 18(9), 1181–1193 (2006)CrossRefGoogle Scholar
  32. 32.
    Rabiner, L.R.: A tutorial on hidden markov models ans selected applications in speech recognition. Proceedings of the IEEE 77(2) (1989)Google Scholar
  33. 33.
    Rubio, L., Reig, J., Cardona, N.: Evaluation of nakagami fading behaviour based on measurements in urban scenarios. AEU - International Journal of Electronics and Communications 61(2), 135–138 (2007)CrossRefGoogle Scholar
  34. 34.
    Rui, C., Yi-bin, H., Zhang-qin, H., Jian, H.: Modeling the ambient intelligence application system: Concept, software, data, and network. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 39(3), 299–314 (2009)CrossRefGoogle Scholar
  35. 35.
    Sun, G., Chen, J., Guo, W., Liu, K.: Signal processing techniques in network-aided positioning: a survey of state-of-the-art positioning designs. IEEE Signal Processing Magazine 22(4), 12–23 (2005)CrossRefGoogle Scholar
  36. 36.
    Tiño, P., Hammer, B., Bodén, M.: Markovian Bias of Neural-based Architectures with Feedback Connections. In: Hammer, B., Hitzler, P. (eds.) Perspectives of Neural-Symbolic Integration. SCI, vol. 77, pp. 95–133. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  37. 37.
    Verstraeten, D., Schrauwen, B., D’Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Networks 20(3), 391–403 (2007)CrossRefzbMATHGoogle Scholar
  38. 38.
    Waegeman, T., Antonelo, E.A., Wyffels, F., Schrauwen, B.: Modular reservoir computing networks for imitation learning of multiple robot behaviors. In: Proceedings of the 8th IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 27–32. IEEE (2009)Google Scholar
  39. 39.
    Youssef, M., Agrawala, A.: The horus wlan location determination system. In: MobiSys 2005: Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services, pp. 205–218. ACM, New York (2005)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Claudio Gallicchio
    • 1
  • Alessio Micheli
    • 1
  • Paolo Barsocchi
    • 2
  • Stefano Chessa
    • 1
    • 2
  1. 1.Computer Science DepartmentUniversity of PisaPisaItaly
  2. 2.ISTI-CNRPisaItaly

Personalised recommendations