Blockchain Applications for Secure IoT Frameworks

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Sharma, Sudhir K.
Otros Autores: Bhushan, Bharat, Astya, Parma N.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Sharjah : Bentham Science Publishers, 2021.
Colección:Advances in Computing Communications and Informatics Ser.
Temas:
Internet of things.
Blockchains (Databases)
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Title
  • Copyright
  • End User License Agreement
  • Contents
  • Preface
  • List of Contributors
  • An Overview of Smart Grid in the Current Age
  • Reinaldo Padilha França1,*, Ana Carolina Borges Monteiro1,*, Rangel Arthur2 and Yuzo Iano1
  • 1. INTRODUCTION
  • 2. SMART CITIES CONCEPTS
  • 3. SMART GRID CONCEPTS
  • 3.1. Smart Grid and IoT
  • 3.2. Smart Grid Applications
  • 3.3. Smart Grid Advantages
  • 4. SMART GRID INFRASTRUCTURE
  • 5. DISCUSSION
  • 5.1. Infrastructure generated by Smart Grid
  • 5.2. Smart Grid as an Instrument of Innovation
  • 5.3. Smart Grid Benefits
  • 5.4. Blockchain for Smart Grids
  • CONCLUSION
  • TRENDS
  • CONSENT FOR PUBLICATION
  • CONFLICT OF INTEREST
  • ACKNOWLEDGEMENT
  • REFERENCES
  • Dynamic Strategies of Machine Learning for Extenuation of Security Breaches in Wireless Sensor Networks
  • Shweta Paliwal1,*
  • 1. INTRODUCTION
  • 2. SECURITY CONCERNS IN WIRELESS SENSOR NETWORKS
  • 2.1. Eavesdropping Attack
  • 2.2. Jamming Attack
  • 2.3. Tampering
  • 2.4. Exhaustion and Collision Attack
  • 2.5. Sybil Attack
  • 2.6. Blackhole Attack
  • 2.7. Wormhole Attack
  • 2.8. Grayhole Attack
  • 2.9. Sinkhole Attack
  • 2.10. Hello Flood Attack
  • 3. MACHINE LEARNING EMERGING AS A SAFEGUARD TO WSNS
  • 4. SUPERVISED LEARNING
  • 4.1. Linear Regression and Logistic Regression
  • 4.2. Artificial Neural Networks (ANN)
  • 4.3. Decision Tree
  • 4.4. Random Forest
  • 4.5. Bayesian Learning
  • 4.6. Support Vector Machine (SVM)
  • 4.7. K- Nearest Neighbor (K-NN)
  • 5. UNSUPERVISED LEARNING
  • 5.1. K- Means Clustering
  • 5.2. Fuzzy C- Means Clustering
  • 6. SEMI-SUPERVISED LEARNING
  • 7. REINFORCEMENT LEARNING
  • 8. MACHINE LEARNING ADDRESSING ISSUES IN WSNS
  • 8.1. Machine Learning Addressing Issue of Security
  • 8.2. Machine Learning Addressing Issue of Routing in Wireless Sensor Network
  • 8.3. Data Aggregation in WSNs with Machine Learning
  • 9. PROPOSED FEATURE SELECTION METHOD AND COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS
  • 9.1. Dataset Gathering
  • 9.2. Feature Selection Methodology
  • 9.2.1. Correlation-based Feature
  • 9.2.2. Info Gain Method
  • 9.2.3. CFS Subset Evaluation
  • 10. EXPERIMENTAL RESULTS AND ANALYSIS
  • CONCLUSION AND FUTURE WORK
  • CONSENT FOR PUBLICATION
  • CONFLICT OF INTEREST
  • ACKNOWLEDGEMENT
  • REFERENCES
  • IoT- Fundamentals and Challenges
  • Mohammad Maksuf Ul Haque1, Shazmeen Shamsi1, Khwaja M. Rafi2 and Mohammad Sufian Badar3,4,*
  • 1. INTRODUCTION
  • 2. HISTORY OF IOT
  • 2.1. Realizing the Concept
  • 3. INTERNET OF THINGS
  • 3.1. Meaning of IoT
  • 3.2. Importance of IoT
  • 3.2.1. Things that Collect and Send Information
  • 3.2.2. Things that Receive Information and then Act on it
  • 3.2.3. Things that Can Do Both
  • 3.3. Scope of IoT: Applications and Examples
  • 3.3.1. Increasing Efficiency
  • 3.3.2. Improved Health and Safety
  • 3.3.3. Enhancing Experience
  • 4. FUNDAMENTALS OF IOT
  • 4.1. IoT Device Architecture

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