Blockchain technology solutions for the security of IoT-based healthcare systems /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Bhushan, Bharat, 1989- (Editor ), Sharma, Sudhir Kumar (Editor ), Saracevic, Muzafer (Editor ), Boulmakoul, Azedine (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam : Academic Press, 2023.
Colección:Cognitive data science in sustainable computing.
Temas:
Medical informatics > Security measures.
Blockchains (Databases)
Internet of things > Security measures.
M�edecine > Informatique > S�ecurit�e > Mesures.
Cha�ines de blocs.
Internet des objets > S�ecurit�e > Mesures.
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Blockchain Technology Solutions for the Security of IoT-Based Healthcare Systems
  • Copyright
  • Contents
  • Contributors
  • About the editors
  • Preface
  • Chapter 1: Integration of E-health and Internet of Things
  • 1. Introduction
  • 2. Overview of protocol stack in IoT networks
  • 3. Scheduling protocols for best effort IoT networks
  • 4. Routing protocol for best effort IoT networks
  • 4.1. Reactive-based AODV-RPL protocol for deterministic IoT networks
  • 4.2. Overview of AODV-RPL mode of operation (MoP)
  • 4.3. RREQ option
  • 4.4. RREP option
  • 4.5. Gratuitous RREP
  • 5. Experimental results
  • 6. Conclusion
  • References
  • Chapter 2: Industry 4.0 technologies for healthcare: Applications, opportunities, and challenges
  • 1. Introduction
  • 2. Literature review
  • 2.1. ICT for Health 4.0
  • 2.2. IoT in Health 4.0
  • 2.3. Cloud and fog computing in Health 4.0
  • 2.4. Big data in Health 4.0
  • 3. Findings
  • 3.1. Applications for Health 4.0
  • 3.1.1. Health monitoring
  • 3.1.2. Prevention and self-management
  • 3.1.3. Smart medication and monitoring
  • 3.1.4. Precision medicine
  • 3.1.5. Cloud-based health information systems
  • 3.1.6. Telemedicine for disease monitoring
  • 4. Transformation case of the pharmaceutical industry
  • 5. Opportunities and challenges
  • 6. Future insights
  • 7. Conclusions
  • References
  • Chapter 3: Integrated machine learning techniques for preserving privacy in Internet of Things (IoT) systems
  • 1. Introduction
  • 2. IoT and its architecture
  • 2.1. IoT elements
  • 2.1.1. Identification
  • 2.1.2. Communication
  • 2.1.3. Sensing
  • 2.1.4. Computation
  • 2.2. IoT architecture layer
  • 2.2.1. Perception layer
  • 2.2.2. Network layer
  • 2.2.3. Application layer
  • 2.3. IoT application and services
  • 2.3.1. Smart cities
  • 2.3.2. Smart home
  • 2.3.3. Smart transport
  • 2.3.4. Smart healthcare
  • 2.3.5. Smart agriculture
  • 3. IoT and its security
  • 3.1. IoT security requirements
  • 3.2. Security attacks in IoT and their sources
  • 3.2.1. Perception layer attacks
  • 3.2.2. Network layer attacks
  • 3.2.3. Application layer attacks
  • 3.3. IoT issues and challenges
  • 3.4. Motivation for ML in IoT
  • 4. Machine learning algorithms
  • 4.1. Supervised learning
  • 4.1.1. Regression techniques
  • Linear regression
  • Logistic regression
  • Nonlinear regression
  • 4.1.2. Classification techniques
  • Support vector machine (SVM)
  • K nearest neighbor (KNN)
  • Decision trees
  • 4.2. Unsupervised learning
  • 4.2.1. K-means clustering
  • 4.2.2. Fuzzy C-means clustering
  • 4.3. SSL
  • 4.4. Reinforcement learning
  • 5. ML for IoT
  • 5.1. ML for privacy-Protecting applications
  • 5.2. ML-based authentication and access control in IoT
  • 5.3. ML-based attack and mitigation in IoT
  • 5.4. ML-based techniques to address DoS and distributed DoS (DDoS) attacks
  • 5.5. ML-based anomaly or intrusion detection system (IDS) in IoT
  • 5.6. ML-based malware analysis in IoT

Ejemplares similares