Distributed Signal Processing Algorithms for Wireless Sensor Networks
DOI:
https://doi.org/10.24113/ijoscience.v7i8.404Keywords:
Wireless Sensor Network, WBAN, Distributed Signal Processing, Compressive sensing.Abstract
Wireless Body Area Networks (WBAN), in particular in the field of wearable health monitoring system (WMB), such as electromagnetic cardiograms (ECG) data collecting system via WBANs in e-health applications, is becoming increasingly important for future communication systems. Compressive sensing (CS), on the other hand, has been shown to consume less power compared classic transform-coding-based approaches. We propose a new low-rank sparse deep signal recovery algorithm for recovering ECG data in the context of CS (Compressive sensing) because the spatial and temporal data collected by a WBAN have some closely correlated structures in certain wavelet domains e.g., the discrete wavelet transform (DWT) domain
Downloads
References
Zhang, YB., Huang, LT., Li, YQ. et al. “Low-rank and joint-sparse signal recovery using sparse Bayesian learning in a WBAN”, Multidim Syst Sign Process 32, 359–379 (2021). https://doi.org/10.1007/s11045-020-00743-y.
Mamaghanian H, Khaled N, Atienza D, et al. Compressive sensing for real-time energy-efficient ECG compression on wireless body sensor nodes. IEEE T Bio-Med Eng 2011; 58(9): 2456–2466.
Brunelli D and Caione C. Sparse recovery optimization in wireless sensor networks with a sub-Nyquist sampling rate. Sensors 2015; 15(7): 16654–16673.
Majumdar A and Ward RK. Energy efficient EEG sensing and transmission for wireless body area networks: a blind Compressive sensing approach. Biomed Signal Proces 2015; 20: 1–9.
Mangia M, Bortolotti D, Pareschi F, et al. Zeroing for HW-efficient compressed sensing architectures targeting data compression in wireless sensor networks. Microprocess Microsy 2017; 48: 69–79.
Peng H, Tian Y, Kurths J, et al. Secure and energyefficient data transmission system based on chaotic compressive sensing in body-to-body networks. IEEE T Biomed Circ S 2017; 11(3): 558–573.
S. Sawant, S. Banerjee and S. Tallur, "Compressive sensing based data-loss recovery enables robust estimation of damage index in ultrasonic structural health monitoring," 2020 IEEE SENSORS, 2020, pp. 1-4, doi: 10.1109/SENSORS47125.2020.9278599.
G. J. A and T. A. C., "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Trans. Info. Theory, vol. 53(12), pp. 4655–4666, 2007
Tirthankar Sengupta, Shivi Jain, Mani Bhushan, “A Compressive sensing Based Basis-pursuit Formulation of the Room Algorithm, IFAC Proceedings, Volume 46, Issue 31, 2013, Pages 238-243.
D. Needell and J. Tropp, "CoSaMP: Iterative signal recovery from incomplete and inaccurate samples," Applied and Computational Harmonic Analysis, pp. 301-321, 2009.
R. Chartrand and Wotao Yin, "Iteratively reweighted algorithms for compressive sensing," 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, pp. 3869-3872, doi: 10.1109/ICASSP.2008.4518498.
Li, Yingsong and M. Hamamura. “An Improved Proportionate Normalized Least-Mean-Square Algorithm for Broadband Multipath Channel Estimation.” The Scientific World Journal 2014 (2014).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Ashwini S Chiwarkar, Dr. K. B. Khanchandani
This work is licensed under a Creative Commons Attribution 4.0 International License.
IJOSCIENCE follows an Open Journal Access policy. Authors retain the copyright of the original work and grant the rights of publication to the publisher with the work simultaneously licensed under a Creative Commons CC BY License that allows others to distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you for the original creation. Authors are permitted to post their work in institutional repositories, social media or other platforms.
Under the following terms:
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
