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Sustainable Resource Management in Next-Generation Computational Constrained Networks

Edited by Subhasis Dash, Manas Ranjan Lenka, S. Balamurugan, Ambika Prasad Tripathy and Amarendra Mohanty
Series: Industry 5.0 Transformation Applications
Copyright: 2025   |   Expected Pub Date:2025/07/30
ISBN: 9781394212569  |  Hardcover  |  
414 pages

One Line Description
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.

Audience
Software engineers, electronic engineers, and policymakers in the networking and security domain.

Description
The landscape of networking and computational technologies is rapidly evolving, driven by the increasing demand for efficient and sustainable resource management. The advent of next-generation technologies such as 5G and 6G has marked a significant leap in enabling high-capacity, low-latency communication and massive connectivity. These advancements are crucial for supporting the growing number of connected devices and complex applications they run, particularly in environments with limited processing, memory, and energy capabilities.
Sustainable Resource Management in Next Generation Computational Constrained Networks provides insight into the advancements of recent cutting-edge networking technologies that cater to society’s needs more efficiently, meeting the expectations of sustainable resource management in computationally constrained networks. By exploring the practical applications of various next-generation technologies, the book addresses critical challenges such as scalability, interoperability, energy efficiency, and security. This knowledge equips professionals with the tools to enhance network performance, optimize resource management, and develop innovative solutions for sustainable and efficient computational networks, ultimately contributing to the advancement of technology and societal well-being.
Readers will find this book:
• Provides thorough reviews on a wide range of cutting-edge network technologies contributing to resource management in computationally constrained networks;
• Explores the role of various network technologies for the development of sustainable applications;
• Details architectural viewpoints of integrating emerging network technologies with real-world applications to manage network resources efficiently;
• Highlights challenges in integrating the latest network technologies with sustainable real-world applications;
• Discusses real-world case studies of various network technologies in leveraging sustainable resource management for the fulfillment of different industrial and societal needs.

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Author / Editor Details
Subhasis Dash, PhD is an assistant professor in the School of Computer Engineering at the Kalinga Institute of Industrial Technology with over 22 years of teaching experience. His research interests include wireless sensor networks, distributed computing, and operating systems.

Manas Ranjan Lenka, PhD is an assistant professor in the School of Computer Engineering, at the Kalinga Institute of Industrial Technology with over 18 years of experience. His current research focuses on wireless sensor networks, mobile wireless networks, Internet of Things, and blockchain.

S. Balamurugan, PhD is the Director of Intelligent Research Consultancy Services and serves as a consultant for many other companies and start-ups. He has published over 70 books, 300 articles in national and international journals and conferences, and 300 patents. His research interests include artificial intelligence, machine learning, soft computing algorithms, and robotics and automation.

Ambika Prasad Tripathy is a senior technical leader with Cisco’s Network Security Business Unit with over 17 years of experience. He has worked in the network industry to standardize Yang-based subscription mechanisms and their applications. He specializes in Sigtran, 3G, 4G core networks, switching and routing, telemetry, and datacenter, network, and cloud security.

Amarendra Mohanty is a senior engineer at the Intel Corporation in India with over 17 years of research experience. He has worked for a number of leading companies in the computer science field, including Intel, VMWare, TCS, and Aricent. His specializations include network security, network virtualization, data center networking, routing and switching, and 3G wireless networks in the development and QA fields.

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Table of Contents
Preface
1. Enhancing Digital Learning Pedagogy for Lecture Video Recommendation Using Brain Wave Signal
Rabi Shaw, Simanjeet Kalia and Sourabh Mohanty
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
2. Blockchain-Based Sustainable Supply Chain Management
Anuja Ajay, Saji M. S. and Subhasis Dash
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
3. Revolutionizing Aquaculture With the Internet of Things (IoT): An Insightful Learning
Arpita Nayak, Atmika Patnaik, Ipseeta Satpathy, Veena Goswami and B.C.M. Patnaik
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
4. Energy Consumption Optimization in Wireless Sensor Networks
Avik Das, Shatyaki Ghosh and Arindam Basak
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
5. Airline Prediction Using Customer Feedback and Rating Using Machine Learning and Deep Learning
Ch Sambasiva Rao, Pabbathi Manobhi Ram, Viswanadhapalli Siva and Motakatla Satya Sai Krishna Reddy
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
6. The Breakthrough of Future Delivery: Delivery Robots
Ayushi Gupta
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
7. Emergence of Cloud Computing in IoT Applications
Priyanshu Sonthalia and Doddi Puneet
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
8. Conceptual Assessment of Sensory Networks and Its Functional Aspects
Barat Nikhita, Siddhant Prateek Mahanayak and Kunal Anand
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
9. System Security Using Artificial Intelligence and Reduction of Data Breach
M. Avrit, G. P. Siranjeevi, Shruti Mishra, Sandeep Kumar Satapathy, Priyanka Mishra, Pradeep Kumar Mallick and Gyoo Soo Chae
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
10. Mitigating DDoS Attacks: Empowering Network Infrastructure Resilience with AI and ML
Teja Pasonri, Saurav Singh, Vedant Shirapure, Sandeep Kumar Satapathy, Sung-Bae Cho, Shruti Mishra and Pradeep Kumar Mallick
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
11. CyberEDU: An Interactive Educational Tool for DDoS Attack Simulation and Prevention
Pulkit Srivastava, Vedant Shah, Priyanshu Singh, Sandeep Kumar Satapathy, Sung-Bae Cho, Shruti Mishra and Pradeep Kumar Mallick
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
12. Resource Management and Performance Optimization in Constraint Network Systems
Amarendra Kumar Mohanty
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
13. Resource-Constrained Network Management Using Software-Defined Networks
Sayan Bhattacharyya, Manas Ranjan Lenka and Subhasis Dash
13.1 Introduction
13.2 Software-Defined Network Architecture and Its Key Components
13.2.1 Application Plane
13.2.1.1 Network Application
13.2.1.2 Language-Level Virtualization
13.2.2 Control Plane
13.2.2.1 Network Operating System (NOS)
13.2.2.2 Network Hypervisor
13.2.3 Data Plane
13.2.3.1 Network Infrastructure
13.2.4 SDN Protocols
13.2.4.1 Northbound Protocol
13.2.4.2 Southbound Protocol
13.2.4.3 Eastbound Protocol
13.2.4.4 Westbound Protocol
13.2.5 SDN Workflows
13.3 Challenges and Opportunities of SDN in Resource-Constrained Scenarios
13.4 State-of-the-Art Techniques and Tools for Efficient Network Resource Management in SDN Environments
13.5 Performance of the Existing Techniques and Tools with Use Case
13.6 Conclusion and Future Scope
References
14. Vehicles Smoke Monitoring Using Internet of Things and Machine Learning
Dhavakumar P. and Selvakumar Samuel
14.1 Introduction
14.2 Vehicle CO2 Emissions
14.2.1 Impacts of CO2 Emissions
14.3 Recommended Solutions with Internet of Things
14.3.1 IoT System and CO2 Sensors
14.3.2 Benefits of the IoT System
14.3.3 Air Quality Monitoring System (AQMS)
14.4 ML Algorithms
14.4.1 K-Means Algorithm (KM)
14.4.2 Decision Tree Algorithm (DT)
14.4.3 Naive Bayes Algorithm (NB)
14.4.4 Controlling Carbon Unlimited Flow Operation with Machine Learning Approach (CULTML)
14.5 Proposed System Architectures and Designs
14.5.1 Vehicular Unit
14.5.2 Software Unit
14.5.3 Road Transport Office (RTO) Unit
14.6 Logical Design of the Proposed System
14.6.1 Summation Detector Using Artificial Intelligence
14.6.2 Digit Recognition
14.7 Experimental Results
14.8 Physical Design of the Proposed System
14.9 Conclusion
References
15. Enhancing Home Security through IoT Innovation: Recommendations for Biometric Door Lock System to Deter Break-Ins
Muhammad Ehsan Rana, Kamalanathan Shanmugam, Lim Enya and Hrudaya Kumar Tripathy
15.1 Introduction
15.2 Literature Review
15.2.1 Home Security Concerns in Malaysia
15.2.2 Introduction to Biometric Solutions
15.2.3 Enhancing Biometrics with Machine Learning
15.2.4 Biometrics in the Realm of Smart Home Security
15.2.5 Review of Existing Commercial Systems
15.2.5.1 Samsung Smart Door Lock
15.2.5.2 Philips EasyKey
15.2.5.3 Comparison of Systems
15.3 Recommendations for the Implementation of the Proposed Biometric Door Lock System
15.3.1 Software Requirements
15.3.2 Key Hardware Requirements
15.3.2.1 Arduino Nano
15.3.2.2 DFRobot HuskyLens
15.3.2.3 DFRobot UART Fingerprint Scanner
15.3.2.4 Five-Volt Single-Channel Relay Module
15.3.2.5 12VDC Solenoid Lock
15.3.3 Workflow of the Proposed System
15.3.4 Key Features of the Proposed System
15.3.5 Testing the Biometric Door Lock System
15.3.5.1 Fingerprint Authentication Test
15.3.5.2 Facial Recognition Test
15.3.5.3 Dual Authentication Test
15.3.5.4 Access Log Test
15.3.5.5 Mobile Application Integration Test
15.3.5.6 Scalability Test
15.3.5.7 Accuracy Result Analysis
15.4 Conclusion and Future Recommendations
References
Index

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Description
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Table of Contents
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