Intelligent Internet of Things for Healthcare and Industry

This book promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of machine learning-based data analytics of IoT infrastructures. This book is focused on the emerging trends, strategies, and applications of IoT in both healthcare and industry data analytics perspectives. The data analytics discussed are relevant for healthcare and industry to meet many technical challenges and issues that need to be addressed to realize this potential. The IoT discussed helps to design and develop the intelligent medical and industry solutions assisted by data analytics and machine learning. At the end of every chapter readers are encouraged to check their understanding by means of brainstorming summary, discussion, exercises and solutions. Focused on the emerging trends, strategies, and applications of IoT in both healthcare and industry data analytics perspectives; Promotes an exchange of research across disciplines on the design and investigation of machine learning-based data analytics of IoT infrastructures; Features case studies emphasizing social and research perspectives on cyber-physical systems, data analytics, intelligence and security. Preface Motivations for This Book Contents of This Book Acknowledgments Contents About the Editors Effectiveness of Machine and Deep Learning in IOT-Enabled Devices for Healthcare System 1 Introduction 1.1 IoT and AI in Healthcare 2 Motivation 3 Background Study 3.1 Framework of IoT in Healthcare 3.2 How IOT Works in Healthcare 3.3 Need of IoT in Healthcare Devices 3.4 Internet of Things in Healthcare: Applications 4 Reported Work 4.1 IOT in Healthcare Using Machine Learning 4.2 IoT in Healthcare Using Deep Learning 4.3 IoT in Healthcare Using Optimization Techniques 5 Comparative Analysis 6 Conclusion and Future Directions References Network Protocols for the Internet of Health Things 1 Introduction 2 Overview of Network Protocol 2.1 List of Network Protocols 2.1.1 Communication 2.1.2 Network Management 2.1.3 Security Protocols 3 Network Protocols for IoT 3.1 IoT Healthcare Networks 3.1.1 Classification of IoT Healthcare Connections 3.1.2 IoT Healthcare Network Topology 3.1.3 IoT Healthcare Network Architecture 3.1.4 Networking Platform: IoT Healthcare 4 Connecting Protocol of IoT 4.1 Message Queuing Telemetry Transport (MQTT) 4.1.1 MQTT: Quality of Service (QoS) 4.1.2 MQTT Applications 4.1.3 Benefits of MQTT 4.1.4 MQTT Limitations 4.1.5 Current Aspects of MQTT 5 Network Encapsulation Protocols for IoT 5.1 6LoWPAN 5.2 6TiSCH 5.2.1 6TiSCH Protocol Stack 5.3 Zigbee IP 5.4 6Lo-IPv6 (6Lo) Working Group 5.5 IPv6 5.5.1 IPv6 over G.9959 5.5.2 IPv6 over Bluetooth Low Energy 5.5.3 IPv6 over NFC 5.5.4 IPv6 over MS/TP (6LoBAC) 5.5.5 IPv6 over DECT/ULE 5.5.6 IPv6 over 802.11ah/Wi-Fi 5.6 IoHT Wireless Protocols: 5G 5.7 IEEE 802.16 (WiMAX) 5.8 Highway Addressable Remote Transducer (HART) 5.9 ISA100.11a: Low Data Rate Network 5.10 LoRaWAN® Specification v1.1 6 Routing Protocols 6.1 RPL 6.2 RPL Enhancements 6.3 CORPL 6.4 Channel-Aware Routing for IoT 7 IoHT Service and Low-Power and Lossy Network IoT Applications 7.1 Ambient Assisted Living (AAL) 7.2 Community Healthcare (CH) 7.3 Adverse Drug Reaction (ADR) 7.4 The Internet of m-Health Things (m-IoT) 7.5 Wearable Device Access (WDA) 7.6 Children Health Information (CHI) 7.7 Semantic Medical Access (SMA) 7.8 Embedded Context Prediction (ECP) 7.9 Embedded Gateway Configuration (EGC) 7.10 Indirect Emergency Healthcare (IEH) 8 IoT Healthcare Applications 8.1 Measuring Glucose Levels 8.2 Blood Pressure Monitoring 8.3 Electrocardiogram Monitoring 8.4 Oxygen Saturation Monitoring 8.5 Medication Management 8.6 Rehabilitation System 8.7 Wheelchair Management 8.8 Healthcare Solutions Using Smartphones 9 IoT Healthcare Security 9.1 Confidentiality 9.2 Integrity 9.3 Authentication 9.4 Information Freshness 9.5 Non-Repudiation 9.6 Authorization 9.7 Resiliency 9.8 Error Limitations 9.9 Self-Protection 10 Security Challenges 10.1 Computational Limitations 10.2 Energy Limitations 10.3 Dynamism 10.4 Scalability 10.5 The Multiplicity of Devices 10.6 A Dynamic Network Topology 10.7 A Multi-Protocol Network 10.8 Data Privacy and Security Updates 11 Future Aspects 12 Conclusion References Affective Computing for eHealth Using Low-Cost Remote Internet of Things-Based EMG Platform 1 Introduction 2 Methods 2.1 Outline 2.2 Hardware 2.3 Data Acquisition 2.4 Data Preprocessing 2.5 Feature Extraction and Reduction 2.6 Classification and Evaluation 3 Results 3.1 Dataset 3.2 Protocol 3.3 Results 4 Conclusions References Application of the Internet of Things (IoT) to Fight the COVID-19 Pandemic 1 Introduction 2 Internet of Things (IoT) in Healthcare Systems 3 Application of Internet of Things Technology in COVID-19 4 Opportunities for Deploying the IoT Technologies During the COVID-19 Pandemic 5 Challenges of Using IoT During the COVID-19 Pandemic 6 Conclusion and Future Research Direction References An Enhanced IoT-Based Array of Sensors for Monitoring Patients' Health 1 Introduction 2 Background and Related Work 2.1 Related Studies on IoT Patient Health Monitoring System 3 Proposed Approach 3.1 Pulse Sensor 3.2 Heartbeat Sensor 3.3 Dallas Temperature Sensor DS18B20 3.4 Ultraviolet (UV) Detection Sensor 3.5 Liquid Crystal Display (LCD) 3.6 ESP8266-1 Wi-Fi Module 3.7 Microcontroller Unit 3.8 Proposed System Circuit Design 3.9 Web Application 4 Results and Analysis 4.1 Comparison with Existing Work 5 Conclusion References A Secured Smart Healthcare Monitoring Systems Using Blockchain Technology 1 Introduction 2 Internet of Thing-Based Challenges in Smart Healthcare System 3 Application of Blockchain in Smart Healthcare System 4 Challenges of Blockchain in Smart Healthcare Systems 5 The Framework for a Secured Smart Healthcare Monitoring Systems Using Blockchain 6 Conclusion and Future Directions References Computational Intelligence in Healthcare with Special Emphasis on Bioinformatics and Internet of Medical Things 1 Introduction 1.1 Contemporaneous Work and Development of Intelligence in the Healthcare Industry 1.2 Overview of Computational Intelligence-Based Paradigms 1.2.1 Artificial Neural Network 1.2.2 Fuzzy Systems 1.2.3 Evolutionary Computing 1.2.4 Swarm Intelligence 1.2.5 Machine Learning Paradigms 1.3 Potential of Computational Intelligence in Medicine and Bioinformatics 2 Challenges to Integration of Computational Intelligence in Medical Applications 2.1 Clinical Applicability of CI-Based Metrics 2.2 Machine Learning Challenges in the Medical Field 2.2.1 Dataset Shift 2.2.2 Accidental Fittings 2.2.3 Bias Based on ML Algorithms 2.3 Susceptibility to Attacks and Other Security-Related Challenges 2.4 Regulatory Impediments and Quality Control 3 Computational Intelligence in Biomedical Engineering 3.1 CI in Cardiovascular Disease Diagnosis 3.2 CI in the Analysis of Electromyography Signals 3.3 CI in the Analysis of Electroencephalogram Signals 3.4 CI in the Analysis of Gait and Movement Patterns 4 Emerging Trends in Computational Intelligence-Based Healthcare Sector and Applications 4.1 Human-Machine Interfaces in IoMT 4.2 Emotion Classification in Medical Industry 4.3 Emerging IoMT-Based Optimized Wearable Technology 4.4 Evaluation and Categorization of Cardiovascular Diseases 4.5 Leveraging Social Media Healthcare for Prognostication and Effective Decision-Making 5 Conclusion and Future Scope References A Review on Security and Privacy of Internet of Medical Things 1 Introduction 2 Risks of Internet of Medical Things 3 Literature Review 4 Challenges for Internet of Medical Things 5 Proposed Solutions for the Current Approach 6 Conclusion References An Introduction to Wearable Sensor Technology 1 Introduction 2 Difficulties and Challenges 2.1 Security Challenges 2.2 Legal Challenges 2.3 Technology Challenges 3 The Reach of the Technology 4 Future of the Technology 5 Conclusion References A Fog-Based Intelligent Secured IoMT Framework for Early Diabetes Prediction 1 Introduction 2 Literature Review 3 Proposed Method 4 Materials and Methods 4.1 Dataset Description 4.2 Performance Metrics 5 Experimental Configuration and Result Analysis 6 Conclusion References A Comprehensive Analysis of Sustainable IoT Infrastructure in the Post-COVID-19 Era 1 Introduction 2 Background Study 3 Problem Domains 3.1 Healthcare 3.2 Transportation 3.3 Manufacturing 3.4 Agriculture 4 Internet of Things-Based Solutions for Smart Sustainable City 4.1 Smart Transportation 4.2 Smart Farming 4.3 Smart Healthcare 4.3.1 Tele-Health Programs 4.3.2 Digital Diagnostics 4.3.3 Remote Tracking 5 Conclusion and Future Scope References Reinforced Rider Optimization Algorithm for Diagnosis of Autism Spectrum Disorder and Medical Data 1 Vitals in Brief 1.1 Imaging the Neuronal Activity 1.2 Note on Search Techniques 1.3 Brief on Feature Selection 2 Summary on the Previous Research 2.1 Machine Learning in ABIDE 2.2 Nature-Inspired Algorithms 2.2.1 Single-Objective Optimization 2.2.2 Discrete Optimization 2.2.3 Multi-Objective Optimization 3 Preliminaries 3.1 Feature Selection 3.2 Dataset and Preprocessing 3.3 Rider Optimization Algorithm 3.3.1 Parameter Initialization 3.3.2 Bypass Rider 3.3.3 Follower Rider 3.3.4 Overtaker Rider 3.3.5 Attacker Rider 4 Proposed Algorithm 4.1 Proposed Reinforced Rider Optimization Algorithm 5 Experimentation and Results 5.1 Wrapper Feature Selection 5.2 Wrapper Model over Medical Data 5.3 Semi-Wrapper for ASD Diagnosis 6 Conclusion and Future Scope References Machine Learning for Fog Computing-Based IoT Networks in Smart City Environment 1 Introduction 2 IoT Architecture 2.1 Three-Layer Architecture 2.2 Five-Layer Architecture 2.3 Fog-Based IoT Architecture 3 Machine Learning in IoT Application 3.1 Supervised Learning 3.2 Unsupervised Learning 3.3 Reinforcement Learning 4 ML-Based IoT Applications 4.1 Example of ML-Based IoT Applications for Smart City Scenario 4.2 Machine Learning in Computing and Resource Management 4.3 Machine Learning in Decision Making 4.4 Machine Learning in Security 5 Conclusions References QoS and Energy Efficiency Using Green Cloud Computing 1 Introduction 2 Green Cloud Computing 2.1 Applications of Green Cloud Computing 2.2 Advantages of Green Cloud Computing 2.2.1 Conserving Electricity 2.2.2 Reduced Carbon Footprint 2.2.3 Going Paperless 2.2.4 Reduced e-Waste 2.2.5 Efficient Resource Management 2.3 Shortcoming of Green Cloud Computing 2.3.1 Implementation Cost 2.3.2 Evolving Technology 2.3.3 Underpowered Computing 3 Green Computing Approaches 3.1 Dynamic Voltage Frequency Scaling (DVFS) 3.2 Nano Data Centers (NaDa) 3.3 Fast Array of Wimpy Nodes (FAWN) 3.4 Virtualization and VM Consolidation 3.5 Geographical Location and Data Center Cooling 4 Security in Green Cloud Computing 4.1 Consumer Apprehensions Toward Green Cloud Security 4.1.1 Loss or Theft of Intellectual Property 4.1.2 Regulatory Compliance Violations 4.1.3 Minimal Visibility of the Cloud Ecosystem 4.1.4 Less Control 4.1.5 Lateral Spreading of Attacks 4.2 Green Cloud Security Aspects 4.2.1 Access Control and Least Privilege 4.2.2 Using SSH and Securely Store Keys 4.2.3 Using Encryption in Cloud 4.2.4 Routing Penetration Tests 4.2.5 Multi-Factor Authentication (MFA) 5 Conclusion References Privacy Issues in Smart IoT for Healthcare and Industry 1 Introduction 2 Related Works 3 Privacy Issues in Healthcare IoT 4 Responsible Parties 5 Privacy-Enhancing Technologies 6 Conclusion References Intelligent IoT for Automotive Industry 4.0: Challenges, Opportunities, and Future Trends 1 Introduction 2 Concept of Intelligent IoT 3 Comprehensive Applications of Intelligent IoT 3.1 Applications That Save Time and Resources 3.2 Applications for Better Lifestyle 3.3 Applications That Improve Healthcare 3.4 Applications for Personalization 4 Intelligent IoT in Image Processing, Pattern Recognition, and Computer Vision 5 Intelligent IoT in Automotive Industry 6 Artificial Intelligence in Intelligent IoT 7 Machine Learning in Intelligent IoT 8 Impact of Neural Networks in Intelligent IoT 8.1 Deep Learning 8.2 Transfer Learning 9 Convolutional Neural Networks for Intelligent IoT 10 Recent Breakthroughs and Techniques 11 Intelligent IoT Reshaping Smartphone Applications 12 Current Challenges and Future Opportunities 12.1 Challenges in Object Identification 12.2 Privacy 12.3 Evidence Collection and Preservation in Forensics 12.4 Future Challenges 13 Conclusion References Smart Security for Industrial and Healthcare IoT Applications 1 Introduction 2 Key Security Goals in IoT 3 Industrial Internet of Things (IIoT) 3.1 Security Issues in IIoT 3.1.1 Security Issues in IIoT Based on Blockchain 3.2 Industry 4.0 3.3 5G-Enabled IIoT 4 IIoT in Healthcare 5 Conclusion References Index