5G-Based Smart Healthcare Network: Architecture, Taxonomy, Challenges and Future Research Directions

Ahad, Abdul; Tahir, Mohammad; Yau, Kok‐Lim Alvin

doi:10.1109/access.2019.2930628

Cited by 241 publications

(123 citation statements)

References 66 publications

(58 reference statements)

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“…Communication is an important consideration in IoMT. For example ( Zhang et al., 2018 ), designed an IoMT system that uses Narrow Band IoT (NB-IoT) protocol ( Ma et al., 2017 ), analyzed IoMT system with Long-Term Evolution (LTE) communication, and ( Ahad et al., 2019 ) incorporated 5G based communication to support long range wireless communication, while the work of ( Tseng et al., 2019 ) focused on short range wireless communication protocol, i.e., Wi-Fi and Bluetooth as part of their work on IoMT.…”

Section: The Iomt Ecosystemmentioning

confidence: 99%

“…The next layer is the communication network layer. Some of the recent communication technologies used are Wireless Sensor Network (WSN), Bluetooth, ZigBee, WiFi, NB-IoT, LTE, 4G, and 5G ( Ahad et al., 2019 ; Sengupta et al., 2019 ; Buurman et al., 2020 ; Gu et al., 2020 ). These are lightweight protocols that are suitable for low power devices in wireless networks such as Body Area Network (BAN) and Personal Area Network (PAN).…”

Section: Iomt Pandemic Mitigation Architecturementioning

confidence: 99%

See 1 more Smart Citation

IoMT amid COVID-19 pandemic: Application, architecture, technology, and security

Aman

Hassan

Sameen

et al. 2021

Journal of Network and Computer Applications

121

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In many countries, the Internet of Medical Things (IoMT) has been deployed in tandem with other strategies to curb the spread of COVID-19, improve the safety of front-line personnel, increase efficacy by lessening the severity of the disease on human lives, and decrease mortality rates. Significant inroads have been achieved in terms of applications and technology, as well as security which have also been magnified through the rapid and widespread adoption of IoMT across the globe. A number of on-going researches show the adoption of secure IoMT applications is possible by incorporating security measures with the technology. Furthermore, the development of new IoMT technologies merge with Artificial Intelligence, Big Data and Blockchain offers more viable solutions. Hence, this paper highlights the IoMT architecture, applications, technologies, and security developments that have been made with respect to IoMT in combating COVID-19. Additionally, this paper provides useful insights into specific IoMT architecture models, emerging IoMT applications, IoMT security measurements, and technology direction that apply to many IoMT systems within the medical environment to combat COVID-19.

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Section: The Iomt Ecosystemmentioning

confidence: 99%

Section: Iomt Pandemic Mitigation Architecturementioning

confidence: 99%

IoMT amid COVID-19 pandemic: Application, architecture, technology, and security

Aman

Hassan

Sameen

et al. 2021

Journal of Network and Computer Applications

121

View full text Add to dashboard Cite

show abstract

“…Furthermore, the network traffic diversity has grown exponentially due to 5G, the advent of 6G, and IoMT smart scenarios (e.g., smart hospital) that daily generate a massive amount of data, resulting in high complexity to support s-health application requirements. The variety of s-health applications (e.g., telemedicine, critical care monitoring) makes the task even more complicated since each application has specific requirements [ 8 ]. For instance, remote surgery requires reliability of 99.999% and latency of 1 ms, while continuous monitoring of noncritical patients support 125 ms of latency [ 5 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…The high level of reliability demanded by s-health applications reveals limitations in traditional network mechanisms (e.g., priority queues) given traffic diversity and volume [ 8 , 10 , 11 ]. Current network technologies are not prepared to deal with such amount of data and devices, since they do not support the dynamics and on-demand network resource allocation.…”

Section: Introductionmentioning

confidence: 99%

An Architecture for the Performance Management of Smart Healthcare Applications

Vergütz

Prates

Schwengber

et al. 2020

Sensors

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The sixth-generation (6G) network intends to revolutionize the healthcare sector. It will offer smart healthcare (s-health) treatments and allow efficient patient remote monitoring, exposing the high potential of 6G communication technology in telesurgery, epidemic, and pandemic. Healthcare relies on 6G communication technology, diminishing barriers as time and space. S-health applications require strict network requirements, for instance, 99.999% of service reliability and 1 ms of end-to-end latency. However, it is a challenging task to manage network resources and applications towards such performance requirements. Hence, significant attention focuses on performance management as a way of searching for efficient approaches to adjust and tune network resources to application needs, assisting in achieving the required performance levels. In the literature, performance management employs techniques such as resource allocation, resource reservation, traffic shaping, and traffic scheduling. However, they are dedicated to specific problems such as resource allocation for a particular device, ignoring the heterogeneity of network devices, and communication technology. Thus, this article presents PRIMUS, a performance management architecture that aims to meet the requirements of low-latency and high-reliability in an adaptive way for s-health applications. As network slicing is central to realizing the potential of 5G–6G networks, PRIMUS manages traffic through network slicing technologies. Unlike existing proposals, it supports device and service heterogeneity based on the autonomous knowledge of s-health applications. Emulation results in Mininet-WiFi show the feasibility of meeting the s-health application requirements in virtualized environments.

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“…Some researchers have studied human stools based on biotechnology [4]. However, an accurate automatic analysis system for stool color based on computer technologies is still absent [5]. Currently, many clinical diagnosis technologies are designed based on digital medical images, so it is significant to improve the quality of medical images [6] to accurately diagnose diseases based on medical images [7].…”

Section: Introductionmentioning

confidence: 99%

StoolNet for Color Classification of Stool Medical Images

2019

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The color classification of stool medical images is commonly used to diagnose digestive system diseases, so it is important in clinical examination. In order to reduce laboratorians’ heavy burden, advanced digital image processing technologies and deep learning methods are employed for the automatic color classification of stool images in this paper. The region of interest (ROI) is segmented automatically and then classified with a shallow convolutional neural network (CNN) dubbed StoolNet. Thanks to its shallow structure and accurate segmentation, StoolNet can converge quickly. The sufficient experiments confirm the good performance of StoolNet and the impact of the different training sample numbers on StoolNet. The proposed method has several advantages, such as low cost, accurate automatic segmentation, and color classification. Therefore, it can be widely used in artificial intelligence (AI) healthcare.

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5G-Based Smart Healthcare Network: Architecture, Taxonomy, Challenges and Future Research Directions

Cited by 241 publications

References 66 publications

IoMT amid COVID-19 pandemic: Application, architecture, technology, and security

IoMT amid COVID-19 pandemic: Application, architecture, technology, and security

An Architecture for the Performance Management of Smart Healthcare Applications

StoolNet for Color Classification of Stool Medical Images

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