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5G IoT and edge computing for smart healthcare
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[S.l.] :
Academic Press,
2022.
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| Colección: | Intelligent data centric systems
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Edge-IoMT-based enabled architecture for smart healthcare system
- Physical layer architecture of 5G enabled IoT/IoMT system
- HetNet/M2M/D2D communication in 5G technologies
- An overview of low power hardware architecture for edge computing devices
- Convergent network architecture of 5G and MEC
- An efficient lightweight speck technique for edge-IoT-based smart healthcare systems
- Deep learning approaches for the cardiovascular disease diagnosis using smartphone
- Advanced pattern recognition tools for disease diagnosis
- Brain-computer interface in Internet of Things environment
- Early detection of COVID-19 pneumonia based on ground-glass opacity (GGO) features of computerized tomography (CT) angiography
- Applications of wearable technologies in healthcare: an analytical study.
- Front Cover
- 5G IoT and Edge Computing for Smart Healthcare
- Copyright Page
- Contents
- List of contributors
- 1 Edge-IoMT-based enabled architecture for smart healthcare system
- 1.1 Introduction
- 1.2 Applications of an IoMT-based system in the healthcare industry
- 1.3 Application of edge computing in smart healthcare systems
- 1.4 Challenges of using edge computing with IoMT-based system in smart healthcare system
- 1.5 The framework for edge-IoMT-based smart healthcare system
- 1.6 Case study for the application of edge-IoMT-based systems enabled for the diagnosis of diabetes mellitus
- 1.6.1 Experimental results
- 1.7 Future prospects of edge computing for internet of medical things
- 1.8 Conclusions and future research directions
- References
- 2 Physical layer architecture of 5G enabled IoT/IoMT system
- 2.1 Architecture of IoT/IoMT system
- 2.1.1 Sensor layer
- 2.1.2 Gateway layer
- 2.1.3 Network layer
- 2.1.4 Visualization layer
- 2.2 Consideration of uplink healthcare IoT system relying on NOMA
- 2.2.1 Introduction
- 2.2.2 System model
- 2.2.3 Outage probability for UL NOMA
- 2.2.3.1 Outage probability of x1
- 2.2.3.2 Outage probability of X2
- 2.2.3.3 Asymptotic
- 2.2.4 Ergodic capacity of UL NOMA
- 2.2.5 Numerical results and discussions
- 2.3 Conclusions
- References
- 3 HetNet/M2M/D2D communication in 5G technologies
- 3.1 Introduction
- 3.2 Heterogenous networks in the era of 5G
- 3.2.1 5G mobile communication standards and enhanced features
- 3.2.2 5G heterogeneous network architecture
- 3.2.3 Intelligent software defined network framework of 5G HetNets
- 3.2.4 Next-Gen 5G wireless network
- 3.2.5 Internet of Things toward 5G and heterogenous wireless networks
- 3.2.6 5G-HetNet H-CRAN fronthaul and TWDM-PON backhaul: QoS-aware virtualization for resource management
- 3.2.7 Spectrum allocation and user association in 5G HetNet mmWave communication: a coordinated framework
- 3.2.8 Diverse service provisioning in 5G and beyond: an intelligent self-sustained radio access network slicing framework
- 3.3 Device-to-Device communication in 5G HetNets
- 3.4 Machine-to-Machine communication in 5G HetNets
- 3.4.1 Machine-to-Machine communication in 5G: state of the art architecture, recent advances and challenges
- 3.4.2 Recent advancement in the Internet of Things related standard: oneM2M perspective
- 3.4.2.1 Advantages of oneM2M
- 3.4.2.2 OneM2M protocols
- 3.4.2.3 OneM2M standard platform: a unified common service-oriented communication framework
- 3.4.3 M2M traffic in 5G HetNets
- 3.4.4 Distributed gateway selection for M2M communication cognitive 5G5G networks
- 3.4.5 Algorithm for clusterization, aggregation, and prioritization of M2M devices in 5G5G HetNets