Cognitive Radio Enabled Telemedicine System

Shaikh, Aamir Zeb; Tamil, Lakshman S.

doi:10.1007/s11277-015-2423-1

Cited by 12 publications

(8 citation statements)

References 22 publications

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“…A MAC protocol that handles emergency data was created to order patient data in accordance with their medical conditions. By switching the data transmission process to any of the available networks (GSM, 3G-UMTS, WiMAX, and 4G-LTE), the framework was able to address network congestion and consistency difficulties [80]. [81] suggested a particle swarm optimization approach for essential medical wireless application networks employing cognitive radio-enabled WBAN for effective battery use and uninterrupted data transfer in BAN.…”

Section: Designing a Telemedicine Networkmentioning

confidence: 99%

Maximizing Network Capacity, Control and Management in Designing a Telemedicine Network: A Review and Recent Challenges

Sadiq,

Zakariyya,

Buhari

et al. 2024

AfricArXiv

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Telemedicine networks have seen significant changes in their capacity, monitoring, management, and control framework during the previous decades. The evolution of network capacity, control, and management for Unmanned Aerial Vehicle (UAV) & Software-Defined Networks (SDN) as support to telemedicine, artificial intelligence in telemedicine networks, and capabilities in designing a telemedicine network with respect to its performance and customization is presented in this study, with a historical history and a future view. The first section of the article goes over the history of traffic and capacity expansion, as well as future projections. By introducing a medical and image data communication protocol for telemedicine, the second section examines the technological constraints of expanding capacity in the era of UAV & software defined networking. The third section discusses ways to maximize network capacity by considering quality of service (QoS) capacity issues. Finally, the article explores how to construct a telemedicine network that can provide performance, customization, and capabilities to keep up with increased traffic in the coming decades. Research gaps and future directions were presented in the last section.

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Section: Designing a Telemedicine Networkmentioning

confidence: 99%

Maximizing Network Capacity, Control and Management in Designing a Telemedicine Network: A Review and Recent Challenges

Sadiq,

Zakariyya,

Buhari

et al. 2024

AfricArXiv

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“…This proposed solution aims to use the measurements of CR parameters. Reference [17] proposed a solution composed of three levels. The first phase is the use of a BSN, the UWB is the technology used in this solution.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Internet of Things in the Health Sector: Toward Minimizing Energy Consumption

Moutaib

Ahajjam

Fattah

et al. 2022

Big Data Min. Anal.

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“…Cognitive Radio may operate on many different schemes. These include underlay, overlay and interweave radio schemes [16].…”

Section: B Cascade Feed Forward Backpropagation Neural Networkmentioning

confidence: 99%

Exploiting White Spaces for Karachi through Artificial Intelligence: Comparison of NARX and Cascade Feed Forward Back Propagation

Naqvi¹,

Ali²,

Shaikh³

et al. 2020

IJACSA

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Marriage of Internet of Everything (IoE) and Cognitive Radio driven technologies seems near under the umbrella of 6G and 6G+ communication standard. The expected new services that will be introduced in 6G communication will require high data rates for transmission. The learning based algorithms will play a key role towards successful implementation of these novel technologies and evolving next generation wireless standards for providing ubiquitous connectivity. This paper investigates performance of two artificial neural network (ANN) based algorithms for Karachi. These include Nonlinear autoregressive exogenous Algorithm (NARX) and cascade feed forward back propagation neural network (CFFBNN) scheme. A dataset for Karachi is also developed for 1805 MHZ. The results of the two algorithms are compared that show Mean Square Error (MSE) for CFFBNN is 6.8877e-5 at epoch 16 and MSE for NARX is 3.1506e-11 at epoch 26. Hence, exploiting computational performance, NARX performs much superior than the classis CFFBNN algorithm.

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Cognitive Radio Enabled Telemedicine System

Cited by 12 publications

References 22 publications

Maximizing Network Capacity, Control and Management in Designing a Telemedicine Network: A Review and Recent Challenges

Maximizing Network Capacity, Control and Management in Designing a Telemedicine Network: A Review and Recent Challenges

Application of Internet of Things in the Health Sector: Toward Minimizing Energy Consumption

Exploiting White Spaces for Karachi through Artificial Intelligence: Comparison of NARX and Cascade Feed Forward Back Propagation

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