Abstract
Energy efficiency is the primary challenge of wireless body sensor network (WBSN). Compressed sensing (CS) is a rapidly emerging signal processing technique that enables accurate capture and reconstruction of sparse signals from only a fraction of Nyquist Rate samples, significantly reducing the data-rate and system power consumption which solve the key issues in the WBSN. This paper proposes an improved CS-based Orthogonal Matching Pursuit (IOMP) algorithm in the WBAN. We evaluate the IOMP algorithm against the OMP algorithm from four aspects: compression ratio, percentage root-mean-square distortion,signal noise ratio and iterative times. Simulation results shows that, at the same compressed ratio, PRD SNR and iterative times of the proposed method are improved over those of the OMP algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Braem, B., Latré, B., Moerman, I., et al.: The wireless autonomous spanning tree protocol for multihop wireless body area networks. In: 2006 Third Annual International Conference on Mobile and Ubiquitous Systems: Networking & Services, pp. 1–8. IEEE (2006)
Corroy, S., Baldus, H.: Low power medium access control for body-coupled communication networks. In: 6th International Symposium on Wireless Communication Systems, ISWCS 2009, pp. 398–402. IEEE (2009)
Ryckaert, J., Desset, C., Fort, A., et al.: Ultra-Wide-Band Transmitter for Low-Power Wireless Body Area Networks: Design and Evaluation. IEEE Transactions on Circuits and Systems I: Regular Papers 52(12), 2515–2525 (2005)
Donoho, D.: Compressed Sensing. IEEE Transactions on Information Theory 52(4), 1289–1306 (2006)
Balouchestani, M., Raahemifar, K., Krishnan, S.: Increasing the reliability of wireless sensor network with a new testing approach based on compressed sensing theory. In: 2011 Eighth International. Conference on Wireless and Optical Communications Networks (WOCN), pp. 1–4. IEEE (2011)
Aeron, S., Saligrama, V., Zhao, M.Q.: Information Theoretic Bounds for Compressed Sensing. IEEE Transactions on Information Theory 56(10), 5111–5130 (2010)
Balouchestani, M., Raahemifar, K., Krishnan, S.: New Testing Method in Wireless Sensor Networks with Compressed Sensing Theory. In: 2011 International Conference on Computer Communication and Management (ICCCM 2011), vol. 5, pp. 1–6. IACSIT Press, Sydney (2011)
Tropp, J.: Greed is good: Algorithmic results for sparse approximation. IEEE Transactions on Information Theory 50(10), 2231–2242 (2004)
Needell, D., Vershynin, R.: Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit. Foundations of Computational Mathematics 9(3), 317–334 (2009)
Donoho, D.L., Tsaig, Y., Drori, I., Starck, J.L.: Sparse solution of underdetermined Linear equations by stagewise orthogonal matching pursuit (StOMP). IEEE Transactions on Information Theory 58(2), 1094–1121 (2012)
Baraniuk, R.: A lecture on compressive sensing. IEEE Signal Processing Magazine 24(4), 118–121 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jiang, R., Ding, Y., Hao, K., Shu, S. (2014). An Improved Orthogonal Matching Pursuit Algorithm for Signal Reconstruction in Wireless Body Sensor Network. In: Ma, S., Jia, L., Li, X., Wang, L., Zhou, H., Sun, X. (eds) Life System Modeling and Simulation. ICSEE LSMS 2014 2014. Communications in Computer and Information Science, vol 461. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45283-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-662-45283-7_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45282-0
Online ISBN: 978-3-662-45283-7
eBook Packages: Computer ScienceComputer Science (R0)