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Convergence of Deep Learning in Cyber-IoT Systems and Security
| Convergence of Deep Learning in Cyber-IoT Systems and Security |
| Autore | Chakraborty Rajdeep |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (472 pages) |
| Altri autori (Persone) |
GhoshAnupam
MandalJyotsna Kumar BalamuruganS |
| Collana | Artificial Intelligence and Soft Computing for Industrial Transformation Ser. |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-85768-6
1-119-85767-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910632496103321 |
Chakraborty Rajdeep
|
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| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Fog Computing for Intelligent Cloud IoT Systems
| Fog Computing for Intelligent Cloud IoT Systems |
| Autore | Banerjee Chandan |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (453 pages) |
| Disciplina | 004.67/82 |
| Altri autori (Persone) |
GhoshAnupam
ChakrabortyRajdeep ElngarAhmed A |
| Collana | Advances in Learning Analytics for Intelligent Cloud-IoT Systems Series |
| Soggetto topico |
Internet of things
Cloud computing |
| ISBN |
9781394175345
1394175345 9781394175338 1394175337 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: Study of Fog Computing and Machine Learning -- Chapter 1 Fog Computing: Architecture and Application -- 1.1 Introduction -- 1.2 Fog Computing: An Overview -- 1.3 Fog Computing for Intelligent Cloud-IoT System -- 1.4 Fog Computing Architecture -- 1.5 Basic Modules of Fog Computing -- 1.6 Cloud Computing vs. Fog Computing -- 1.7 Fog Computing vs. IoT -- 1.8 Applications of Fog Computing -- 1.9 Will the Fog Be Taken Over by the Cloud? -- 1.10 Challenges in Fog Computing -- 1.11 Future of Fog Computing -- 1.12 Conclusion -- References -- Chapter 2 A Comparative Review on Different Techniques of Computation Offloading in Mobile Cloud Computing -- 2.1 Introduction -- 2.2 Related Works -- 2.3 Computation Offloading Techniques -- 2.3.1 MAUI Architecture -- 2.3.2 Clone-Cloud Based Model -- 2.3.3 Cuckoo Design -- 2.3.4 MACS Architecture -- 2.3.5 AHP and TOPSIS Design Technique -- 2.3.6 Energy Aware Design for Workflows -- 2.3.7 MCSOS Architecture -- 2.3.8 Cloudlet -- 2.3.9 Jade -- 2.3.10 Phone2Cloud -- 2.4 Conclusion -- 2.5 Future Scope -- 2.6 Acknowledgement -- References -- Chapter 3 Fog Computing for Intelligent Cloud-IoT System: Optimization of Fog Computing in Industry 4.0 -- 3.1 Introduction -- 3.1.1 Industry 4.0 -- 3.1.2 Fog Computing -- 3.1.3 Fog Nodes -- 3.2 How Fog Computing with IIoT Brings Revolution -- 3.2.1 Hierarchical Fog Computing Architecture -- 3.2.2 Layered Fog Computing Architecture -- 3.3 Applications of Fog Computing on Which Industries Rely -- 3.3.1 In the Field of Agriculture -- 3.3.2 In Healthcare Industry -- 3.3.3 In Smart Cities -- 3.3.4 In Education -- 3.3.5 In Entertainment -- 3.4 Data Analysis -- 3.5 Illustration of Fog Computing and Application -- 3.5.1 Figures -- 3.6 Conclusion -- 3.7 Future Scope/Acknowledgement -- References.
Chapter 4 Machine Learning Integration in Agriculture Domain: Concepts and Applications -- 4.1 Introduction -- 4.2 Fog Computing in Agriculture -- 4.2.1 Smart Farming -- 4.3 Methodology -- 4.3.1 Data Source -- 4.3.2 Data Analysis and Pre-Processing -- 4.3.3 Feature Extraction -- 4.3.4 Model Selection -- 4.3.5 Hyper-Parameter Tuning -- 4.3.6 Train-Test Split -- 4.4 Results and Discussion -- 4.4.1 Modeling Algorithms -- 4.5 Conclusion -- 4.6 Future Scope -- References -- Chapter 5 Role of Intelligent IoT Applications in Fog Computing -- 5.1 Introduction -- 5.1.1 PaaS/SaaS Platforms Have Various Benefits That are Crucial to the Success of Many Small IoT Startup Businesses -- 5.2 Cloud Service Model's Drawbacks -- 5.3 Fog Computation -- 5.3.1 Standardization -- 5.3.2 Growing Use Cases for Fog Computing -- 5.3.3 IoT Applications with Intelligence -- 5.3.4 Graphics Processing Units -- 5.4 Recompenses of FoG -- 5.5 Limitation of Fog Computing -- 5.6 Fog Computing with IoT -- 5.6.1 Benefits of Fog Computing with IoT -- 5.6.2 Challenges of Fog Computing with IoT -- 5.7 Edge AI Embedded -- 5.7.1 Key Software Characteristics in Fog Computing -- 5.7.2 Fog Cluster Management -- 5.7.3 Technology for Computing in the Fog -- 5.7.4 Concentrating Intelligence -- 5.7.5 Device-Driven Intelligence -- 5.8 Network Intelligence Objectives -- 5.9 Farming with Fog Computation (Case Study) -- 5.10 Conclusion -- References -- Chapter 6 SaaS-Based Data Visualization Platform-A Study in COVID-19 Perspective -- 6.1 Introduction -- 6.1.1 Motivation and the Problem of Interest -- 6.2 Summary of Objectives -- 6.3 What is a Pandemic? -- 6.4 COVID-19 and Information Gap -- 6.5 Data Visualization and its Importance -- 6.6 Data Management with Data Visualization -- 6.7 What is Power BI? -- 6.7.1 Data Collection & -- Wrangling -- 6.7.2 Data Description & -- Source. 6.7.3 Data Transformation -- 6.8 Output Data -- 6.9 Design & -- Implementation -- 6.9.1 Integration Design -- 6.9.2 High-Level Process Flow -- 6.9.3 Solution Flow -- 6.10 Dashboard Development -- 6.10.1 Landing Page -- 6.10.2 Approach and Design -- 6.10.3 Helpline Information -- 6.10.3.1 Approach and Design -- 6.10.4 Symptom Detection -- 6.10.4.1 Approach and Design -- 6.10.5 Testing Lab Information -- 6.10.5.1 Approach and Design -- 6.10.6 Hospital Information -- 6.10.6.1 Approach and Design -- 6.10.7 Oxygen Suppliers Information -- 6.10.7.1 Approach and Design -- 6.10.8 COVID Cases Information -- 6.10.8.1 Approach and Design -- 6.10.9 Vaccination Information -- 6.10.9.1 Approach and Design -- 6.10.10 Patients' Information -- 6.10.10.1 Approach and Design -- 6.11 Advantages and its Impact -- 6.12 Conclusion and Future Scope -- References -- Chapter 7 A Complete Study on Machine Learning Algorithms for Medical Data Analysis -- 7.1 Introduction -- 7.1.1 Importance of Machine Learning Algorithms in Medical Data Analysis -- 7.2 Pre-Processing Medical Data for Machine Learning -- 7.3 Supervised Learning Algorithms for Medical Data Analysis -- 7.3.1 Linear Regression Algorithm -- 7.3.2 Logistic Regression Algorithm -- 7.3.3 Decision Trees Algorithm -- 7.3.3.1 Advantages of Decision Tree Algorithm -- 7.3.3.2 Limitations of Decision Tree Algorithm -- 7.3.4 Random Forest Algorithm -- 7.3.4.1 Advantages of Random Forest Algorithm -- 7.3.4.2 Limitations of Random Forest Algorithm -- 7.3.4.3 Applications of Random Forest Algorithm in Medical Data Analysis -- 7.3.5 Support Vector Machine Algorithm -- 7.3.5.1 Advantages of SVM Algorithm -- 7.3.5.2 Limitations of SVM Algorithm -- 7.3.5.3 Applications of SVM Algorithm in Medical Data Analysis -- 7.3.6 Naive Bayes Algorithm -- 7.3.7 KNN (K-Nearest Neighbor Algorithm) -- 7.3.7.1 Applications of K-NN Algorithm. 7.3.8 Deep Learning Algorithm -- 7.3.9 Deep Learning Application -- 7.4 Unsupervised Learning Algorithms for Medical Data Analysis -- 7.4.1 Clustering Algorithm -- 7.4.2 Principal Component Analysis Algorithm -- 7.4.3 Independent Component Analysis Algorithm -- 7.4.4 Association Rule Mining Algorithm -- 7.5 Applications of Machine-Learning Algorithms in Medical Data Analysis -- 7.6 Limitations and Challenges of Machine Learning Algorithms in Medical Data Analysis -- 7.7 Future Research Directions and Machine Learning Developments in the Realm of Medical Data Analysis -- 7.8 Conclusion -- References -- Part II: Applications and Analytics -- Chapter 8 Fog Computing in Healthcare: Application Taxonomy, Challenges and Opportunities -- 8.1 Introduction -- 8.2 Research Methodology -- 8.3 Application Taxonomy in FC-Based Healthcare -- 8.3.1 Diagnosis -- 8.3.2 Monitoring -- 8.3.3 Notification -- 8.3.4 Zest of Applications of FC in Healthcare -- 8.4 Challenges in FC-Based Healthcare -- 8.4.1 QoS Optimization -- 8.4.2 Patient Authentication and Access Control -- 8.4.3 Data Processing -- 8.4.4 Data Privacy Preservation -- 8.4.5 Energy Efficiency -- 8.5 Research Opportunities -- 8.5.1 Research Opportunity in Computing -- 8.5.2 Research Opportunity in Security -- 8.5.3 Research Opportunity in Services -- 8.5.4 Research Opportunity in Implementation -- 8.6 Conclusion -- References -- Chapter 9 IoT-Driven Predictive Maintenance Approach in Industry 4.0: A Fiber Bragg Grating (FBG) Sensor Application -- 9.1 Introduction -- 9.2 Review of Related Research Articles -- 9.2.1 Studies on FBG Sensors and Their Role in Industry 4.0 -- 9.2.1.1 Magnetostrictive Material -- 9.2.1.2 Magneto-Optical (MO) Materials -- 9.2.1.3 Magnetic Fluid (MF) Materials -- 9.2.1.4 Magnetically Sensitive Materials and Their Application -- 9.2.1.5 Optical Fiber Current Sensors. 9.3 Research Gaps -- 9.4 Emerging Research Directions -- 9.5 The Broad Concept of FBG Sensor Applications in Industry 4.0 -- 9.6 Conclusion -- References -- Chapter 10 Fog Computing-Enabled Cancer Cell Detection System Using Convolution Neural Network in Internet of Medical Things -- 10.1 Introduction -- 10.2 Fog Computing: Approach of IoMT -- 10.3 Relationship Between IoMT and Deep Neural Network -- 10.4 Fog Computing Enabled CNN for Medical Imaging -- 10.5 Algorithm Approach of Proposed Model -- 10.6 Result and Analysis -- 10.7 Conclusion -- References -- Chapter 11 Application of IoT in Smart Farming and Precision Farming: A Review -- 11.1 Introduction -- 11.2 Methodologies Used in Precision Agriculture -- 11.3 Contribution of IoT in Agriculture -- 11.4 IoT Enabled Smart Farming -- 11.5 IoT Enabled Precision Farming -- 11.6 Machine Learning Enable Precision Farming -- 11.7 Application of Operational Research Method in Farming System -- 11.8 Conclusion -- 11.9 Future Scope -- References -- Chapter 12 Big IoT Data Analytics in Fog Computing -- 12.1 Introduction -- 12.2 Literature Review -- 12.3 Motivation -- 12.4 Fog Computing -- 12.4.1 Fog Node -- 12.4.2 Characteristics of Fog Computing -- 12.4.3 Attributes of Fog Node -- 12.4.4 Fog Computing Service Model -- 12.4.5 Fog Computing Architecture -- 12.4.6 Data Flow and Control Flow in Fog Architecture -- 12.4.7 Fog Deployment Models -- 12.5 Big Data -- 12.5.1 What is Big Data? -- 12.5.2 Source of Big Data -- 12.5.3 Characteristic of Big Data -- 12.6 Big Data Analytics Using Fog Computing -- 12.7 Conclusion -- References -- Chapter 13 IOT-Based Patient Monitoring System in Real Time -- 13.1 Introduction -- 13.2 Components Used -- 13.2.1 Node MCU -- 13.2.2 Heart Rate/Pulse Sensor -- 13.2.3 Temperature Sensor (LM35) -- 13.3 IoT Platform -- 13.3.1 ThingSpeak-IoT Platform Used in This Work. 13.4 Proposed Method. |
| Record Nr. | UNINA-9911019372203321 |
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Banerjee Chandan
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine learning techniques and analytics for cloud security / / edited by Rajdeep Chakraborty, Anupam Ghosh, and Jyotsna Kumar Mandal
| Machine learning techniques and analytics for cloud security / / edited by Rajdeep Chakraborty, Anupam Ghosh, and Jyotsna Kumar Mandal |
| Pubbl/distr/stampa | Beverly, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , [2022] |
| Descrizione fisica | 1 online resource (480 pages) |
| Disciplina | 006.31 |
| Collana | Advances in learning analytics for intelligent cloud-IoT systems |
| Soggetto topico |
Machine learning
Cloud computing Computer security |
| ISBN |
1-119-76409-2
1-119-76411-4 1-119-76410-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910554801703321 |
| Beverly, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine learning techniques and analytics for cloud security / / edited by Rajdeep Chakraborty, Anupam Ghosh, and Jyotsna Kumar Mandal
| Machine learning techniques and analytics for cloud security / / edited by Rajdeep Chakraborty, Anupam Ghosh, and Jyotsna Kumar Mandal |
| Pubbl/distr/stampa | Beverly, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , [2022] |
| Descrizione fisica | 1 online resource (480 pages) |
| Disciplina | 006.31 |
| Collana | Advances in learning analytics for intelligent cloud-IoT systems |
| Soggetto topico |
Machine learning
Cloud computing Computer security |
| ISBN |
1-119-76409-2
1-119-76411-4 1-119-76410-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910676645103321 |
| Beverly, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Smart Edge Computing : An Operation Research Perspective
| Smart Edge Computing : An Operation Research Perspective |
| Autore | Chakraborty Rajdeep |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (275 pages) |
| Altri autori (Persone) |
GhoshAnupam
MandalJyotsna Kumar ChoudhuryTanupriya ChatterjeePrasenjit |
| ISBN |
1-394-27759-8
1-394-27757-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- Chapter 1. Introduction to Operations Research Methodologies -- 1.1. Introduction -- 1.2. Decision-making framework/models for operations research -- 1.3. Operations research in IoT, IIoT, edge and smart edge computing, sensor data -- 1.4. Paradigms and procedures -- 1.5. Conclusion -- 1.6. References -- Chapter 2. Edge Computing: The Foundation, Emergence and Growing Applications -- 2.1. Introduction -- 2.2. Objective of the work -- 2.3. Methods adopted -- 2.4. Edge computing and edge cloud: basics -- 2.5. Edge computing and edge devices -- 2.6. Edge computing: working fashions, buying and deploying and 5G -- 2.7. Functions and features of edge computing -- 2.7.1. Privacy and security -- 2.7.2. Scalability -- 2.7.3. Reliability -- 2.7.4. Speed -- 2.7.5. Efficiency -- 2.7.6. Latency and bandwidth -- 2.7.7. Reduction in congestion -- 2.8. Edge computing: applications and examples -- 2.8.1. Self-managed and automated cars/vehicles -- 2.8.2. Fleet management -- 2.8.3. Predictive maintenance -- 2.8.4. Voice assisting systems -- 2.8.5. Smart cities and town planning -- 2.8.6. Manufacturing and core sector -- 2.8.7. Healthcare and medical segment -- 2.8.8. Edge computing and augmented reality -- 2.9. Drawbacks, obstacles and issues in edge computing -- 2.10. Edge computing, cloud computing and Internet of Things: some concerns -- 2.11. Future and emergence of edge computing -- 2.12. Conclusion -- 2.13. Acknowledgment -- 2.14. References -- Chapter 3. Utilization of Edge Computing in Digital Education: Conceptual Overview -- 3.1. Introduction -- 3.2. Objectives -- 3.3. Methodology used -- 3.4. Digital education -- 3.4.1. Emerging technologies in digital education -- 3.5. Education and information science -- 3.6. Edge computing.
3.6.1. Edge computing promotes education and information science -- 3.6.2. Conceptual overview of edge computing in education -- 3.6.3. Conceptual diagram of edge computing in education -- 3.6.4. Concept of communication between different layers of edge computing in education -- 3.6.5. Diagram of communication between different layers of edge computing in education -- 3.6.6. Stakeholder of edge computing in digital education -- 3.6.7. Advantages of edge computing in digital education -- 3.6.8. Challenges of edge computing in digital education -- 3.7. Conclusion -- 3.8. Acknowledgment -- 3.9. References -- Chapter 4. Edge Computing with Operations Research Using IoT Devices in Healthcare: Concepts, Tools, Techniques and Use Cases -- 4.1. Overview -- 4.2. The smartness of edge across artificial intelligence with the IoT -- 4.2.1. Operations research in edge computing -- 4.2.2. Artificial intelligence and its innovative strategy -- 4.2.3. Machine learning and its potential application -- 4.2.4. Deep learning and its significance -- 4.2.5. Generative adversarial network and healthcare records -- 4.2.6. Natural language processing and its driving factors -- 4.2.7. Cloud-based intelligent edge computing infrastructure -- 4.2.8. Handling security and privacy issues -- 4.3. Promising approaches in edge healthcare system -- 4.3.1. Software adaptable network -- 4.3.2. Self-learning healthcare IoT -- 4.3.3. Towards Big Data in healthcare IoT -- 4.4. Impact of smartphones on edge computing -- 4.4.1. Use in clinical practice -- 4.4.2. Application for healthcare professionals -- 4.4.3. Edge computing in cutting edge devices -- 4.4.4. Robust smartphone using deep learning -- 4.4.5. Smartphone towards healthcare IoT -- 4.5. Tools, techniques and use cases -- 4.5.1. Smart self-monitoring healthcare system -- 4.5.2. Healthcare development tools. 4.5.3. Simple use cases -- 4.6. Significant forthcomings of edge healthcare IoT -- 4.7. Software and hardware companies developing healthcare tools -- 4.8. Summary -- 4.9. References -- Chapter 5. Performance Measures in Edge Computing Using the Queuing Model -- 5.1. Introduction -- 5.2. Methodology -- 5.2.1. Queuing theory on edge computing -- 5.2.2. Result -- 5.3. Conclusion -- 5.4. Future scope -- 5.5. References -- Chapter 6. A Smart Payment Transaction Procedure by Smart Edge Computing -- 6.1. Introduction -- 6.2. Related works -- 6.3. Ethereum -- 6.3.1. Ethereum's four stages of development -- 6.4. Ethereum's components -- 6.4.1. P2P network -- 6.4.2. Consensus rules -- 6.4.3. Transactions -- 6.4.4. State machine -- 6.4.5. Data structures -- 6.4.6. Consensus algorithm -- 6.4.7. Economic security -- 6.4.8. Clients -- 6.5. General-purpose blockchains to decentralized applications (DApps) -- 6.6. Ether currency units -- 6.7. Ethereum wallet -- 6.7.1. MetaMask -- 6.7.2. Jaxx -- 6.7.3. MyEtherWallet (MEW) -- 6.7.4. Emerald Wallet -- 6.8. A simple contract: a test Ether faucet -- 6.9. Ethereum clients -- 6.9.1. Hardware requirements for a full node -- 6.9.2. Advantages and disadvantages of full node -- 6.9.3. The advantages and disadvantages of public testnet -- 6.10. Conclusion -- 6.11. References -- Chapter 7. Statistical Learning Approach for the Detection of Abnormalities in Cancer Cells for Finding Indication of Metastasis -- 7.1. Introduction -- 7.2. Edge computation: a new era -- 7.3. Impact of edge computation in cancer treatment -- 7.4. Assessment parameters operational methodologies -- 7.5. Shape descriptor analysis: statistical approach -- 7.6. Results and discussion -- 7.7. Conclusion -- 7.8. References -- Chapter 8. Overcoming the Stigma of Alzheimer's Disease by Means of Natural Language Processing as well as Blockchain Technologies. 8.1. Introduction -- 8.2. Alzheimer's disease -- 8.3. Alzheimer's disease types -- 8.4. NLP in chat-bots/AI companions -- 8.5. Proposed methodologies for reduction of stigma -- 8.5.1. Proposed methodology using NLP -- 8.5.2. Model objective function of Alzheimer's disease -- 8.6. Blockchain technology for securing all medical data -- 8.6.1. Blockchain strategies for data privacy in healthcare -- 8.6.2. Application of blockchain technologies -- 8.6.3. Blockchain application intended for EHR data management -- 8.6.4. Issues with blockchain security and privacy -- 8.6.5. Challenges faced by blockchain applications -- 8.7. Conclusion -- 8.8. Future scope -- 8.9. Acknowledgments -- 8.10. References -- Chapter 9. Computer Vision-based Edge Computing System to Detect Health Informatics for Oral Pre-Cancer -- 9.1. Introduction -- 9.2. Related works -- 9.3. Materials and methods -- 9.3.1. Microscopic imaging -- 9.3.2. Proposed methodology -- 9.3.3. RGB color segmentation -- 9.4. Results -- 9.5. Conclusion -- 9.6. References -- Chapter 10. A Study of Ultra-lightweight Ciphers and Security Protocol for Edge Computing -- 10.1. Introduction -- 10.1.1. Evolution of the IoT -- 10.1.2. Content of the review work -- 10.2. Ultra-lightweight ciphers -- 10.2.1. SLIM -- 10.2.2. Piccolo -- 10.2.3. Hummingbird -- 10.2.4. Comparison between SLIM, Piccolo and Hummingbird ciphers -- 10.3. Ultra-lightweight security protocols -- 10.3.1. Lightweight extensible authentication protocol (LEAP) -- 10.3.2. MIFARE -- 10.3.3. Remote frame buffer (RFB) -- 10.3.4. Comparison between LEAP, MIFARE and RFB protocols -- 10.4. Conclusion -- 10.5. References -- Chapter 11. A Study on Security Protocols, Threats and Probable Solutions for Internet of Things Using Blockchain -- 11.1. Introduction -- 11.2. IoT architecture and security challenges -- 11.3. Security threat classifications. 11.3.1. Low-level security threats -- 11.3.2. Intermediate-level security threats -- 11.3.3. High-level security threats -- 11.4. Security solutions for IoT -- 11.4.1. Low-level security solutions -- 11.4.2. Intermediate-level security solutions -- 11.4.3. High-level security solutions -- 11.5. Blockchain-based IoT paradigm: security and privacy issues -- 11.5.1. Lack of IoT-centric agreement mechanisms -- 11.5.2. IoT device incorporation -- 11.5.3. Software update -- 11.5.4. Data scalability and organization -- 11.5.5. Interoperability with the varied IoT devices organized lying on blockchain network -- 11.5.6. Perception layer -- 11.5.7. Network layer -- 11.5.8. Processing layer -- 11.5.9. Application layer -- 11.6. IoT Messaging Protocols -- 11.6.1. Hyper Text Transfer Protocol (HTTP) -- 11.6.2. Message Queue Telemetry Protocols (MQTT) -- 11.6.3. Secure MQTT (SMQTT) -- 11.6.4. Advanced Message Queuing Protocol (AMQP) -- 11.6.5. Constrained Application Protocol (CoAP) -- 11.6.6. Extensible Messaging and Presence Protocol (XMPP) -- 11.6.7. Relative study of different messaging protocols of IoT environments -- 11.7. Advantages of edge computing -- 11.8. Conclusion -- 11.9. References -- List of Authors -- Index -- EULA. |
| Record Nr. | UNINA-9910835065703321 |
| Chakraborty Rajdeep | ||
| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||