Sustainable resource management in next-generation computational constrained networks

The book provides essential insights into cutting-edge networking technologies that not only enhance performance and efficiency but also address critical sustainability challenges in an increasingly connected world.

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Bibliographic Details
Main Author Dash, Subhasis
Other Authors Lenka, Manas Ranjan, Balamurugan, S., Prasad Tripathy, Ambika, Mohanty, Amarendra
Format Electronic eBook
LanguageEnglish
Published Hoboken, NJ : Beverly, MA : John Wiley & Sons, Inc. ; Scrivener Publishing LLC, 2025.
SeriesIndustry 5. 0 Transformation Applications Series
Subjects
Computer networks > Management.
elektronické knihy
electronic books
Online AccessFull text
ISBN9781394212781
9781394212798
9781394212569
Physical Description1 online zdroj.

Cover

Table of Contents:
  • Cover
  • Series Page
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Chapter 1 Enhancing Digital Learning Pedagogy for Lecture Video Recommendation Using Brain Wave Signal
  • 1.1 Introduction
  • 1.2 Related Work
  • 1.2.1 E-Learning, M-Learning, and T-Learning
  • 1.2.2 Involvement of Networking Reforms in Education
  • 1.2.3 Literature Review for Use of NeuroSky Headset in Education Domain
  • 1.3 Background
  • 1.4 Dataset
  • 1.5 Proposed Method and Result
  • 1.5.1 Collaborative Filtering Using Brain Signal-Induced Preferences
  • 1.5.1.1 Neurophysiological Experiment
  • 1.5.1.2 Deducing Preferences from Brain Signals
  • 1.5.2 Proposed Methodology for FlipRec Model
  • 1.5.2.1 Module for Data Preparation
  • 1.5.2.2 FlipRec: Preferred Recommendation Model
  • 1.5.3 Using Brain Signal Technology, a Cognitively Aware Lecture Video Recommendation System in Flipped Learning
  • 1.5.3.1 Finding Successful Cognitive States with a Clustering Method
  • 1.5.3.2 Feature Derivation for Estimating Attention
  • 1.6 Result Analysis
  • 1.7 Conclusion and Future Research
  • References
  • Chapter 2 Blockchain-Based Sustainable Supply Chain Management
  • 2.1 Introduction
  • 2.1.1 Significance of Blockchain for SCM
  • 2.1.2 Introduction to Blockchain Interoperability
  • 2.2 Blockchain for Supply Chain Management
  • 2.2.1 Characteristics and Requirements of Blockchain-Based Supply Chain
  • 2.2.1.1 Characteristics of Supply Chain
  • 2.2.1.2 Requirements of Supply Chain
  • 2.2.2 Blockchain-Based Data Sharing for Supply Chain
  • 2.2.3 Access Control and Trust Management in Blockchain- Based SCM
  • 2.2.3.1 Access Control Mechanisms in SCM
  • 2.2.3.2 Trust Management in Supply Chain
  • 2.3 Interoperability in Blockchain
  • 2.3.1 Overview of Blockchain Interoperability Approaches
  • 2.3.1.1 Public Connectors
  • 2.3.1.2 Blockchain of Blockchains (BoB).
  • 2.3.1.3 Hybrid Connectors
  • 2.3.2 Gateways for Interoperability and Manageability
  • 2.3.3 Interoperability Approaches
  • 2.4 Design Considerations and Open Challenges
  • 2.5 Summary
  • 2.5.1 Advantages of Blockchain for SSCM
  • 2.6 Scope of Future Work Emphasis
  • References
  • Chapter 3 Revolutionizing Aquaculture With the Internet of Things (IoT): An Insightful Learning
  • 3.1 Introduction
  • 3.2 Environmental Monitoring via IoT for Sustainable Aquaculture
  • 3.3 The Primacy of IoT in Enhancing Fish Health Monitoring
  • 3.4 Delving Into IoT: Improving Agricultural Water Quality Management
  • 3.5 Connecting the Dots: Using IoT Fish Behavior Monitoring to Improve Aquaculture Practices
  • 3.6 The Worldwide Deployment of IoT in Aquaculture: Advantages and Success Factors
  • 3.7 Conclusion
  • Acknowledgment
  • References
  • Chapter 4 Energy Consumption Optimization in Wireless Sensor Networks
  • 4.1 Introduction
  • 4.1.1 WSN Application and Hardware Characteristics
  • 4.2 MAC Layer Approaches
  • 4.2.1 IEEE 802.15.4 Standard along with the ZigBee Technology
  • 4.2.2 Different Other MAC Approaches
  • 4.3 Routing Approaches
  • 4.4 Transmission Power Control Approaches
  • 4.5 Autonomic Approaches
  • 4.6 Application of ZigBee in a WSN
  • 4.7 WSN with Cloud Computing
  • 4.8 Final Considerations and Future Directions
  • References
  • Chapter 5 Airline Prediction Using Customer Feedback and Rating Using Machine Learning and Deep Learning
  • 5.1 Introduction
  • 5.1.1 Customer Ratings and Recommendation
  • 5.2 Literature Survey
  • 5.3 System Design
  • 5.4 Methodology
  • 5.4.1 Modules
  • 5.4.1.1 Data Collection
  • 5.4.1.2 Review-Based Airline Prediction
  • 5.4.1.3 Rating-Based Airline Prediction
  • 5.5 Algorithm Used: Random Forest, Convolutional Neural Network, and AdaBoost
  • 5.5.1 Random Forest System
  • 5.5.2 Convolutional 1D Neural Network-Based Training.
  • 5.5.2.1 Sequential Model
  • 5.5.2.2 Add 1D Convolutional Layer
  • 5.5.2.3 Adding 1D Max Pooling Layer
  • 5.5.2.4 Adding Dense Layer
  • 5.5.2.5 Neural Network Training
  • 5.5.3 AdaBoost Algorithm
  • 5.6 Experimental Results and Evaluations
  • 5.7 Screenshots
  • 5.8 Conclusion
  • References
  • Chapter 6 The Breakthrough of Future Delivery: Delivery Robots
  • 6.1 Introduction
  • 6.2 Related Work
  • 6.3 Evolution of Delivery Robot
  • 6.4 Working Principal/Model of Delivery Robots
  • 6.5 Benefits of Delivery Robots
  • 6.6 Applications of Delivery Robots
  • 6.7 Development Projects
  • 6.8 Challenging Issues with Delivery Robots
  • 6.9 Conclusion and Future Work
  • References
  • Chapter 7 Emergence of Cloud Computing in IoT Applications
  • 7.1 Introduction
  • 7.1.1 Characteristics of Cloud Computing
  • 7.1.2 Types of Cloud Deployment Models
  • 7.1.3 Categories of Cloud Computing Architectures
  • 7.1.4 Types of Cloud Service Models
  • 7.2 Benefits of IoT and Cloud Integration
  • 7.2.1 Scalability and Elasticity of Cloud Resources for Managing IoT Data
  • 7.2.2 Reduced Infrastructure Costs with Cloud-Based Solutions
  • 7.2.3 Improved Accessibility and Availability of IoT Services with Cloud Deployment
  • 7.2.4 Enhanced Processing Power and Analytics Capabilities with Cloud Computing
  • 7.2.5 Reduced Time to Market and Increased Innovation with Cloud-Based IoT Development
  • 7.3 Cloud-Based IoT Architecture
  • 7.3.1 Four Layers of Cloud-Based IoT Architecture
  • 7.3.2 Role of Gateways in Linking IoT Devices to the Cloud
  • 7.3.3 Overview of Cloud-Based IoT Platforms and Services
  • 7.3.4 Cloud-Based IoT Standards and Protocols, such as MQTT, CoAP, AMQP, and HTTP
  • 7.4 Cloud-Based IoT Applications
  • 7.5 Challenges in IoT Cloud Integration
  • 7.5.1 Security Risks and Challenges Associated with Cloud-Based IoT Solutions.
  • 7.5.2 Latency and Bandwidth Constraints of IoT Systems Hosted in the Cloud
  • 7.5.3 Interoperability Issues Between Different IoT Devices and Cloud Platforms
  • 7.5.4 Legal and Regulatory Challenges Associated with IoT Using Cloud Solutions
  • 7.6 Open Issues and Research Directions
  • 7.6.1 Future Trends and Developments in Cloud-Based IoT Solutions
  • 7.6.2 Opportunities for Research in Cloud-Based IoT Solutions
  • 7.6.3 Overview of Emerging Cloud-Based IoT Standards and Protocols
  • 7.7 Case Study 1: Smart Home Automation Using Cloud-Based IoT
  • 7.8 Case Study 2: Industrial IoT Optimization Using Cloud-Based IoT
  • 7.9 Conclusion
  • References
  • Chapter 8 Conceptual Assessment of Sensory Networks and Its Functional Aspects
  • 8.1 Introduction
  • 8.2 Evolution of IoT
  • 8.2.1 Phase 1: Early Adopters (Pre-2010)
  • 8.2.2 Phase 2: Connectivity and Smart Devices (2010-2015)
  • 8.2.3 Phase 3: Big Data and Cloud Computing (2015 to Present)
  • 8.2.4 Phase 4: Artificial Intelligence and Edge Computing (Present and Future)
  • 8.3 Features of IoT
  • 8.4 Architectural Framework of IoT
  • 8.4.1 Device Layer
  • 8.4.2 Network Layer
  • 8.4.3 Platform Layer
  • 8.4.4 Application Layer
  • 8.5 Components of IoT
  • 8.6 Applications of IoT
  • 8.7 Case Study
  • 8.7.1 Overview of Barcelona Smart City Project
  • 8.7.2 Methodology
  • 8.8 Conclusion
  • References
  • Chapter 9 System Security Using Artificial Intelligence and Reduction of Data Breach
  • 9.1 Introduction
  • 9.2 Related Work
  • 9.3 Methodology
  • 9.3.1 Implementation of Socket Programming Concept
  • 9.3.2 Machine Learning
  • 9.3.3 Deep Learning
  • 9.3.4 Human Assistance
  • 9.4 Proposed Model
  • 9.5 Experimental Result/Result Analysis
  • 9.6 Conclusion and Future Work
  • References
  • Chapter 10 Mitigating DDoS Attacks: Empowering Network Infrastructure Resilience with AI and ML
  • 10.1 Introduction.
  • 10.1.1 Categories of DDoS Attack
  • 10.1.1.1 SYN Flood Attacks
  • 10.1.1.2 UDP Flood Attacks
  • 10.1.1.3 MSSQL Attacks
  • 10.1.1.4 LDAP Attacks
  • 10.1.1.5 Portmap Attacks
  • 10.1.1.6 NetBIOS Attacks
  • 10.1.2 Harnessing Machine Learning for DDoS Threat Detection
  • 10.1.3 AI Models for DDoS Threat Detection
  • 10.1.4 Beyond Classification: AI for Real-Time Detection and Mitigation
  • 10.1.5 Collaboration and Knowledge Sharing
  • 10.2 Related Work
  • 10.3 Methodology
  • 10.3.1 Pseudocode-1: Jupyter Project Code
  • 10.3.2 Pseudocode-2: Project KNN Model
  • 10.3.3 Hyperparameter Tuning and Evaluation
  • 10.3.4 Enhancing Model Accuracy
  • 10.3.5 Ping Request and DDoS Attack
  • 10.4 Proposed Model
  • 10.5 Experimental Result/Result Analysis
  • 10.5.1 Demo of DDoS Attack
  • 10.5.2 Packet Sniffing and Detecting Traffic
  • 10.5.3 Accuracy Graph
  • 10.5.4 Precision Graph
  • 10.6 Conclusion/Future Work
  • References
  • Chapter 11 CyberEDU: An Interactive Educational Tool for DDoS Attack Simulation and Prevention
  • 11.1 Introduction
  • 11.2 Related Work
  • 11.3 Methodology
  • 11.4 Proposed Model
  • 11.5 Experimental Result/Result Analysis
  • 11.6 Conclusion and Future Work
  • References
  • Chapter 12 Resource Management and Performance Optimization in Constraint Network Systems
  • 12.1 Introduction
  • 12.2 Resource Allocation Principles
  • 12.3 Network Capacity and Utilization
  • 12.4 Performance Optimization Strategies
  • 12.4.1 Resource Management in Physical Networks
  • 12.4.2 Resource Management in Virtual Networks
  • 12.4.3 Resource Management in Software-Defined Networking (SDN)
  • 12.5 Real-World Applications
  • 12.5.1 Data Plane Development Kit Libraries
  • 12.5.2 Virtual Machine Device Queues (VMDQ)
  • 12.6 Conclusion and Future Directions
  • References
  • Chapter 13 Resource-Constrained Network Management Using Software-Defined Networks.
  • 13.1 Introduction.

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