A Review on Compressive Sensing for Distributed Signal Processing in WBAN
DOI:
https://doi.org/10.24113/ijoscience.v7i7.397Keywords:
WBAN, CS, OMP, BP, LMS, NLMS.Abstract
Wireless networks of the body (WBANs) that support healthcare applications are in the early stages of development but make valuable contributions to surveillance, diagnostics or therapy. They cover real-time medical information acquisition from various sensors with secure data communication and low power consumption. WBANs promises discreet outpatient medical monitoring over a long period of time and inform the physician in real-time about the patient's condition. They are widely used for ubiquitous healthcare, entertainment, and military applications. This article presents distributed wireless networks and describes the search for Orthogonal Matching Pursuit (OMP), Basis Pursuit (BP), Least Mean Square Technique (LMS), and Normalized Least Mean Square Technique (NLMS).
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.
G. Mateos, I. D. Schizas, and G. B. Giannakis, “Distributed Recursive Least- Squares for Consensus-Based In-Network Adaptive Estimation,” IEEE Trans. Signal Process., vol. 57, no. 11, pp. 4583–4588, November 2009.
C. G. Lopes and A. H. Sayed, “Incremental adaptive strategies over distributed networks,” IEEE Trans. Signal Process., vol. 48, no. 8, pp. 223–229, August 2007.
A. H. Sayed and C. G. Lopes, “Adaptive processing over distributed networks,” IEICE Trans. Fundam. Electron. Commun. Comput. Sci., vol. E90-A, no. 8, pp. 1504–1510, August 2007.
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.
Wang A, Lin F, Jin Z, et al. A configurable energy efficient compressive sensing architecture with its application on body sensor networks. IEEE T Ind Inform 2016; 12(1): 15–27.
Li S, Da Xu L and Wang X. A continuous biomedical signal acquisition system based on compressive sensing in body sensor networks. IEEE T Ind Inform 2013; 9(3): 1764–1771.
Dixon AM, Allstot EG, Gangopadhyay D, et al. Compressive sensing system considerations for ECG and EMG wireless biosensors. IEEE T Biomed Circ S 2012; 6(2): 156–166.
Yang, Y., Smith, D. B., & Seneviratne, S. (2019). Deep Learning Channel Prediction for Transmit Power Control in Wireless Body Area Networks. IEEE International Conference on Communications, 2019-May, 1–6. https://doi.org/10.1109/ICC.2019.8761432
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.