Abstract
A new transmission model, compressed sensing point-to-point transmission, is presented in this paper for the low power consumption of Body Area Networks. As a kind of novel information source coding and decoding technologies, compressed sensing reduces the redundancies in signal, compressing long signal to short one, then recovers original signal through corresponding recovery algorithm. It is shown by theory analysis and simulation results that, compressed sensing does not only reduce the power consumption in Body Area Networks, but also recovers original signal accurately. When sparsity is 16, more than 70% power is saved. In the end, distributed compressed sensing is introduced as future research work.
The work is supported by the Chinese National Natural Science Fund (No.6174165).
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References
Zimmerman, T.G.: Personal Area Networks: Near-field intrabody communication. IBM Syst. J. 35(3.4), 609–617 (1996)
Patel, M., Wang, J.: Applications, challenges, and prospective in emerging body area networking technologies. IEEE Wireless Communications 17(1), 80–88 (2010)
Falck, Thomas: Plug’n Play Simplicity for Wireless Medical Body Sensors In: Pervasive Health Conference and Workshops, November 29-December 1, vol. 1, pp. 1–5 (2006)
Braem, B., Latre, B., Moerman, I., et al.: The wireless autonomous spanning tree protocol for multihop wireless body area networks. In: 3rd Annual International Conference on Mobile and Ubiquitous Systems, July 17-21, pp. 1–8 (2006)
Ullah, S., et al.: On The Development of Low-power MAC Protocol for WBANs. In: Proceedings of the International Multi Conference of Engineers and Computer Scientists, Hong Kong, March 18–20, vol. 1 (2009)
Donoho, D.: Compressed sensing. IEEE Transactions on Information Theory 52(4), 1289–1306 (2006)
Candes, E., Romberg, J., Tao, J.: Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory 52(2), 489–509 (2006)
Baraniuk, R.: Compressive sensing. IEEE Signal Processing Magazine 24(4), 118–121 (2007)
Candes, E., Romberg, J.: Sparsity and incoherence in compressive sampling. Inverse Problems 23(3), 969–985 (2007)
Yu, L., Barbot, J.P., Zheng, G., Sun, H.: Compressive Sensing With Chaotic Sequence. IEEE Signal Processing Letters 17(8), 731–734 (2010)
Haupt, J., Nowak, R.: Signal reconstruction from noisy random projections. IEEE Transactions Information Theory (2006)
Baron, D., Wakin, M.B., Duarte, M., et al.: Distributed compressed sensing
Wang, W., Garofalakis, M., Ramchandran, K.: Distributed sparse random projections for Refinable approximation. In: Proceedings of the Sixth International Symposium on Information Pro2cessing in Sensor Networks (IPSN2007). Association for Computing Machinery, New York (2007)
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Li, S., Hu, F., Li, G. (2011). BAN with Low Power Consumption Based on Compressed Sensing Point-to-Point Transmission. In: Zhang, J. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23235-0_7
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DOI: https://doi.org/10.1007/978-3-642-23235-0_7
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