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E-raamat: 5G Impact on Biomedical Engineering: Wireless Technologies Applications [Taylor & Francis e-raamat]

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  • Formaat: 186 pages, 11 Tables, black and white; 17 Line drawings, black and white; 32 Halftones, black and white; 49 Illustrations, black and white
  • Sari: Prospects in Biomedical Engineering and Applications
  • Ilmumisaeg: 19-May-2022
  • Kirjastus: CRC Press
  • ISBN-13: 9781003058434
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  • Taylor & Francis e-raamat
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5G Impact on Biomedical Engineering: Wireless Technologies ApplicationsSuurem pilt
  • Formaat: 186 pages, 11 Tables, black and white; 17 Line drawings, black and white; 32 Halftones, black and white; 49 Illustrations, black and white
  • Sari: Prospects in Biomedical Engineering and Applications
  • Ilmumisaeg: 19-May-2022
  • Kirjastus: CRC Press
  • ISBN-13: 9781003058434
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003058434
"This book is dedicated to studying the innovations and advancements of wireless networks for biomedical applications. This book focuses on a wide range of wireless technologies including body sensor networks, mobile networks, internet of things, mobile cloud computing, pervasive computing and wearable computing"--

Considering the importance of wireless networks in healthcare, this book is dedicated to studying the innovations and advancements of wireless networks for biomedical application and their impact. This book focuses on a wide range of wireless technologies related to healthcare and biomedical applications which include, among others, body sensor networks, mobile networks, internet of things, mobile cloud computing, pervasive computing and wearable computing. First the authors explain how biomedical applications using wireless technologies are built across networks. The authors also detail 5G spectrum splicing for medical applicatons. They then discuss how wearable computing can be used as activity recognition tools for biomedical applications through remote health monitoring and and remote health risk assessment. Finally the authors provide detailed discussions on security and privacy in wirelessly transmitted medical senor data. This book targets research-oriented and professional readers. It would fit as a recommended supplemental reading for graduate students. It also helps researchers enter the field of wireless biomedical applications.

This book is dedicated to studying the innovations and advancements of wireless networks for biomedical applications. This book focuses on a wide range of wireless technologies including body sensor networks, mobile networks, internet of things, mobile cloud computing, pervasive computing and wearable computing.
Preface xi
Editor Biographies xiii
List of Figures
xv
List of Tables
xvii
Contributors xix
Symbols xxi
I Introduction
1(36)
1 Healthcare 4.0: Technologies and Policies
3(16)
1.1 Introduction
3(1)
1.2 Technology and e-Health
4(4)
1.2.1 E-Health through Cloud Computing
4(1)
1.2.2 E-Health through Internet of Things
5(2)
1.2.3 E-Health through 5G
7(1)
1.3 Policy Challenges
8(4)
1.3.1 Trust and Data Privacy
9(2)
1.3.2 Incentives for Using e-Health
11(1)
1.3.3 Responsibility and Evidence
11(1)
1.3.4 Spectrum Licensing and Regulation
12(1)
1.4 Conclusion
12(7)
2 Management of Collaborative BSN in Smart Environments
19(18)
2.1 Introduction
19(1)
2.2 BSN Architecture and Technologies
20(5)
2.2.1 General Architecture
20(1)
2.2.2 BSN Applications
20(1)
2.2.2.1 Medical Applications
20(2)
2.2.2.2 Non-Medical Applications
22(1)
2.2.3 Sensors Types, Properties, and Challenges
22(1)
2.2.3.1 Sensors Types
22(2)
2.2.3.2 BSN Challenges
24(1)
2.2.4 Sensors' Wireless Communication Technologies
24(1)
2.3 From BSN to CBSN
25(7)
2.3.1 Introduction
25(1)
2.3.2 CBSN Concept and Architecture
26(1)
2.3.3 CBSN Applications
27(1)
2.3.4 Comparison between BSN and CBSN
28(1)
2.3.5 Major Challenges in CBSN
28(1)
2.3.6 Open Research Issues in CBSN
28(2)
2.3.6.1 Sensor Nodes
30(1)
2.3.6.2 Data Fusion
30(1)
2.3.6.3 MAC Protocols
30(1)
2.3.6.4 Routing
31(1)
2.3.6.5 Inter-BSN Communication
31(1)
2.3.6.6 Coverage and Connectivity
31(1)
2.3.6.7 Localization and Tracking
32(1)
2.3.6.8 Power Supply and Energy Concern
32(1)
2.3.6.9 Security
32(1)
2.4 Conclusion
32(5)
II Communication Technologies
37(74)
3 Smart Resource Allocation for LoRaWAN-based e-Health Applications in Dense Deployments
39(18)
3.1 Introduction
39(2)
3.2 Related Works
41(3)
3.2.1 SF Allocation in LoRaWAN
42(1)
3.2.2 Contribution
43(1)
3.3 System Model and Specifications
44(1)
3.4 Optimization Problem for SF Selection
45(1)
3.5 Spreading Factor Selection Game in LoRaWAN
45(3)
3.6 Distributed Learning for SF Selection in LoRaWAN
48(1)
3.7 Experimental Evaluation
48(5)
3.7.1 SF Selection Game vs. EXP3
49(1)
3.7.2 Energy Efficiency in LoRaWAN
50(3)
3.8 Conclusion
53(4)
4 Dynamic Health Assessment in Water Environments using LPWAN Technologies
57(16)
4.1 Introduction
57(1)
4.2 Application Domains in Water Environments
58(2)
4.2.1 First Aid Operations
59(1)
4.2.2 Monitoring Floods
59(1)
4.3 Real-time Monitoring Systems in Water Environments
60(3)
4.3.1 Discovering Navigation Environment
60(1)
4.3.2 Survivors Identification and Assessment of Their Health Conditions
61(2)
4.4 Wireless Communication in Water Networks
63(2)
4.4.1 LTE-M Communication
63(1)
4.4.2 NB-IoT Communication
64(1)
4.4.3 LoRa Communication
64(1)
4.5 Proposed LoRa-based Monitoring System
65(3)
4.6 Conclusion
68(5)
5 Quality of Service Provisioning for Ambulance Tele-medicine in a Slice-based 5G Network
73(18)
5.1 Introduction
73(1)
5.2 Tele-medicine 5G Network Slice
74(4)
5.2.1 Network Slicing
74(1)
5.2.2 5G Reference Slices
75(1)
5.2.3 Tele-Medicine Network Slice Architecture
75(3)
5.3 Mobility Management Solution Overview
78(3)
5.3.1 Slice Attachment
79(1)
5.3.2 Slice Handover Solution
80(1)
5.4 Slice Selection Function
81(3)
5.4.1 Related works
81(1)
5.4.2 Slice Selection Algorithm
82(1)
5.4.3 End-to-End Slice Load Utility Calculation
82(1)
5.4.4 Candidates PoA QoS Utility Calculation
83(1)
5.4.5 Target Slice Selection
84(1)
5.5 Performance Evaluation
84(4)
5.6 Conclusion
88(3)
6 Routing Protocol Algorithms for Single-Body and Multi-Body Sensor Networks
91(20)
6.1 Introduction
91(2)
6.2 Related Work
93(2)
6.3 Comparison of Different Routing Models
95(3)
6.4 An Efficient Cluster-based Routing Model
98(3)
6.4.1 Cluster Formation
98(1)
6.4.2 Cluster Head Election
99(1)
6.4.3 Routing Operation
99(2)
6.5 Implementation and Results
101(2)
6.6 Conclusion
103(8)
III Applications
111(74)
7 Towards WBSNs Based Healthcare Applications: From Energy-Efficient Data Collection to Fusion
113(16)
7.1 Introduction
113(1)
7.2 WBSN: Architecture and Biosensor Nodes
114(1)
7.3 Healthcare Applications
115(2)
7.4 Healthcare Application Requirements
117(1)
7.5 Energy-Efficient Mechanisms
118(2)
7.6 Multi-sensor Data Fusion
120(2)
7.7 Challenging Aspects in Data
122(1)
7.8 High-Level Fusion: Data-Driven vs Knowledge-Driven Approaches
122(1)
7.9 Discussion
123(2)
7.10 Conclusion
125(4)
8 Data Quality Management for Pervasive Health Monitoring in Body Sensor Networks
129(18)
8.1 Introduction
129(1)
8.2 Data Quality Basic Concepts
130(6)
8.2.1 Data Quality Dimensions
132(2)
8.2.2 Data Quality Factors
134(1)
8.2.2.1 Sensor level
134(1)
8.2.2.2 Human level
135(1)
8.2.2.3 Network level
136(1)
8.3 Data Quality Remedies
136(4)
8.3.1 Data Cleaning Approaches in WSNs
136(2)
8.3.2 Data Cleaning Approaches in Healthcare Industry
138(2)
8.4 Conclusion
140(7)
9 Wireless Techniques and Applications of the Internet of Medical Things
147(20)
9.1 Introduction
147(1)
9.2 Historical view and trends of IoMT in medical applications
148(1)
9.2.1 Physiological Analysis
148(1)
9.2.2 Rehabilitation Systems
148(1)
9.2.3 Nutritional Evaluation and Skin Pathologies
149(1)
9.2.4 Epidemic Infections and Diseases Spot Localization
149(1)
9.2.5 Diabetes Treatment
149(1)
9.3 Advantages of IoMT
149(1)
9.4 Wireless Technology for Healthcare
150(2)
9.5 Mobile Communications for Healthcare
152(2)
9.5.1 Security Threats
153(1)
9.5.2 Wireless Communication and HIPAA Compliance
153(1)
9.5.3 Considerations of Wireless Technology in the Healthcare System
153(1)
9.6 IoT-based Healthcare Applications
154(9)
9.6.1 IoMT-based Health Monitoring
155(3)
9.6.2 Application of COVID-19 Fighting Using Cognitive Internet of Medical Things
158(3)
9.6.3 Early Identification and Monitoring of COVID-19 Individuals Deploying IoMT-based Framework
161(2)
9.7 Conclusion
163(4)
10 Deep Learning for IoT-Healthcare Based on Physiological Signals
167(18)
10.1 Introduction
167(2)
10.2 Physiological Signals
169(2)
10.2.1 Electrocardiogram
169(1)
10.2.2 Photoplethysmogram
170(1)
10.2.3 Electromyogram
170(1)
10.2.4 Electrodermal Activity
170(1)
10.2.5 Electroencephalography
170(1)
10.3 Deep Learning
171(5)
10.3.1 Generative Models
173(1)
10.3.1.1 Restricted Boltzmann Machine
173(1)
10.3.1.2 Autoencoder
174(1)
10.3.2 Hybrid Models
174(1)
10.3.3 Discriminative Models
175(1)
10.3.3.1 Multi-Layer Perceptron
175(1)
10.3.3.2 Convolutional Neural Network
175(1)
10.3.3.3 Long Short-Term Memory
175(1)
10.4 Deep Learning-based Physiological Signals Analysis
176(4)
10.4.1 Time Series Classification
176(1)
10.4.2 Physiological Signals Cleaning
177(2)
10.4.3 Artifacts Removal
179(1)
10.5 Conclusion
180(5)
Index 185
Jacques Bou Abdo is an Assistant Professor of Cyber Systems at University of Nebraska at Kearney. His main research interests include: Cybersecurity, Blockchain, Recommender Systems, Machine Learning and Network Economics.

Jacques Demerjian is a Full Professor of Computer Science and the Director of LaRRIS (Laboratoire de Recherche en Réseaux, Informatique et Sécurité) research laboratory at the Faculty of Sciences at the Lebanese University. His main research interests include: Body Sensor Network and E-Health Monitoring.

Abdallah Makhoul is a Full Professor of Computer Science at University of Bourgogne - Franche-Comté, France. He is the head the research team OMNI (Optimization, Mobility and NetworkIng). His main research interests include: Distributed Algorithms, Internet of Things, Programmable Matter, E-Health Monitoring and real-time issues in Wireless Sensor Networks.
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