Artificial Intelligence for Internet of Things: Design Principle, Modernization, and Techniques

Cover
Half Title
Series Information
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
Chapter 1 Cyber Security Control Systems for Operational Technology
1.1 Introduction
1.2 Operational Technology Security Risk
1.2.1 Today’s Security of Industrial Networks
1.2.2 User Activity Monitoring
1.2.3 Hazard in Reputed Industries
1.2.4 Dynamic Security Battle Space
1.3 Taxonomy of Security Vulnerabilities
1.3.1 Buffer Overflow
1.3.2 Nonsubstantial Input
1.3.3 Race Conditions
1.3.4 Lack of Security Practices
1.3.5 Access Control Problems
1.3.6 Malicious Software
1.3.7 Spyware
1.3.8 Program in Adware
1.3.9 Bot
1.3.10 Ransomware
1.3.11 Scareware
1.3.12 Rootkit
1.3.13 Virus
1.3.14 Trojan Horse
1.3.15 Worms
1.3.16 Man-In-The-Middle [MitM]
1.3.17 Blended Attacks
1.4 Methodology
1.4.1 Stronger Operational Technology (OT) Security
1.4.2 Creating Inventory and Identifying OT Vulnerabilities
1.4.3 Acquiring Automated Threat Intelligence Feeds
1.4.4 Back/restore
1.5 Style of Cyber Security
1.5.1 Security Automation
1.5.2 Breach Detection System (BDS)
1.5.3 Protection of Computing Devices From Intrusion
1.5.3.1 Keep the Firewall-On Condition
1.5.3.2 Antivirus and Antispyware
1.5.3.3 Manage Your Operating System and Browser
1.5.3.4 Protection of Smart Devices
1.5.3.5 Unique Passwords for Each Online Account
1.5.3.6 Detecting Attacks in Real Time
1.5.3.7 Cyber Attacks in Operational Technology
1.6 Avoidance of Threads in Operational Technology
1.6.1 Distributed Denial of Services (DDoS) Attacks and Response
1.6.2 Protecting Against Malware in Operational Technology
1.7 Conclusion
Bibliography
Chapter 2 Cyber Security Ecosystem and Essentials for Modern Technology World
2.1 Introduction
2.1.1 Confidentiality
2.1.2 Integrity
2.1.3 Availability
2.2 Cyber Security Layers, Securing OS, Antiviruses and Malwares
2.2.1 Cyber Security Layers
2.2.2 Securing OS
2.2.3 Antiviruses
2.2.4 Malwares
2.3 Cyber Attacks and Strategies
2.3.1 Security Attacks
2.3.1.1 Passive Attack (Reading of Messages)
2.3.1.2 Passive Attack
2.3.2 Active Attacks
2.3.3 Cyber Security Strategies
2.3.3.1 Creating a Secure Cyber Ecosystem
2.4 Network Security
2.4.1 Network Components
2.4.2 Securing Networks
2.5 Cyber Security Network and Policies
2.5.1 Types of Security Policies
2.5.1.1 Promiscuous Policy
2.5.1.2 Permissive Policy
2.5.1.3 Prudent Policy
2.5.1.4 Paranoid Policy
2.5.2 Examples of Security Policies
2.5.2.1 Access Management Policy
2.5.2.2 Remote-Access Policy
2.5.2.3 Firewall Management Policy
2.5.2.4 Network Connection Policy
2.6 Disaster Recovery Planning
2.6.1 What Is Disaster Recovery Planning (DRR)?
2.6.2 Why Does It Matter?
2.6.3 What You Can Do?
2.7 Conclusion
Bibliography
Chapter 3 Smart Manufacturing in Industry 4.0 Using Computational Intelligence
3.1 Introduction
3.2 Artificial Intelligence
3.3 Neural Networks
3.4 Deep Learning
3.5 Industry 4.0
3.6 Convolutional Neural Networks
3.7 The Evolution of Data-Driven Artificial Intelligence
3.8 LSTM Neural Networks
3.9 Data-Driven Smart Manufacturing
3.9.1 Characteristics of Data-Driven Smart Manufacturing
3.9.2 Data-Driven Smart Manufacturing Application
3.9.3 Smart Design
3.9.4 Intelligent Planning and Process Improvement
3.9.5 Material Distribution and Tracking
3.9.6 Manufacturing Process Monitoring
3.9.7 Control of Product Quality
3.9.8 Smart Equipment Maintenance
3.9.9 Dataset Framework and Detection
References
Chapter 4 Heterogeneous Data Management in IoT-Based Health Care Systems
4.1 Introduction
4.2 Architecture of Typical Health Care System
4.3 Data Preprocessing and Storage
4.3.1 Data Cleaning
4.3.2 Missing Values
4.3.3 Noise Removal
4.3.4 Data Integration
4.3.5 Feature Or Data Reduction
4.3.6 Data Transformation and Discretisation
4.3.7 Data Discretisation
4.3.8 Data Storage
4.3.9 Data Imbalance in Health Care
4.4 Big Data Processing in Health Care
4.5 Algorithms for Intelligent Decisions
4.6 Challenges and Scope for Further Research
4.7 Discussion and Conclusion
Bibliography
Chapter 5 Comparative Study On SMS Spam Message Detection With Different Machine Learning Methods for Safety Communication
5.1 Introduction
5.2 Literature Survey
5.3 Methodology
5.3.1 Preprocessing
5.3.1.1 Data Preparation
5.3.2 Feature Extraction
5.3.3 Training and Testing the Model
5.3.4 Comparison and Analysis of Algorithms
5.3.5 Flow Diagram
5.4 Results and Discussion
5.4.1 Dataset
5.4.2 Performance Measure
5.5 Conclusion
Bibliography
Chapter 6 An Optimal More Than One Stage (MTOS) Authentication Model to Ensure Security in Cloud Computing
6.1 Introduction and Related Works
6.2 More Than One Stage Security
6.3 More Than One Authentication Model
6.4 The Proposed More Than One Stage (MTOS) Authentication Model for Cloud Computing
6.5 Results and Discussions
6.6 Conclusion
Bibliography
Chapter 7 Robotic Harvesters for Strawberry and Apple
7.1 Introduction
7.2 Problems in the Manual Harvesting of Apple and Strawberry
7.3 Challenges for Fruit Harvesting Robot
7.4 Strawberry Harvesting Robots
7.4.1 Components of a Strawberry Harvester
7.4.2 Operational Flow of Harvesting
7.4.3 Classification of Strawberries
7.4.3.1 General Flow of Harvesting
7.4.4 Maturity Calculation
7.4.5 Success Rate of Peduncle Recognition
7.5 Apple Harvesting Robot
7.5.1 Major Components of an Apple Harvesting Robot
7.5.2 Apple Recognition Tests in Different Conditions
7.6 Conclusion
Bibliography
Chapter 8 Scalability and Security Requirements for the Internet of Things Architecture
8.1 Introduction
8.1.1 Importance of Scalability
8.1.2 Vertical Scalability
8.1.3 Horizontal Scalability
8.2 Scalability Features
8.2.1 Business
8.2.2 Marketing
8.2.3 Software
8.2.4 Hardware
8.2.5 Networks
8.3 Techniques for Scalability
8.3.1 Automated Bootstrapping
8.3.2 Controlling the IoT Data Pipeline
8.3.3 Applying the Three-Axis Approach for Scaling
8.3.4 Developing Microservices Architecture
8.3.5 Adopting Multiple Data Storage Technologies
8.3.6 System Expansion
8.4 Scalability Challenges and Issues
8.4.1 Protocol and Network Security
8.4.2 Identity Management
8.4.3 Privacy
8.4.4 Trust and Governance
8.4.5 Fault Tolerance
8.4.6 Access Control
8.4.7 Big Data and Knowledge
8.5 Scalable IoT Architecture
8.5.1 Layers of IoT Architecture
8.5.2 Network
8.5.3 Application
8.5.4 Stages of IoT Architecture
8.5.5 Connected Objects
8.5.6 Edge IT Systems
8.5.7 Data Centres and Cloud Storage
8.6 IoT Architecture in Business Software
8.6.1 Basic Elements of IoT Architecture
8.7 IoT Building Blocks and Architecture
8.7.1 Things
8.7.2 Physical World
8.7.3 Virtual World
8.7.4 Communication Network
8.8 IoT System Architecture
8.8.1 IoT Devices
8.8.2 Sensing and Actuating Devices
8.8.3 Role of Gateways
8.8.4 Communication Gateway
8.8.5 Communication Network
8.8.6 Cloud Server
8.8.7 IoT Application
8.9 Scalable and Secure IoT
8.9.1 IoT Connectivity Technologies
8.9.2 Authentication Methods of IoT Devices
8.9.2.1 Identity- Or Password-Based Authentication
8.9.2.2 MAC Address-Based Authentication
8.9.2.3 Encryption-Based Authentication
8.9.2.4 One-Time Password (OTP) Based Authentication
8.9.3 IoT Authentication Limitations
8.9.4 Scalable and Secure IoT
8.10 Scalability Performance On a Large-Scale Network
8.10.1 Performance Evaluation
8.10.2 Evaluation Method
8.10.3 Evaluation Results and Analysis
8.11 IoT Applications and Transformations
8.11.1 IoT Applications
8.11.2 Health Care Systems
8.11.3 Smart Homes and Buildings
8.11.4 Smart Transportation
8.11.5 Smart Grid
8.11.6 Smart City
8.12 Threats and Attacks
8.12.1 Category
8.12.2 Communications
8.12.3 Device/services
8.13 Conclusion
Bibliography
Chapter 9 Applying Fuzzy Logics to Detect Reliable Sensors On IoT
9.1 Introduction
9.1.1 Clustered Architecture
9.1.2 Flat Network
9.2 Related Work
9.3 Energy Loss
9.4 Proposed Methodology
9.4.1 Fuzzy Logics
9.4.1.1 Disjunction Union Model
9.5 Conclusion
References
Chapter 10 Data Analytics Techniques and Tools in Smart City Applications
10.1 Introduction
10.2 Steps of Data Analytics
10.2.1 Identify
10.2.2 Collect
10.2.3 Clean
10.2.4 Analyze
10.2.5 Interpret
10.3 Types of Data Analytics
10.4 Smart City
10.4.1 The Architecture of Smart City
10.4.1.1 Sensing Layer
10.4.1.2 Transmission Layer
10.4.1.3 Data Management
10.4.1.4 Application Layer
10.5 Characteristics of a Smart City
10.6 Applications of Smart City
10.6.1 Smart Community
10.6.2 Smart Transportation
10.6.3 Smart Healthcare
10.6.4 Smart Energy
10.7 Data Analysis Techniques for Smart City Applications
10.7.1 Cluster Analysis
10.7.2 Cohort Analysis
10.7.3 Regression Analysis
10.7.4 Neural Networks
10.7.5 Factor Analysis
10.7.6 Time-Series Analysis
10.7.7 Sentiment Analysis
10.8 Data Analytics Tools
10.8.1 R Programming Language
10.8.2 Python
10.8.3 Spark
10.8.4 Tableau
10.8.5 Konstanz Information Miner (KNIME)
10.8.6 Rapid Miner
10.8.7 Tensorflow
10.9 Challenges of Data Analytics in Smart City Applications
10.10 Conclusions
References
Chapter 11 Machine Learning Algorithms for IoT Applications
11.1 Introduction
11.2 History of IoT
11.3 The Architecture of IoT Networks
11.4 Applications of IoT Networks
11.4.1 Smart Home
11.4.2 Health Care
11.4.3 Agriculture
11.4.4 Smart City
11.4.5 Smart Grid
11.4.6 Smart Car
11.4.7 Wearable Devices
11.4.8 Industrial IoT (IIoT)
11.5 Challenges of IoT
11.5.1 Networking
11.5.2 Big Data
11.5.3 Scalability
11.5.4 Interoperability
11.5.5 Heterogeneity
11.5.6 Security and Privacy
11.6 Machine Learning
11.6.1 Supervised Learning
11.6.2 Unsupervised Learning
11.6.3 Semisupervised Learning
11.6.4 Reinforcement Learning
11.7 Role of Machine Learning in IoT Applications
11.8 Machine Learning Algorithms for IoT Applications
11.8.1 Logistic Regressions
11.8.2 Decision Tree
11.8.3 Random Forest
11.8.4 Support Vector Machine (SVM)
11.8.5 Naïve Bayes
11.8.6 K-Nearest Neighbor (KNN)
11.8.7 K-Means Algorithm
11.8.8 Artificial Neural Networks
11.8.8.1 Back Propagation Neural Network (BPNN)
11.8.8.2 Radial Basis Functions (RBF)
11.8.9 Deep Learning Methods
11.8.9.1 Deep Neural Networks
11.8.9.2 Convolutional Neural Networks
11.8.9.3 Recurrent Neural Networks
11.8.9.4 Reinforcement Learning
11.9 Conclusions
References
Chapter 12 Application of IoT in Smart Cities
12.1 Introduction
12.2 Internet of Things (IoT) Approach to Smart City
12.2.1 Radiofrequency Identification
12.2.2 Near-Field Communication
12.2.3 LWPN Like ZigBee
12.2.4 Wireless Sensor Networks (WSNs)
12.2.5 Dash7
12.2.6 3G and Long-Term Evolution (LTE)
12.2.7 Middleware
12.3 Application of IoT in Smart Cities
12.3.1 IoT in Smart Energy Management
12.3.1.1 How Does Smart Energy Management Work?
12.3.2 IoT in Smart Waste Management
12.3.2.1 How Does Smart Waste Management Work?
12.3.3 IoT in Smart Transportation
12.3.3.1 How Does Smart Traffic Management System Work?
12.3.4 IoT in Smart Health Management System
12.3.4.1 How Does IoT-Based Smart Health Management Work?
12.3.5 IoT in Smart Surveillance
12.3.5.1 How Does IoT-Based Smart Surveillance Work?
12.3.6 IoT-Enabled Smart Homes
12.3.7 IoT in Miscellaneous Applications for Converting Urban Cities Into Smart Ones
12.3.7.1 Smart Air Quality Monitors
12.3.8 Smart Weather Monitoring
12.3.9 Smart Water System
12.3.10 Smart Parking System
12.4 Challenges for Implementing IoT-Based Smart Cities
12.5 Future of IoT-Based Smart Cities
12.6 Conclusion
Bibliography
Chapter 13 Novel Vocal Biomarker-Based Biosignal Acquisition, Analysis and Diagnosis System
13.1 Introduction
13.2 Experimental Analysis
13.3 Results and Discussion
13.4 Use of Biomedical Signals for Authentication
13.5 Benefits of Using Bioelectrical Signals as Biometrics
13.5.1 Use of Biomedical Signals in Multimodal Biometric Systems for High-Security Applications
13.6 Recent Advances and Applications in Medical Imaging Techniques
13.6.1 Applications Advances in Medical Imaging Techniques
13.6.1.1 X-Ray Radiography
13.6.1.2 X-Ray Computed Tomography
13.6.1.3 Wavelet Transform Technology in Medical Imaging
13.7 Applications of Wavelet Transform in Medical Image Processing
13.7.1 Segmentation of Medical Images for Best Diagnosis
13.7.2 LEGION Model
13.7.3 Segmentation Algorithm
13.7.4 Superimposing a Medical Image Within the Subject
13.7.5 Medical Image Repository and Image Categorization
13.7.5.1 Medical Image Categorization Using a Texture-Based Symbolic Description
13.7.6 Web-Based Interactive Applications of High-Resolution 3D Medicinal Image Data
13.7.7 Data Access Optimization
13.8 Conclusion
Bibliography
Chapter 14 Leveraging Health Care Industry Through Medical IoT: Its Implementation and Case Studies Within India
14.1 Introduction
14.1.1 Challenges and Opportunities
14.1.2 Present and Future Investments in Digital Health
14.2 Functional Framework of the Medical IoT Or Health Care IoT
14.2.1 Technologies Involved in Building the Framework of Medical IoT
14.2.1.1 Identification Technologies and Their Standards
14.2.1.2 Communication Technologies and Their Protocols
14.2.1.3 Location Technology
14.3 Applications of IoT in the Health Care Sector
14.3.1 Remote Monitoring of the Patients By Medical Practitioners
14.3.2 IoT in Vaccine Manufacturing and Supply Chain Management
14.3.3 Evolution of IoT in Wearable Technology
14.3.4 IoT in Surgery: a Digital Revolution
14.3.5 IoT Technology for Alzheimer’s, Parkinson and Dementia Patients
14.4 Successful Case Studies of Medical IoT
14.4.1 Apple Watch Series’ ECG Feature Saved Human Life
14.4.2 Telemedicine Platforms to Access Health Care for All People
14.4.3 IoT in Robotic-Assisted Surgery
14.5 Conclusion
References
Chapter 15 Energy-Efficient Architectures for IoT Applications
15.1 Introduction
15.2 Role of Sensor Nodes and Relay Nodes
15.3 Hierarchical Node Placement
15.4 Routing Mechanism
15.5 Energy Storage Through IoT Energy-Efficient Architecture
15.5.1 Concepts
15.5.2 IoT and Energy Generation
15.5.3 Smart Cities
15.5.4 Smart Grid
15.5.5 Smart Building
15.5.6 Smart Use of Energy in Industry
15.5.7 Intelligent Transportation
15.5.8 Challenges of Applying IoT
15.6 Conclusion
Chapter 16 Exploring Robotics Technology for Health Care Applications
16.1 Introduction
16.2 Benefits of Robotics in Health Care
16.3 Classification of Robots Utilized in Health Care
16.3.1 Health Care Robot
16.3.2 Surgical Robot
16.3.3 Rehabilitation Robot
16.3.4 Wearable Robot
16.3.5 Robots for Care
16.4 Conclusion
References
Chapter 17 Design Principles, Modernization and Techniques in Artificial Intelligence for IoT: Advanced Technologies, …
17.1 Introduction
17.1.1 Internet of Things
17.1.2 Artificial Intelligence
17.2 The Industrial Revolution
17.2.1 The First Industrial Revolution
17.2.2 The Second Industrial Revolution
17.2.3 The Third Industrial Revolution
17.2.4 The Fourth Industrial Revolution
17.3 Industry 4.0 Explained
17.4 The Building Blocks of Industry 4.0
17.5 Six Design Principles of Industry 4.0
17.6 Industrial IoT
17.6.1 What Is the Industrial Internet of Things?
17.7 Essential IIoT Terms and Concepts
17.8 The Smart Factory – Built On Industrial IoT
17.9 How Is the Industry 4.0 Maturity Assessed?
17.9.1 Industry 4.0 Maturity
17.10 Industry 4.0 in Medical Systems
17.11 Revolutions Are Disruptive – Advantages and Disadvantages of Industrial Revolution
Bibliography
Index