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E-raamat: 5G IoT and Edge Computing for Smart Healthcare

Edited by (Directorate of Research, Gangtok, Sikkim Manipal University, Sikkim, India), Edited by , Edited by (Assistant P), Edited by (Professor and Senior Researcher, Federal University of Ceara, Fortaleza, Graduate Program on Teleinformatics Engineering, Fortaleza/CE, Brazil)
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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/processors that have been adopted for processing the medical data that would assist in addressing the challenges in computer-aided diagnosis and clinical risk analysis on a real-time basis. Each chapter is self-sufficient, solving real-time problems through novel approaches that help the audience acquire the right knowledge.

With the progressive development of medical and communication - computer technologies, the healthcare system has seen a tremendous opportunity to support the demand of today's new requirements.

  • Focuses on the advancement of 5G in terms of its security and privacy aspects, which is very important in health care systems
  • Address advancements in signal processing and, more specifically, the cognitive computing algorithm to make the system more real-time
  • Gives insights into various information-processing models and the architecture of layers to realize a 5G based smart health care system
List of contributors
xiii
Chapter 1 Edge-IoMT-based enabled architecture for smart healthcare system
1(28)
Joseph Bamidele Awotunde
Muhammed Fazal Ijaz
Akash Kumar Bhoi
Muyideen AbdulRaheem
Idowu Dauda Oladipo
Paolo Barsocchi
1.1 Introduction
1(2)
1.2 Applications of an IoMT-based system in the healthcare industry
3(4)
1.3 Application of edge computing in smart healthcare systems
7(4)
1.4 Challenges of using edge computing with IoMT-based system in smart healthcare system
11(2)
1.5 The framework for edge-IoMT-based smart healthcare system
13(2)
1.6 Case study for the application of edge-IoMT-based systems enabled for the diagnosis of diabetes mellitus
15(3)
1.6.1 Experimental results
16(2)
1.7 Future prospects of edge computing for internet of medical things
18(3)
1.8 Conclusions and future research directions
21(8)
References
22(7)
Chapter 2 Physical layer architecture of 5G enabled IoT/IoMT system
29(16)
Anh-Tu Le
Munyaradzi Munochiveyi
Samarendra Nath Sur
2.1 Architecture of IoT/IoMT system
29(4)
2.1.1 Sensor layer
31(1)
2.1.2 Gateway layer
31(1)
2.1.3 Network layer
32(1)
2.1.4 Visualization layer
33(1)
2.2 Consideration of uplink healthcare IoT system relying on NOMA
33(8)
2.2.1 Introduction
33(1)
2.2.2 System model
34(1)
2.2.3 Outage probability for UL NOMA
35(3)
2.2.4 Ergodic capacity of UL NOMA
38(1)
2.2.5 Numerical results and discussions
38(3)
2.3 Conclusions
41(4)
References
41(4)
Chapter 3 HetNet/M2M/D2D communication in 5G technologies
45(44)
Ayaskanta Mishra
Anita Swain
Arun Kumar Ray
Raed M. Shubair
3.1 Introduction
45(3)
3.2 Heterogenous networks in the era of 5G
48(10)
3.2.1 5G mobile communication standards and enhanced features
48(2)
3.2.2 5G heterogeneous network architecture
50(4)
3.2.3 Intelligent software defined networkiramework of 5G HetNets
54(1)
3.2.4 Next-Gen 5G wireless network
54(1)
3.2.5 Internet of Things toward 5G and heterogenous wireless networks
55(2)
3.2.6 5G-HetNet H-CRAN fronthaul and TWDM-PON backhaul: QoS-aware virtualization for resource management
57(1)
3.2.7 Spectrum allocation and user association in 5G HetNet mmWave communication: a coordinated framework
58(1)
3.2.8 Diverse service provisioning in 5G and beyond: an intelligent self-sustained radio access network slicing framework
58(1)
3.3 Device-to-Device communication in 5G HetNets
58(3)
3.4 Machine-to-Machine communication in 5G HetNets
61(9)
3.4.1 Machine-to-Machine communication in 5G: state of the art architecture, recent advances and challenges
61(1)
3.4.2 Recent advancement in the Internet of Things related standard: oneM2M perspective
62(4)
3.4.3 M2M traffic in 5G HetNets
66(2)
3.4.4 Distributed gateway selection for M2M communication cognitive 5G5G networks
68(1)
3.4.5 Algorithm for clusterization, aggregation, and prioritization of M2M devices in 5G5G HetNets
69(1)
3.5 Heterogeneity and interoperability
70(2)
3.5.1 User interoperability
70(2)
3.5.2 Device interoperability
72(1)
3.6 Research issues and challenges
72(5)
3.6.1 Resource allocation
73(1)
3.6.2 Interference management
74(1)
3.6.3 Power allocation
74(1)
3.6.4 User association
75(1)
3.6.5 Computational complexity and multiaccess edge computing
75(1)
3.6.6 Current research in HetNet based on various technologies
76(1)
3.7 Smart healthcare using 5G5G Inter of Things: a case-study
77(5)
3.7.1 Mobile cellular network architecture: 5th generation
77(1)
3.7.2 ZigBeelP
78(1)
3.7.3 Healthcare system architecture using wireless sensor network and mobile cellular network
78(4)
3.8 Conclusions
82(7)
References
82(7)
Chapter 4 An overview of low power hardware architecture for edge computing devices
89(22)
Kushika Sivaprakasam
P. Sriramalakshmi
Pushpa Singh
M.S. Bhaskar
4.1 Introduction
89(2)
4.2 Basic concepts of cloud, fog and edge computing infrastructure
91(4)
4.2.1 Role of edge computing in Internet of Things
93(1)
4.2.2 Edge intelligence and 5G in Internet of Things based smart healthcare system
94(1)
4.3 Low power hardware architecture for edge computing devices
95(5)
4.3.1 Objectives of hardware development in edge computing
95(1)
4.3.2 System architecture
96(1)
4.3.3 Central processing unit architecture
96(2)
4.3.4 Input---output architecture
98(1)
4.3.5 Power consumption
99(1)
4.3.6 Data processing and algorithmic optimization
99(1)
4.4 Examples of edge computing devices
100(1)
4.5 Edge computing for intelligent healthcare applications
101(5)
4.5.1 Edge computing for healthcare applications
101(1)
4.5.2 Advantages of edge computing for healthcare applications
102(2)
4.5.3 Implementation challenges of edge computing in healthcare systems
104(1)
4.5.4 Applications of edge computing based healthcare system
104(1)
4.5.5 Patient data security in edge computing
105(1)
4.6 Impact of edge computing, Internet of Things and 5G on smart healthcare systems
106(1)
4.7 Conclusion and future scope of research
107(4)
References
107(4)
Chapter 5 Convergent network architecture of 5G and MEC
111(28)
Ayaskanta Mishra
Anita Swain
Arun Kumar Ray
Raed M. Shubair
5.1 Introduction
111(3)
5.2 Technical overview on 5G network with MEC
114(8)
5.2.1 5G with multi-access edge computing (MEC): a technology enabler
115(2)
5.2.2 Application splitting in MEC
117(2)
5.2.3 Layered service oriented architecture for 5G MEC
119(3)
5.3 Convergent network architecture for 5G with MEC
122(3)
5.4 Current research in 5G with MEC
125(4)
5.5 Challenges and issues in implementation of MEC
129(5)
5.5.1 Communication and computation perspective
131(2)
5.5.2 Application perspective
133(1)
5.6 Conclusions
134(5)
References
135(4)
Chapter 6 An efficient lightweight speck technique for edge-IoT-based smart healthcare systems
139(24)
Muyideen AbdulRaheem
Idowu Dauda Oladipo
Alfonso Gonzalez-Briones
Joseph Bamidele Awotunde
Adekola Rasheed Tomori
Rasheed Gbenga Jimoh
6.1 Introduction
139(2)
6.2 The Internet of Things in smart healthcare system
141(5)
6.2.1 Support for diagnosis treatment
142(1)
6.2.2 Management of diseases
143(1)
6.2.3 Risk monitoring and prevention of disease
144(1)
6.2.4 Virtual support
144(1)
6.2.5 Smart healthcare hospitals support
145(1)
6.3 Application of edge computing in smart healthcare system
146(2)
6.4 Application of encryptions algorithm in smart healthcare system
148(4)
6.4.1 Speck encryption
150(2)
6.5 Results and discussion
152(5)
6.6 Conclusions and future research directions
157(6)
References
158(5)
Chapter 7 Deep learning approaches for the cardiovascular disease diagnosis using smartphone
163(32)
Abdulhamit Subasi
Elina Kontio
Mojtaba Jafaritadi
7.1 Introduction
163(4)
7.2 Disease diagnosis and treatment
167(3)
7.3 Deep learning approaches for the disease diagnosis and treatment
170(3)
7.3.1 Artificial neural networks
171(1)
7.3.2 Deep learning
171(1)
7.3.3 Convolutional Neural Networks
172(1)
7.4 Case study of a smartphone-based Atrial Fibrillation Detection
173(11)
7.4.1 Smartphone data acquisition
175(1)
7.4.2 Biomedical signal processing
176(1)
7.4.3 Prediction and classification
177(4)
7.4.4 Experimental data
181(1)
7.4.5 Performance evaluation measures
182(1)
7.4.6 Experimental results
183(1)
7.5 Discussion
184(2)
7.6 Conclusion
186(9)
References
186(9)
Chapter 8 Advanced pattern recognition tools for disease diagnosis
195(36)
Abdulhamit Subasi
Siba Smarak Panigrahi
Bhalchandra Sunil Patil
M. Abdullah Canbaz
Riku Klen
8.1 Introduction
195(4)
8.2 Disease diagnosis
199(4)
8.3 Pattern recognition tools for the disease diagnosis
203(7)
8.3.1 Artificial neural networks
204(1)
8.3.2 K-nearest neighbor
204(1)
8.3.3 Support vector machines
205(1)
8.3.4 Random forests
205(1)
8.3.5 Bagging
205(1)
8.3.6 AdaBoost
206(1)
8.3.7 XGBoost
206(1)
8.3.8 Deep learning
206(1)
8.3.9 Convolutional neural network
207(1)
8.3.10 Transfer learning
207(3)
8.4 Case study of COVID-19 detection
210(10)
8.4.1 Experimental data
213(1)
8.4.2 Performance evaluation measures
213(1)
8.4.3 Feature extraction using transfer learning
213(1)
8.4.4 Experimental results
214(6)
8.5 Discussion
220(1)
8.6 Conclusions
221(10)
References
222(9)
Chapter 9 Brain-computer interface in Internet of Things environment
231(26)
Vijay Jeyakumar
Palani Thanaraj Krishnan
Prema Sundaram
Alex Noel Joseph Raj
9.1 Introduction
231(4)
9.1.1 Components of BCI
232(1)
9.1.2 Types of BCI
233(1)
9.1.3 How does BCI work?
233(1)
9.1.4 Key features of BCI
234(1)
9.1.5 Applications
234(1)
9.2 Brain-computer interface classification
235(2)
9.2.1 Noninvasive BCI
235(2)
9.2.2 Semiinvasive or partially invasive BCI
237(1)
9.2.3 Invasive BCI
237(1)
9.3 Key elements of BCI
237(3)
9.3.1 Signal acquisition
238(1)
9.3.2 Preprocessing or signal enhancement
238(1)
9.3.3 Feature extraction
238(1)
9.3.4 Classification stage
238(1)
9.3.5 Feature translation or control interface stage
239(1)
9.3.6 Device output or feedback stage
239(1)
9.4 Modalities of BCI
240(2)
9.4.1 Electrical and magnetic signals
240(1)
9.4.2 Metabolic signals
241(1)
9.5 Computational intelligence methods in BCI/BMI
242(3)
9.5.1 State of the prior art
242(3)
9.6 Online and offline BCI applications
245(1)
9.7 BCI for the Internet of Things
245(4)
9.8 Secure brain-brain communication
249(2)
9.8.1 Edge computing for brain--to--things
250(1)
9.9 Summary and conclusion
251(1)
9.10 Future research directions and challenges
251(6)
Abbreviations
252(1)
References
253(4)
Chapter 10 Early detection of COVID-19 pneumonia based on ground-glass opacity (GGO) features of computerized tomography (CT) angiography
257(22)
H.M.K.K.M.B. Herath
G.M.K.B. Karunasena
B.G.D.A. Madhusanka
10.1 Introduction
257(2)
10.2 Background
259(3)
10.2.1 Ground-glass opacity (GGO)
259(1)
10.2.2 Support vector machine (SVM)
260(1)
10.2.3 Histogram of oriented gradients (HOG) algorithm
260(1)
10.2.4 Convolutional neural network (CNN)
261(1)
10.2.5 Literature
262(1)
10.3 Materials and methods
262(4)
10.3.1 Dataset description
262(1)
10.3.2 Methodology
263(3)
10.4 Results and analysis
266(8)
10.4.1 Test results of the COVID-19 pneumonia detection system
267(4)
10.4.2 Analysis of the test results
271(3)
10.5 Conclusion
274(5)
References
275(4)
Chapter 11 Applications of wearable technologies in healthcare: an analytical study
279(22)
Hiren Kumar Thakkar
Shamit Roy Chowdhury
Akash Kumar Bhoi
Paolo Barsocchi
11.1 Introduction
279(2)
11.2 Application of wearable devices
281(2)
11.3 The importance of wearable technology in healthcare
283(1)
11.3.1 Personalization
283(1)
11.3.2 Remote patient monitoring
283(1)
11.3.3 Early diagnosis
283(1)
11.3.4 Medication adherence
284(1)
11.3.5 Complete information
284(1)
11.3.6 Cost savings
284(1)
11.4 Current scenario of wearable computing
284(2)
11.5 The wearable working procedure
286(1)
11.6 Wearables in healthcare
286(3)
11.6.1 Weight loss
286(1)
11.6.2 Medication tracking
287(1)
11.6.3 Virtual doctor consultations
287(1)
11.6.4 Geiger counter for illnesses
288(1)
11.6.5 Hydration tool
288(1)
11.6.6 Pregnancy and fertility tracking
288(1)
11.7 State-of-the-art implementation of wearables
289(7)
11.7.1 Detection of soft fall in disabled or elderly people
289(3)
11.7.2 The third case study is based on the detection of stress using a smart wearable band
292(1)
11.7.3 Use of wearables to reduce cardiovascular risk
293(3)
11.8 Future scope and conclusion
296(5)
References
296(5)
Index 301
Dr. Akash Kumar Bhoi, holds degrees in B.Tech, M.Tech, and Ph.D., and has been contributing to the field of computer science and engineering. He assumed the role of Assistant Professor (Research) at the Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology (SMIT), India, in 2012. In addition to his academic responsibilities, Dr. Bhoi extended his expertise during a research tenure as a Research Associate at the Wireless Networks (WN) Research Laboratory, Institute of Information Science and Technologies, National Research Council (ISTI-CRN) in Pisa, Italy, from January 20, 2021, to January 19, 2022. Dr. Bhoi further serves as the University Ph.D. Course Coordinator for "Research & Publication Ethics (RPE)." He is an active member of professional organizations such as IEEE, ISEIS, and IAENG, and holds associate membership with IEI and UACEE. He plays a significant role as an editorial board member and reviewer for esteemed Indian and international journals and regularly contributes as a reviewer. His research expertise encompasses a wide array of domains, including Biomedical Technologies, the Internet of Things, Computational Intelligence, Antenna technology, and Renewable Energy. Dr. Bhoi has a notable publication record, with multiple papers featured in national and international journals and conferences. Dr. Bhoi has played a pivotal role in the organization of international conferences and workshops, offering his expertise as a key contributor. Currently, he is involved in editing several books in collaboration with international publishers

Victor Hugo C. de Albuquerque [ M17, SM19] is a collaborator Professor and senior researcher at the Graduate Program on Teleinformatics Engineering at the Federal University of Ceará, Brazil, and at the Graduate Program on Telecommunication Engineering, Federal Institute of Education, Science and Technology of Ceará, Fortaleza/CE, Brazil. He has a Ph.D in Mechanical Engineering from the Federal University of Paraíba (UFPB, 2010), an MSc in Teleinformatics Engineering from the Federal University of Ceará (UFC, 2007), and he graduated in Mechatronics Engineering at the Federal Center of Technological Education of Ceará (CEFETCE, 2006). He is a specialist, mainly, in Image Data Science, IoT, Machine/Deep Learning, Pattern Recognition, Robotic. Dr. Samarendra Nath Sur (M2016). He received B.Sc degree in Physics (Hons.) from the University of Burdwan in 2007. He received M.Sc. degree in Electronics Science from Jadavpur University in 2007 and M.Tech degree in Digital Electronics and Advanced Communication from Sikkim Manipal University in 2012. He received his Ph. D degree from NIT, Durgapur. Since 2008, he has been associated with the Sikkim Manipal Institute of Technology, India, where he is currently an assistant professor in the Department of Electronics & Communication Engineering. His current research interests include Broadband Wireless Communication (MIMO and Spread Spectrum Technology), Advanced Digital Signal Processing, and Remote Sensing. He is a Member of the Institute of Electrical and Electronics Engineers (IEEE), IEEE-IoT, IEEE Signal Processing Society, Institution of Engineers (India) (IEI) and International Association of Engineers (IAENG). He was the recipient of the University Medal & Dr. S.C. Mukherjee Memorial Gold Centered Silver Medal from Jadavpur University in 2007. Paolo Barsocchi is a researcher at the Information Science and Technologies Institute (ISTI) of the National Research Council (CNR) at Pisa, Italy. He received his M.Sc. and Ph.D. degrees in information engineering from the University of Pisa in 2003 and 2007, respectively. Since 2017 he is Head of the Wireless Networks Research Laboratory. He is currently involved in several European projects, national and regional projects. The overall amount of attracted and managed funds both at European and national level is about 3M. He has been nominated as a regional competence reference person for advanced manufacturing solutions in Industry 4.0 in 2017, and as a contact person in the Cluster-PON call in 2017 for the CNR Department DIITET, which ISTI belongs to. His research interests are in the areas of internet of things (IoT), wireless sensor networks, cyberphysical systems, machine learning and data analysis techniques, smart environments, ambient assisted living, activity recognition and indoor localization.
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