6G Access Network for Intelligent Internet of Healthcare Things: Opportunity, Challenges, and Research Directions

Kaiser, M. Shamim; Zenia, Nusrat Zerin; Tabassum, Fariha; Mamun, Shamim Al; Rahman, Muhammad Arifur; Islam, Md. Shahidul; Mahmud, Mufti

doi:10.1007/978-981-33-4673-4_25

Cited by 65 publications

(18 citation statements)

References 21 publications

(23 reference statements)

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“…In recent years artificial intelligence (AI) has been applied in diverse problem domains to solve various challenging problems, including text classification [13,14,15,16], cyber security [17,18,19,20], neurological disease detection [21,22,23,12] and management [24,25,26,27,28,29], elderly care [30,31], fighting pandemic [32,33,34,35,36,37,38], and healthcare service delivery [39,40,41]. In particular, deep learning (DL) has attracted a lot of attention [6,7].…”

Section: Related Workmentioning

confidence: 99%

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Hajamohideen

Shaffi

Mahmud

et al. 2022

Preprint

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Alzheimer's disease (AD) is a neurodegenerative disease that causes irreversible damage to several brain regions, including the hippocampus causing impairment in cognition, function, and behaviour. Early diagnosis of the disease will reduce the suffering of the patients and their family members. Towards this aim, in this paper, we propose a Siamese Convolutional Neural Network (CNN) architecture that employs the triplet-loss function for the representation of input MRI images as k-dimensional embeddings. We used both pre-trained and non-pre-trained CNNs to transform images into the embedding space. These embeddings are subsequently used for the 4-way classification of Alzheimer's Disease. The model efficacy was tested using the ADNI and OASIS datasets which produced an accuracy of 91.83% and 93.85%, respectively. Furthermore, obtained results are compared with similar methods proposed in the literature.

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Section: Related Workmentioning

confidence: 99%

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Hajamohideen

Shaffi

Mahmud

et al. 2022

Preprint

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“…The process of integration of the retrieved information is crucial, as their coordination and synchronization in order to provide advanced context-aware applications (such as behaviour monitors, fall detectors, automatic diary builders and so on). The storage and computing of these data can be done at the edge in the resource-constrained "medical things", which allows data from IoMT devices to be processed locally reducing latency and security-related issues [32,37]. Edge computing and storage are forms of cloud parts that enable physically and logically moving the service-specific processing and data storage from the central to the edge network that is close to both the end users and providers.…”

Section: Iomt Microservices and Edge Intelligencementioning

confidence: 99%

CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario

et al. 2021

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The forthcoming 6G will attempt to rewrite the communication networks’ perspective focusing on a shift in paradigm in the way technologies and services are conceived, integrated and used. In this viewpoint, the Internet of Medical Things (IoMT) represents a merger of medical devices and health applications that are connected through networks, introducing an important change in managing the disease, treatments and diagnosis, reducing costs and faults. In 6G, the edge intelligence moves the innovative abilities from the central cloud to the edge and jointly with the complex systems approach will enable the development of a new category of lightweight applications as microservices. It requires edge intelligence also for the service evaluation in order to introduce the same degree of adaptability. We propose a new evaluation model, called CoKnowEMe (context knowledge evaluation model), by introducing an architectural and analytical scheme, modeled following a complex and dynamical approach, consisting of three inter-operable level and different networked attributes, to quantify the quality of IoMT microservices depending on a changeable context of use. We conduct simulations to display and quantify the structural complex properties and performance statistical estimators. We select and classify suitable attributes through a further detailed procedure in a supplementary information document.

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“…Accordingly, nano-devices, implants, and onbody sensors can communicate and transmit data in real-time with extremely high reliability and availability to edge devices or cloud centres for short-and long-term medical analysis. In particular, mURLLC also plays a key role in hospitalbased telestration where doctors can monitor and manage the surgery procedure remotely using real-time video streaming from medical robotics and assistant devices interconnected by 6G core networks [81]. Moreover, 6G-based URLLC has been exploited to facilitate connected ambulance in future healthcare, by allowing real-time video streaming with high color resolution for reliable diagnosis to clinicians and paramedical staff from the hospital at moderately high speeds (up to 100 km/h) [82].…”

Section: G For Iot Applicationsmentioning

confidence: 99%

6G Internet of Things: A Comprehensive Survey

Nguyen,

Ding,

Pathirana

et al. 2021

Preprint

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The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems. In this article, we explore the emerging opportunities brought by 6G technologies in IoT networks and applications, by conducting a holistic survey on the convergence of 6G and IoT. We first shed light on some of the most fundamental 6G technologies that are expected to empower future IoT networks, including edge intelligence, reconfigurable intelligent surfaces, space-air-groundunderwater communications, Terahertz communications, massive ultra-reliable and low-latency communications, and blockchain. Particularly, compared to the other related survey papers, we provide an in-depth discussion of the roles of 6G in a wide range of prospective IoT applications via five key domains, namely Healthcare Internet of Things, Vehicular Internet of Things and Autonomous Driving, Unmanned Aerial Vehicles, Satellite Internet of Things, and Industrial Internet of Things. Finally, we highlight interesting research challenges and point out potential directions to spur further research in this promising area.

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6G Access Network for Intelligent Internet of Healthcare Things: Opportunity, Challenges, and Research Directions

Cited by 65 publications

References 21 publications

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario

6G Internet of Things: A Comprehensive Survey

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