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time warnings during clinical visits based on travel Data processing is a critical component of achieving Industry
background and clinical indications, supporting in case 4.0's predicted goals. Without effective information
classification. They've also used this cutting-edge technique processing and management, providing valuable services
to detect infected persons, which includes QR code scanning will be difficult given the predicted volumes of received data.
and related reporting of transportation history [38]. The G06F19/00 category (i.e., digital computing or data
processing equipment or methods, specially adapted for
Fig. 1: The key metric used of IOT for fighting COVID-19 specific applications) is ranked fifth among the top 20
technical IPC categories, according to the leading
The Internet of Things (IoT) makes use of a huge International Patent Classification (IPC) analysis, with 130
number of networked equipment to establish an intelligent patents filed between January 2006 and December 2015 [40].
network for efficient supervision of health. It detects and Moreover, for battery-operated IOMT devices, minimizing
tracks any form of disease to ensure the patient's security. It the amount of communicated data is critical in order to
digitally captures the patient's data and all the information minimize transmitting power consumption in this case,
without the need for human engagement. This information is several promising approaches are:
also useful in achieving informed decisions [39]. IoT is being - processing and compressing acquired data locally in the
used for a variety of purposes in order to meet the critical network prior to propagation, with hardware implementation
need of reducing the effects of the COVID-19 pandemic. included (CS).
With the help of suitably gathered data, it has the potential to
forecast the upcoming circumstance. Its applications are used - Deep learning is being used as a powerful machine
to handle the epidemic effectively. For tailored attention, the
patient can use IoT services to monitor their heart indicator, learning and health informatics technique to generate the best
pressure of blood, gluco-meter, with rest activities. It aids in high-level features and semantic interpretation from data
the monitoring of elderly people's health. The most important [41]. The original signal is multiplied with a linear form of
performing of this technique in health-care are tracking the reinforcement in CS to achieve sampling, resulting in a low-
fact-time position of medical tools and instruments to ensure dimensional subspace projection of the high-dimensional
a smooth and timely treatment process. This technology can data vector. Although CS has showed significant promise in
be used by healthcare insurance companies to identify terms of high compression ratios, building CS-based
deception claims and promote opacity across the whole technology is difficult [42]. Signal reconstruction, in
system. This improves the patient's treatment workflow by particular, has a significant CS has a high computational
allowing for more efficient performance, as well as assisting cost, which limits its utility in scenarios that must be
in the decision-making process in complex circumstances. In completed in real time [43]. The Correlation - based feature
the current pandemic situation, COVID-19 is concerned with Chasing (OMP) signal reconstruction approach, for example,
the security and privacy of the data received, which is unique necessitates a lot of matrix processing. Furthermore, there is
and critical in terms of patient health, and is a major source a tradeoff between the energy economy of the hardware and
of worry when using the Internet of Things. The second the precision of signal recovery. If a data-driven optimization
factor to consider is the caution that must be exercised when strategy is employed to improve signal recovery precision,
integrating the data network among the various devices and for example, additional computing capacity will be
protocols. consumed. On the other hand, excellent recovery accuracy
cannot be assured if non-data-driven random Boolean
IV. EEG-BASED TRANSCEIVER DESIGN embedding is used to improve energy efficiency of hardware
Rapid advanced data amounts, late cloud storage, [44]. Compression algorithms designed exclusively for e-
approaching edge computing, and omnipresent networking health depended in applications that have been proposed in
capabilities have made it possible to acquire, store, and the literature The computational complexity, loss and
analyze huge volumes of operational data that were lossless properties, and waveform transformations
previously unattainable in the IOMT and Industry 4.0 eras. performed in these techniques are all different (Fourier or
wavelet transforms, vector quantization, or the discrete
cosine transform are only a few examples). In summary,
most present compression research focuses on upper layers,
while lower layer factors (such as wireless channel
characteristics, Bit/symbol error rate (BSER) and signal-to-
interference-plus-noise ratio (SINR) are ignored.
Furthermore, the increased processing complexity may make
implementing such systems on battery-powered devices
prohibitively expensive. Designing application-specific
transceivers, on the other hand, has recently become popular.
Long-range IoT connectivity and multi-standard RF
transceivers are being addressed in future transceiver
architecture, while preserving a high level of adaptability,
versatility, and renewability [45]. SRT Marine Systems, for
example, was granted US patent 9473197 and European
patent EP2951930 for their reversible time domain duplex
(RTDD) transceiver technology. This method allows a single
