5G IoT and edge computing for smart healthcare

5G IoT and edge computing for smart healthcare

5G IoT and Edge Computing for Smart Healthcare addresses the importance of a 5G IoT and Edge-Cognitive-Computing-based system for the successful implementation and realization of a smart-healthcare system. The book provides insights on 5G technologies, along with intelligent processing algorithms/pr...

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Detalles Bibliográficos
Otros Autores: Bhoi, Akash Kumar, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: [Place of publication not identified] : Elsevier [2022]
Colección:Intelligent Data-Centric Systems
Materias:
Medical informatics.
Artificial intelligence > Medical applications.
5G mobile communication systems.
Edge computing.
Internet of things.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009835437506719
Tabla de Contenidos:
  • 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
  • 3.5 Heterogeneity and interoperability
  • 3.5.1 User interoperability
  • 3.5.1.1 Locating the device through identification and classification
  • 3.5.1.2 Syntactic and semantic interoperability for interconnecting devices
  • 3.5.2 Device interoperability
  • 3.6 Research issues and challenges
  • 3.6.1 Resource allocation
  • 3.6.2 Interference management
  • 3.6.3 Power allocation
  • 3.6.4 User association
  • 3.6.5 Computational complexity and multiaccess edge computing
  • 3.6.6 Current research in HetNet based on various technologies
  • 3.7 Smart healthcare using 5G5G Inter of Things: a case-study
  • 3.7.1 Mobile cellular network architecture: 5th generation
  • 3.7.1.1 5G5G system architecture
  • 3.7.1.2 Master core technology
  • 3.7.2 ZigBee IP
  • 3.7.3 Healthcare system architecture using wireless sensor network and mobile cellular network
  • 3.7.3.1 System protocol
  • 3.7.3.2 Data transmission by 5G terminal in ZigBee network.
  • 3.7.3.3 Data transmission through 5G terminal by ZigBee network
  • 3.8 Conclusions
  • References
  • 4 An overview of low power hardware architecture for edge computing devices
  • 4.1 Introduction
  • 4.2 Basic concepts of cloud, fog and edge computing infrastructure
  • 4.2.1 Role of edge computing in Internet of Things
  • 4.2.2 Edge intelligence and 5G in Internet of Things based smart healthcare system
  • 4.3 Low power hardware architecture for edge computing devices
  • 4.3.1 Objectives of hardware development in edge computing
  • 4.3.2 System architecture
  • 4.3.3 Central processing unit architecture
  • 4.3.4 Input-output architecture
  • 4.3.5 Power consumption
  • 4.3.6 Data processing and algorithmic optimization
  • 4.4 Examples of edge computing devices
  • 4.5 Edge computing for intelligent healthcare applications
  • 4.5.1 Edge computing for healthcare applications
  • 4.5.2 Advantages of edge computing for healthcare applications
  • 4.5.3 Implementation challenges of edge computing in healthcare systems
  • 4.5.4 Applications of edge computing based healthcare system
  • 4.5.5 Patient data security in edge computing
  • 4.6 Impact of edge computing, Internet of Things and 5G on smart healthcare systems
  • 4.7 Conclusion and future scope of research
  • References
  • 5 Convergent network architecture of 5G and MEC
  • 5.1 Introduction
  • 5.2 Technical overview on 5G network with MEC
  • 5.2.1 5G with multi-access edge computing (MEC): a technology enabler
  • 5.2.2 Application splitting in MEC
  • 5.2.3 Layered service oriented architecture for 5G MEC
  • 5.3 Convergent network architecture for 5G with MEC
  • 5.4 Current research in 5G with MEC
  • 5.5 Challenges and issues in implementation of MEC
  • 5.5.1 Communication and computation perspective
  • 5.5.1.1 MEC service orchestration and programmability.
  • 5.5.1.2 MEC service continuity and mobility and service enhancements
  • 5.5.1.3 MEC security and privacy
  • 5.5.1.4 Standardization of protocols
  • 5.5.1.5 MEC service monetization
  • 5.5.1.6 Edge cloud infrastructure and resource management
  • 5.5.1.7 Mobile data offloading
  • 5.5.2 Application perspective
  • 5.5.2.1 Industrial IoT application in 5G
  • 5.5.2.2 Large scale healthcare and big data management
  • 5.5.2.3 Integration of AI and 5G for MEC enabled healthcare application
  • 5.6 Conclusions
  • References
  • 6 An efficient lightweight speck technique for edge-IoT-based smart healthcare systems
  • 6.1 Introduction
  • 6.2 The Internet of Things in smart healthcare system
  • 6.2.1 Support for diagnosis treatment
  • 6.2.2 Management of diseases
  • 6.2.3 Risk monitoring and prevention of disease
  • 6.2.4 Virtual support
  • 6.2.5 Smart healthcare hospitals support
  • 6.3 Application of edge computing in smart healthcare system
  • 6.4 Application of encryptions algorithm in smart healthcare system
  • 6.4.1 Speck encryption
  • 6.5 Results and discussion
  • 6.6 Conclusions and future research directions
  • References
  • 7 Deep learning approaches for the cardiovascular disease diagnosis using smartphone
  • 7.1 Introduction
  • 7.2 Disease diagnosis and treatment
  • 7.3 Deep learning approaches for the disease diagnosis and treatment
  • 7.3.1 Artificial neural networks
  • 7.3.2 Deep learning
  • 7.3.3 Convolutional Neural Networks
  • 7.4 Case study of a smartphone-based Atrial Fibrillation Detection
  • 7.4.1 Smartphone data acquisition
  • 7.4.2 Biomedical signal processing
  • 7.4.3 Prediction and classification
  • 7.4.4 Experimental data
  • 7.4.5 Performance evaluation measures
  • 7.4.6 Experimental results
  • 7.5 Discussion
  • 7.6 Conclusion
  • References
  • 8 Advanced pattern recognition tools for disease diagnosis
  • 8.1 Introduction.
  • 8.2 Disease diagnosis
  • 8.3 Pattern recognition tools for the disease diagnosis
  • 8.3.1 Artificial neural networks
  • 8.3.2 K-nearest neighbor
  • 8.3.3 Support vector machines
  • 8.3.4 Random forests
  • 8.3.5 Bagging
  • 8.3.6 AdaBoost
  • 8.3.7 XGBoost
  • 8.3.8 Deep learning
  • 8.3.9 Convolutional neural network
  • 8.3.10 Transfer learning
  • 8.4 Case study of COVID-19 detection
  • 8.4.1 Experimental data
  • 8.4.2 Performance evaluation measures
  • 8.4.3 Feature extraction using transfer learning
  • 8.4.4 Experimental results
  • 8.5 Discussion
  • 8.6 Conclusions
  • References
  • 9 Brain-computer interface in Internet of Things environment
  • 9.1 Introduction
  • 9.1.1 Components of BCI
  • 9.1.2 Types of BCI
  • 9.1.3 How does BCI work?
  • 9.1.4 Key features of BCI
  • 9.1.5 Applications
  • 9.2 Brain-computer interface classification
  • 9.2.1 Noninvasive BCI
  • 9.2.2 Semiinvasive or partially invasive BCI
  • 9.2.3 Invasive BCI
  • 9.3 Key elements of BCI
  • 9.3.1 Signal acquisition
  • 9.3.2 Preprocessing or signal enhancement
  • 9.3.3 Feature extraction
  • 9.3.4 Classification stage
  • 9.3.5 Feature translation or control interface stage
  • 9.3.6 Device output or feedback stage
  • 9.4 Modalities of BCI
  • 9.4.1 Electrical and magnetic signals
  • 9.4.1.1 Intracortical electrode array
  • 9.4.1.2 Electrocorticography
  • 9.4.1.3 Electroencephalography
  • 9.4.1.4 Magnetoencephalography
  • 9.4.2 Metabolic signals
  • 9.4.2.1 Positron emission tomography
  • 9.4.2.2 Functional magnetic resonance imaging
  • 9.4.2.3 Functional near-infrared spectroscopy
  • 9.5 Computational intelligence methods in BCI/BMI
  • 9.5.1 State of the prior art
  • 9.5.1.1 Preprocessing
  • 9.5.1.2 Feature extraction
  • 9.5.1.3 Feature classification
  • 9.5.1.4 Performance evaluation of BCI systems
  • 9.6 Online and offline BCI applications
  • 9.7 BCI for the Internet of Things.
  • 9.8 Secure brain-brain communication.

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