Machine learning approaches for convergence of IoT and blockchain

Detalles Bibliográficos
Otros Autores: Singh, Akansha, editor (editor), Singh, Krishna Kant, editor, Sharma, Sanjay, Dr., editor
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, New Jersey : Wiley-Scrivener [2021]
Materias:
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631652406719
Tabla de Contenidos:
  • Cover
  • Half-Title Page
  • Series Page
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • 1 Blockchain and Internet of Things Across Industries
  • 1.1 Introduction
  • 1.2 Insight About Industry
  • 1.2.1 Agriculture Industry
  • 1.2.2 Manufacturing Industry
  • 1.2.3 Food Production Industry
  • 1.2.4 Healthcare Industry
  • 1.2.5 Military
  • 1.2.6 IT Industry
  • 1.3 What is Blockchain?
  • 1.4 What is IoT?
  • 1.5 Combining IoT and Blockchain
  • 1.5.1 Agriculture Industry
  • 1.5.2 Manufacturing Industry
  • 1.5.3 Food Processing Industry
  • 1.5.4 Healthcare Industry
  • 1.5.5 Military
  • 1.5.6 Information Technology Industry
  • 1.6 Observing Economic Growth and Technology's Impact
  • 1.7 Applications of IoT and Blockchain Beyond Industries
  • 1.8 Conclusion
  • References
  • 2 Layered Safety Model for IoT Services Through Blockchain
  • 2.1 Introduction
  • 2.1.1 IoT Factors Impacting Security
  • 2.2 IoT Applications
  • 2.3 IoT Model With Communication Parameters
  • 2.3.1 RFID (Radio Frequency Identification)
  • 2.3.2 WSH (Wireless Sensor Network)
  • 2.3.3 Middleware (Software and Hardware)
  • 2.3.4 Computing Service (Cloud)
  • 2.3.5 IoT Software
  • 2.4 Security and Privacy in IoT Services
  • 2.5 Blockchain Usages in IoT
  • 2.6 Blockchain Model With Cryptography
  • 2.6.1 Variations of Blockchain
  • 2.7 Solution to IoT Through Blockchain
  • 2.8 Conclusion
  • References
  • 3 Internet of Things Security Using AI and Blockchain
  • 3.1 Introduction
  • 3.2 IoT and Its Application
  • 3.3 Most Popular IoT and Their Uses
  • 3.4 Use of IoT in Security
  • 3.5 What is AI?
  • 3.6 Applications of AI
  • 3.7 AI and Security
  • 3.8 Advantages of AI
  • 3.9 Timeline of Blockchain
  • 3.10 Types of Blockchain
  • 3.11 Working of Blockchain
  • 3.12 Advantages of Blockchain Technology
  • 3.13 Using Blockchain Technology With IoT
  • 3.14 IoT Security Using AI and Blockchain.
  • 3.15 AI Integrated IoT Home Monitoring System
  • 3.16 Smart Homes With the Concept of Blockchain and AI
  • 3.17 Smart Sensors
  • 3.18 Authentication Using Blockchain
  • 3.19 Banking Transactions Using Blockchain
  • 3.20 Security Camera
  • 3.21 Other Ways to Fight Cyber Attacks
  • 3.22 Statistics on Cyber Attacks
  • 3.23 Conclusion
  • References
  • 4 Amalgamation of IoT, ML, and Blockchain in the Healthcare Regime
  • 4.1 Introduction
  • 4.2 What is Internet of Things?
  • 4.2.1 Internet of Medical Things
  • 4.2.2 Challenges of the IoMT
  • 4.2.3 Use of IoT in Alzheimer Disease
  • 4.3 Machine Learning
  • 4.3.1 Case 1: Multilayer Perceptron Network
  • 4.3.2 Case 2: Vector Support Machine
  • 4.3.3 Applications of the Deep Learning in the Healthcare Sector
  • 4.4 Role of the Blockchain in the Healthcare Field
  • 4.4.1 What is Blockchain Technology?
  • 4.4.2 Paradigm Shift in the Security of Healthcare Data Through Blockchain
  • 4.5 Conclusion
  • References
  • 5 Application of Machine Learning and IoT for Smart Cities
  • 5.1 Functionality of Image Analytics
  • 5.2 Issues Related to Security and Privacy in IoT
  • 5.3 Machine Learning Algorithms and Blockchain Methodologies
  • 5.3.1 Intrusion Detection System
  • 5.3.2 Deep Learning and Machine Learning Models
  • 5.3.3 Artificial Neural Networks
  • 5.3.4 Hybrid Approaches
  • 5.3.5 Review and Taxonomy of Machine Learning
  • 5.4 Machine Learning Open Source Tools for Big Data
  • 5.5 Approaches and Challenges of Machine Learning Algorithms in Big Data
  • 5.6 Conclusion
  • References
  • 6 Machine Learning Applications for IoT Healthcare
  • 6.1 Introduction
  • 6.2 Machine Learning
  • 6.2.1 Types of Machine Learning Techniques
  • 6.2.2 Applications of Machine Learning
  • 6.3 IoT in Healthcare
  • 6.3.1 IoT Architecture for Healthcare System
  • 6.4 Machine Learning and IoT.
  • 6.4.1 Application of ML and IoT in Healthcare
  • 6.5 Conclusion
  • References
  • 7 Blockchain for Vehicular Ad Hoc Network and Intelligent Transportation System: A Comprehensive Study
  • 7.1 Introduction
  • 7.2 Related Work
  • 7.3 Connected Vehicles and Intelligent Transportation System
  • 7.3.1 VANET
  • 7.3.2 Blockchain Technology and VANET
  • 7.4 An ITS-Oriented Blockchain Model
  • 7.5 Need of Blockchain
  • 7.5.1 Food Track and Trace
  • 7.5.2 Electric Vehicle Recharging
  • 7.5.3 Smart City and Smart Vehicles
  • 7.6 Implementation of Blockchain Supported Intelligent Vehicles
  • 7.7 Conclusion
  • 7.8 Future Scope
  • References
  • 8 Applications of Image Processing in Teleradiology for the Medical Data Analysis and Transfer Based on IOT
  • 8.1 Introduction
  • 8.2 Pre-Processing
  • 8.2.1 Principle of Diffusion Filtering
  • 8.3 Improved FCM Based on Crow Search Optimization
  • 8.4 Prediction-Based Lossless Compression Model
  • 8.5 Results and Discussion
  • 8.6 Conclusion
  • Acknowledgment
  • References
  • 9 Innovative Ideas to Build Smart Cities with the Help of Machine and Deep Learning and IoT
  • 9.1 Introduction
  • 9.2 Related Work
  • 9.3 What Makes Smart Cities Smart?
  • 9.3.1 Intense Traffic Management
  • 9.3.2 Smart Parking
  • 9.3.3 Smart Waste Administration
  • 9.3.4 Smart Policing
  • 9.3.5 Shrewd Lighting
  • 9.3.6 Smart Power
  • 9.4 In Healthcare System
  • 9.5 In Homes
  • 9.6 In Aviation
  • 9.7 In Solving Social Problems
  • 9.8 Uses of AI-People
  • 9.8.1 Google Maps
  • 9.8.2 Ridesharing
  • 9.8.3 Voice-to-Text
  • 9.8.4 Individual Assistant
  • 9.9 Difficulties and Profit
  • 9.10 Innovations in Smart Cities
  • 9.11 Beyond Humans Focus
  • 9.12 Illustrative Arrangement
  • 9.13 Smart Cities with No Differentiation
  • 9.14 Smart City and AI
  • 9.15 Further Associated Technologies
  • 9.15.1 Model Identification
  • 9.15.2 Picture Recognition.
  • 9.15.3 IoT
  • 9.15.4 Big Data
  • 9.15.5 Deep Learning
  • 9.16 Challenges and Issues
  • 9.16.1 Profound Learning Models
  • 9.16.2 Deep Learning Paradigms
  • 9.16.3 Confidentiality
  • 9.16.4 Information Synthesis
  • 9.16.5 Distributed Intelligence
  • 9.16.6 Restrictions of Deep Learning
  • 9.17 Conclusion and Future Scope
  • References
  • Index
  • EULA.