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[null,"1.","Record Nr.",null,"UNINA9911021977503321"]
[null,null,"Autore",null,"Dash Subhasis"]
[null,null,"Titolo",null,"Sustainable Resource Management in Next-Generation Computational Constrained Networks"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2025"]
[{"$nulls":4},"©2025"]
[null,null,"ISBN",null,"1-394-21279-8"]
[{"$nulls":4},"1-394-21278-X"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (419 pages)"]
[null,null,"Collana",null,"Industry 5. 0 Transformation Applications Series"]
[null,null,"Disciplina",null,"004.6"]
[null,null,"Soggetti",null,"Computer networks - Management"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"-- 2.2.3.2 Trust Management in Supply Chain -- 2.3 Interoperability in Blockchain -- 2.3.1 Overview of Blockchain Interoperability Approaches -- 2.3.1.1 Public Connectors -- 2.3.1.2 Blockchain of Blockchains (BoB)."]
[{"$nulls":4},"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."]
[{"$nulls":4},"5.5.2.1 Sequential Model -- 5.5.2.2 Add 1D Convolutional Layer -- 5.5.2.3 Adding 1D Max Pooling Layer -- 5.5.2.4 Adding Dense Layer -- 5.5.2.5 Neural Network Training -- 5.5.3 AdaBoost Algorithm -- 5.6 Experimental Results and Evaluations -- 5.7 Screenshots -- 5.8 Conclusion -- References -- Chapter 6 The Breakthrough of Future Delivery: Delivery Robots -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Evolution of Delivery Robot -- 6.4 Working Principal/Model of Delivery Robots -- 6.5 Benefits of Delivery Robots -- 6.6 Applications of Delivery Robots -- 6.7 Development Projects -- 6.8 Challenging Issues with Delivery Robots -- 6.9 Conclusion and Future Work -- References -- Chapter 7 Emergence of Cloud Computing in IoT Applications -- 7.1 Introduction -- 7.1.1 Characteristics of Cloud Computing -- 7.1.2 Types of Cloud Deployment Models -- 7.1.3 Categories of Cloud Computing Architectures -- 7.1.4 Types of Cloud Service Models -- 7.2 Benefits of IoT and Cloud Integration -- 7.2.1 Scalability and Elasticity of Cloud Resources for Managing IoT Data -- 7.2.2 Reduced Infrastructure Costs with Cloud-Based Solutions -- 7.2.3 Improved Accessibility and Availability of IoT Services with Cloud Deployment -- 7.2.4 Enhanced Processing Power and Analytics Capabilities with Cloud Computing -- 7.2.5 Reduced Time to Market and Increased Innovation with Cloud-Based IoT Development -- 7.3 Cloud-Based IoT Architecture -- 7.3.1 Four Layers of Cloud-Based IoT Architecture -- 7.3.2 Role of Gateways in Linking IoT Devices to the Cloud -- 7.3.3 Overview of Cloud-Based IoT Platforms and Services -- 7.3.4 Cloud-Based IoT Standards and Protocols, such as MQTT, CoAP, "]
[{"$nulls":4},"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."]
[{"$nulls":4},"7.5.2 Latency and Bandwidth Constraints of IoT Systems Hosted in the Cloud -- 7.5.3 Interoperability Issues Between Different IoT Devices and Cloud Platforms -- 7.5.4 Legal and Regulatory Challenges Associated with IoT Using Cloud Solutions -- 7.6 Open Issues and Research Directions -- 7.6.1 Future Trends and Developments in Cloud-Based IoT Solutions -- 7.6.2 Opportunities for Research in Cloud-Based IoT Solutions -- 7.6.3 Overview of Emerging Cloud-Based IoT Standards and Protocols -- 7.7 Case Study 1: Smart Home Automation Using Cloud-Based IoT -- 7.8 Case Study 2: Industrial IoT Optimization Using Cloud-Based IoT -- 7.9 Conclusion -- References -- Chapter 8 Conceptual Assessment of Sensory Networks and Its Functional Aspects -- 8.1 Introduction -- 8.2 Evolution of IoT -- 8.2.1 Phase 1: Early Adopters (Pre-2010) -- 8.2.2 Phase 2: Connectivity and Smart Devices (2010-2015) -- 8.2.3 Phase 3: Big Data and Cloud Computing (2015 to Present) -- 8.2.4 Phase 4: Artificial Intelligence and Edge Computing (Present and Future) -- 8.3 Features of IoT -- 8.4 Architectural Framework of IoT -- 8.4.1 Device Layer -- 8.4.2 Network Layer -- 8.4.3 Platform Layer -- 8.4.4 Application Layer -- 8.5 Components of IoT -- 8.6 Applications of IoT -- 8.7 Case Study -- 8.7.1 Overview of Barcelona Smart City Project -- 8.7.2 Methodology -- 8.8 Conclusion -- References -- Chapter 9 System Security Using Artificial Intelligence and Reduction of Data Breach -- 9.1 Introduction -- 9.2 Related Work -- 9.3 Methodology -- 9.3.1 Implementation of Socket Programming Concept -- 9.3.2 Machine Learning -- 9.3.3 Deep Learning -- 9.3.4 Human Assistance -- 9.4 Proposed Model -- 9.5 Experimental Result/Result Analysis -- 9.6 Conclusion and Future Work -- References -- Chapter 10 Mitigating DDoS Attacks: Empowering Network Infrastructure Resilience with AI and ML -- 10.1 Introduction."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"Directions -- References -- Chapter 13 Resource-Constrained Network Management Using Software-Defined Networks."]
[{"$nulls":4},"13.1 Introduction."]
[null,null,null,"Sommario/riassunto","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."]