Smart Edge Computing : An Operation Research Perspective

Smart Edge Computing An Operation Research Perspective

This book pioneers the synergy between state-of-the-art edge computing technologies and the power of operations research. It comprehensively explores real-world applications, demonstrating how various operations' research techniques enhance edge computing's efficiency, reliability and reso...

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Detalles Bibliográficos
Otros Autores: Chakraborty, Rajdeep, editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: London, England : ISTE Ltd and John Wiley & Sons, Inc [2024]
Edición:First edition
Colección:Computer engineering series. International perspectives in decision analysis and operations research set ; volume 2
Materias:
Edge computing.
Artificial intelligence.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009828030006719
Tabla de Contenidos:
  • 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.

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