Energy-Efficient Protocol of Link Scheduling in Cognitive Radio Body Area Networks for Medical and Healthcare Applications

Le, Thien T. T.; Moh, Sangman

doi:10.3390/s20051355

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…An efficient and successful smart healthcare system can be designed by incorporating machine learning and cognitive radio with it [35,36]. Smart healthcare devices and systems must be spectral and energy efficient while they are assisted by cognitive radio [37,38]. Spectrum utilization and energy harvesting protocols can make WBANs more convenient and efficient in smart healthcare systems and applications [39].…”

Section: Related Work: Smart Healthcare Using Machine Learning and Co...mentioning

confidence: 99%

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

Raza,

Ali,

Ehsan

et al. 2023

Sensors

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The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients’ health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient’s health data by exploiting the primary user’s (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection (Pd) and probability of false alarm (Pf). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE).

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Section: Related Work: Smart Healthcare Using Machine Learning and Co...mentioning

confidence: 99%

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

Raza,

Ali,

Ehsan

et al. 2023

Sensors

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“…A few scheduling algorithms are proposed [15][16][17][18][19] for cognitive radio-based WBANs. The interference between WBANs and medical devices causes harm to patients in telemedicine services.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed method is simulated using MATLAB 2019 and compared with three other similar schemes, namely, energy-efficient spectrum-aware reinforcement learningbased clustering (EESA-RLC), energy-efficient link scheduling (ELS), and CogMed [16][17][18][19]21]. The performance metrics chosen for comparison are end-to-end delay, throughput, network lifetime, and stability and residual energy.…”

Section: Energy Efficiency Of Compressedmentioning

confidence: 99%

Energy-Efficient Compressed Sensing in Cognitive Radio Network for Telemedicine Services

Vivekanand¹,

Inbamalar²,

Nadar

et al. 2023

Wireless Communications and Mobile Computing

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Wireless body area networks (WBAN) are becoming a promising solution for health care applications. WBAN allows monitoring of patients continuously in their own comfort zone. These devices use the industrial, scientific, and medical band (ISM) for communication. This band is overcrowded due to the increasing number of wireless medical devices and other wireless devices occupying this band. This causes interference, which can be damaging and could result in a change in received power. However, WBAN also needs minimum and reliable energy communication for a longer lifetime and improved quality of service. This work addresses both problems and proposes solutions for the same. A cognitive radio controller is employed as a centralized controller with dynamic spectrum allocation properties to mitigate the interference. The sensing of the spectrum is based on compressed sensing with a nonreconstruction model to save energy. To quantify interference measurement, the interference mitigation factor is introduced. Further, to increase energy efficiency, the K-means algorithm is used to cluster WBANs. However, critical emergency data and normal data are categorized as priority data and normal data, respectively, by the proposed priority scheduling algorithm. The performance of this cognitive radio-based system for telemedicine applications is analyzed through simulations. The simulations are performed using MATLAB 2019.

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“…In general, the opportunities offered by IoT are unlimited and its full impact and potential will be realized in the near future as more and more devices connect to the Internet. At present, there are few works in the literature dealing with potential applications of CR technology for IoT, including military applications, cognitive radio-vehicular ad hoc networks (CR-VANET), emergency networks, smart grids, smart metering, and medical applications [117][118][119][120][121][122][123]. Table IV describes some IoT applications while demonstrating how CR can be used to address a number of their concerns.…”

Section: Iot Applications and Crmentioning

confidence: 99%

“…Applications [122,123] Smart sensors are deployed to monitor critical data like blood pressure, glucose levels, and temperature. With Cognitive Radio this information can be transmitted to medical staff in real time, over long distances without worrying about spectrum availability.…”

Section: Healthcare and Medicalmentioning

confidence: 99%

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

Kassri¹,

En‐Nouaary²,

Bah³

et al. 2021

IJACSA

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The inherent scarcity of frequency spectrum, along with the fixed spectrum allocation adopted policy, has led to a dire shortage of this indispensable resource. Furthermore, with the tremendous growth of wireless applications, this problem is intensified as the unlicensed frequency spectrum becomes overcrowded and unable to meet the requirement of emerging radio devices operating at higher data rates. Additionally, the already assigned spectrum is underutilized. That has prompted researchers to look for a way to address spectrum scarcity and enable efficient use of the available spectrum. In this context, Cognitive Radio (CR) technology has been proposed as a potential means to overcome this issue by introducing opportunistic usage to less congested portions of the licensed spectrum. In addition to outlining the fundamentals of Cognitive Radio, including Dynamic Spectrum Access (DSA) paradigms and CR functions, this paper has a three-fold objective: first, providing an overview of Software Defined Radio (SDR), in which the architecture, benefits, and ongoing challenges of SDR are presented; second, giving an extensive review of spectrum sensing, covering sensing types, narrowband and wideband sensing schemes with their pros and cons, Machine Learningbased sensing, and open issues that need to be further addressed in this field; third, exploring the use of Cognitive Radio in the Internet of Things (IoT) while highlighting the crucial contribution of CR in enabling IoT. This Review is elaborated in an informative fashion to help new researchers entering the area of Cognitive Radio Networks (CRN) to easily get involved.

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Energy-Efficient Protocol of Link Scheduling in Cognitive Radio Body Area Networks for Medical and Healthcare Applications

Cited by 5 publications

References 15 publications

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

Energy-Efficient Compressed Sensing in Cognitive Radio Network for Telemedicine Services

A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks

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