Compressive sensing for localisation in wireless sensor networks: an approach for energy and error control

Compressive sensing for localisation in wireless sensor networks: an approach for energy and error control

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Energy efficiency is an important requirement in wireless sensor networks in order to achieve cost-effectiveness and practical implementation. The present work deals with the problem of minimising node power consumption in the context of moving-node localisation and tracking. Time-of-arrival measurements are sent from anchor nodes to a powerful, usually sophisticated, central node, called the fusion centre, where all computations are performed. Low data rates are desirable to economise on node energy but result in sub-optimal localisation accuracy. It makes sense, therefore, to sample measurements at a low data rate while interpolating the data stream at the fusion centre to improve localisation. The localisation error is remarkably reduced and energy efficiency increased by using this conventional sample rate conversion technique. A further improvement in terms of localisation error is achieved using compressive sensing (via random sampling and interpolation), whereby the localisation error function is shown to decrease with higher-average random sampling periods.


    1. 1)
      • 1. Candes, E.J., Wakin, M.: ‘An introduction to compressive sampling’, IEEE Signal Process. Mag., 2008, 25, (2), pp. 2130.
    2. 2)
      • 2. Azghani, M., Marvasti, F.: ‘Iterative methods for random sampling and compressed sensing recovery’. Proc. Tenth Int. Conf. Sampling Theory and Applications, 2013.
    3. 3)
      • 3. Patwari, N., Hero, A.O.III, Perkins, M., et al: ‘Relative localization estimation in wireless sensor networks’, IEEE Trans. Signal Process., 2003, 51, (8), pp. 21372148.
    4. 4)
      • 4. Patwari, N., Ash, J.N., Kyperoutas, S., et al: ‘Locating the nodes: cooperative localization in wireless sensor networks’, IEEE Signal Process. Mag., 2005, 22, (4), pp. 5469.
    5. 5)
      • 5. Bachrach, J., Taylor, C.: ‘Localization in sensor networks: handbook of sensor networks: algorithms and architectures’, vol. 2005 (John Wiley & Sons, Inc., Hoboken, New Jersey, USA, 2005).
    6. 6)
      • 6. Qiao, D., Pang, G.K.H.: ‘Localization in wireless sensor networks with gradient descent’. IEEE Pacific Rim Conf. Communications, Computers and Signal Processing, 2011.
    7. 7)
      • 7. Alwan, N.A.S., Mahmood, A.S.: ‘Distributed gradient descent localization in wireless sensor networks’, Arab. J. Sci. Eng., 2015, 40, (3), pp. 893899.
    8. 8)
      • 8. Rhee, S., Seetharam, D., Liu, S.: ‘Techniques for minimizing power consumption in low data-rate wireless sensor networks’. Wireless Communications and Networking Conf. (WCNC 2004), 2004.
    9. 9)
      • 9. Ez-Zaidi, A., Rakrak, S.: ‘A comparative study of target tracking approaches in wireless sensor networks’, J. Sens., 2016, 2016.
    10. 10)
      • 10. Wen, Y., Gao, R., Zhao, H.: ‘Energy efficient moving target tracking in wireless sensor networks’, Sensors, 2016, 16, (1).
    11. 11)
      • 11. Chen, W., Wassell, I.J.: ‘Energy-efficient signal acquisition in wireless sensor networks: a compressive sensing framework’, IET Wirel. Sens. Syst., 2012, 2, (1), pp. 18.
    12. 12)
      • 12. Wei, Y., Li, W., Chen, T.: ‘Node localization algorithm for wireless sensor networks using compressive sensing theory’, Pers. Ubiquit. Comput., 2016, 20, (5), pp. 809819.
    13. 13)
      • 13. Gui, L., Yang, M., Fang, P., et al: ‘RSS-based indoor localisation using MDCF’, IET Wirel. Sens. Syst., 2017, 7, (4), pp. 98104.
    14. 14)
      • 14. Zhang, B., Cheng, X., Zhang, N., et al: ‘Sparse target counting and localization in sensor networks based on compressive sensing’. Proc. IEEE INFOCOM, 2011.
    15. 15)
      • 15. Jiang, R., Zhu, Y., Zhu, H., et al: ‘Compressive detection and localization of multiple heterogeneous events in sensor networks’, Ad Hoc Netw., 2017, 65, pp. 6577.
    16. 16)
      • 16. Nikitaki, S., Tsakalides, P.: ‘Localization in wireless sensor networks based on jointly compressed sensing’. Proc. 19th European Signal Processing Conf. (EUSIPCO), 2011.
    17. 17)
      • 17. Barzilai, J., Borwein, J.M.: ‘Two point step size gradient methods’, IMA J. Numer. Anal., 1988, 8, (1), pp. 141148.
    18. 18)
      • 18. Alwan, N.A.S.: ‘Adaptive step-sizes for gradient descent localization in wireless sensor networks’, Int. J. Inf. Commun. Technol. Res., 2016, 6, (1), pp. 17.
    19. 19)
      • 19. Bilinskis, I., Selavo, L., Sudars, K.: ‘Method for sensor data alias-free acquisition from wideband signal sources and their asymmetric compression-reconstruction’, Balt. J. Mod. Comput., 2013, 1, (3), pp. 199209.
    20. 20)
      • 20. Tarczynski, A., Allay, N.: ‘Spectral analysis of randomly sampled signals: suppression of aliasing and sample jitter’, IEEE Trans. Signal Process., 2004, 12, (52), pp. 33243334.
    21. 21)
      • 21. Abolghasemi, V., Ferdowsi, S., Sanei, S.: ‘A block-wise random sampling approach: compressed sensing problem’, J. AI Data Min., 2015, 3, (1), pp. 93100.
    22. 22)
      • 22. Beutler, F.J.: ‘Recovery of randomly sampled signals by simple interpolators’, Inf. Control, 1974, 26, (4), pp. 312340.
    23. 23)
      • 23. Ragheb, T., Kirolos, S., Laska, J., et al: ‘Implementation models for analog-to-information conversion via random sampling’. The 2007 50th Midwest Symp. Circuits and Systems, 2007.
    24. 24)
      • 24. Boyle, F.A., Haupt, J., Fudge, G.L., et al: ‘Detecting signal structure from randomly-sampled data’. The 2007 IEEE/SP 14th Workshop on Statistical Signal Processing, 2007.
    25. 25)
      • 25. Nguyen, L.T., Phong, D.V., Hussain, Z.M., et al: ‘Compressed sensing using chaos filters’. Australasian Telecommunication Networks and Applications Conf. (ATNAC 2008), 2008.

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