Compression and encryption of self-test programs for wireless sensor network nodes

Mihajlović, Bojan; Žilić, Željko; Radecka, Katarzyna

doi:10.1109/mwscas.2007.4488798

Cited by 3 publications

(1 citation statement)

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“…In order to recover K-sparsity of the original signal, now we have MK  system of linear equations, with M equations and K unknowns, and since MK  , it is possible to find the K-sparsity of the original signal [73]. As an example, Figure 3.4 shows how compressed signal can be generated from the original signal D [74]. The received signal can be written as:…”

Section: Basic Theoremmentioning

confidence: 99%

Wireless Body Area Networks Based on Compressed Sensing Theory

Asli¹

2021

Preprint

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In this research, the effective sampling method known as Compressed Sensing (CS) theory is applied to Wireless Body Area Networks (WBANs) to provide low power and low sampling-rate wireless healthcare systems and intelligent emergency care management systems. The fundamental contribution of this work can be divided into three areas. 1) We propose two new algorithms in the sensing, measurement, and processing area to compress biomedical data. 2) In the communication area, one new channel model based on CS theory is defined to transmit compressed data to the receiver side. 3) In the receiver side or reconstruction area, two new algorithms for recovering the original biomedical data are presented to recover the original data. Our results will be divided into three areas. 1) We employ the proposed algorithms to WBANs with a single biomedical signal (i.e. Electroencephalography [ECG] signals as a sample signal). In this area, the simulation results illustrate an increment of 10% improved for sensitivity in receiving compressed ECG signals. The simulation results also illustrate a 25% reduction of Percentage Root-mean-square Difference (PRD) for ECG signals on the receiver side. In addition, they confirm the ability of CS to maximize the prediction level for received the ECG signal at either Gate Ways (GWs) or Access Points (APs). 2) We illustrate that the proposed algorithms can be employed in WBANs with multiple biomedical signals to enhance current health care systems into low-power wireless healthcare systems. In this area, the simulation results confirm that for a particular WBAN, including N biomedical signals, the sampling-rate can be reduced by 25-35% and power consumption by 35-40%, without sacrificing the network’s performance. 3) Here improvements for wireless channel feature between BWSs and either GWs or APs are shown. In this area, the results demonstrate that CS is able to maximize signal amplitude to 25-30% at the receiver as well as distance between transmitter and receiver BWS to 30%. Moreover, these results confirm that path loss can be reduced to 25%.

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Section: Basic Theoremmentioning

confidence: 99%