Freshly Printed - allow 7 days lead
Couldn't load pickup availability
Sustainable Resource Management in Next-Generation Computational Constrained Networks
Subhasis Dash (Edited by), Manas Ranjan Lenka (Edited by), S. Balamurugan (Edited by), Ambika Prasad Tripathy (Edited by), Amarendra Mohanty (Edited by)
9781394212569, Wiley
Hardback, published 27 August 2025
416 pages
28 x 19 x 2.7 cm, 0.862 kg
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. 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: Audience Software engineers, electronic engineers, and policymakers in the networking and security domain.
Preface xv 1 Enhancing Digital Learning Pedagogy for Lecture Video Recommendation Using Brain Wave Signal 1 1.1 Introduction 2 1.2 Related Work 4 1.2.1 E-Learning, M-Learning, and T-Learning 4 1.2.2 Involvement of Networking Reforms in Education 6 1.2.3 Literature Review for Use of NeuroSky Headset in Education Domain 6 1.3 Background 10 1.4 Dataset 10 1.5 Proposed Method and Result 11 1.5.1 Collaborative Filtering Using Brain Signal–Induced Preferences 11 1.5.1.1 Neurophysiological Experiment 11 1.5.1.2 Deducing Preferences from Brain Signals 14 1.5.2 Proposed Methodology for FlipRec Model 16 1.5.2.1 Module for Data Preparation 16 1.5.2.2 FlipRec: Preferred Recommendation Model 19 1.5.3 Using Brain Signal Technology, a Cognitively Aware Lecture Video Recommendation System in Flipped Learning 20 1.5.3.1 Finding Successful Cognitive States with a Clustering Method 20 1.5.3.2 Feature Derivation for Estimating Attention 22 1.6 Result Analysis 23 1.7 Conclusion and Future Research 25 References 25 2 Blockchain-Based Sustainable Supply Chain Management 31 2.1 Introduction 32 2.1.1 Significance of Blockchain for SCM 34 2.1.2 Introduction to Blockchain Interoperability 35 2.2 Blockchain for Supply Chain Management 35 2.2.1 Characteristics and Requirements of Blockchain-Based Supply Chain 37 2.2.1.1 Characteristics of Supply Chain 37 2.2.1.2 Requirements of Supply Chain 40 2.2.2 Blockchain-Based Data Sharing for Supply Chain 41 2.2.3 Access Control and Trust Management in Blockchain- Based SCM 43 2.2.3.1 Access Control Mechanisms in SCM 43 2.2.3.2 Trust Management in Supply Chain 44 2.3 Interoperability in Blockchain 45 2.3.1 Overview of Blockchain Interoperability Approaches 45 2.3.1.1 Public Connectors 45 2.3.1.2 Blockchain of Blockchains (BoB) 46 2.3.1.3 Hybrid Connectors 46 2.3.2 Gateways for Interoperability and Manageability 48 2.3.3 Interoperability Approaches 49 2.4 Design Considerations and Open Challenges 50 2.5 Summary 51 2.5.1 Advantages of Blockchain for SSCM 51 2.6 Scope of Future Work Emphasis 52 References 53 3 Revolutionizing Aquaculture With the Internet of Things (IoT): An Insightful Learning 59 3.1 Introduction 60 3.2 Environmental Monitoring via IoT for Sustainable Aquaculture 63 3.3 The Primacy of IoT in Enhancing Fish Health Monitoring 67 3.4 Delving Into IoT: Improving Agricultural Water Quality Management 70 3.5 Connecting the Dots: Using IoT Fish Behavior Monitoring to Improve Aquaculture Practices 74 3.6 The Worldwide Deployment of IoT in Aquaculture: Advantages and Success Factors 79 3.7 Conclusion 81 Acknowledgment 81 References 81 4 Energy Consumption Optimization in Wireless Sensor Networks 87 4.1 Introduction 87 4.1.1 WSN Application and Hardware Characteristics 90 4.2 MAC Layer Approaches 93 4.2.1 IEEE 802.15.4 Standard along with the ZigBee Technology 94 4.2.2 Different Other MAC Approaches 95 4.3 Routing Approaches 98 4.4 Transmission Power Control Approaches 99 4.5 Autonomic Approaches 102 4.6 Application of ZigBee in a WSN 105 4.7 WSN with Cloud Computing 106 4.8 Final Considerations and Future Directions 109 References 110 5 Airline Prediction Using Customer Feedback and Rating Using Machine Learning and Deep Learning 115 5.1 Introduction 116 5.1.1 Customer Ratings and Recommendation 116 5.2 Literature Survey 117 5.3 System Design 119 5.4 Methodology 120 5.4.1 Modules 120 5.4.1.1 Data Collection 120 5.4.1.2 Review-Based Airline Prediction 120 5.4.1.3 Rating-Based Airline Prediction 121 5.5 Algorithm Used: Random Forest, Convolutional Neural Network, and AdaBoost 121 5.5.1 Random Forest System 121 5.5.2 Convolutional 1D Neural Network–Based Training 122 5.5.2.1 Sequential Model 122 5.5.2.2 Add 1D Convolutional Layer 123 5.5.2.3 Adding 1D Max Pooling Layer 123 5.5.2.4 Adding Dense Layer 123 5.5.2.5 Neural Network Training 123 5.5.3 AdaBoost Algorithm 124 5.6 Experimental Results and Evaluations 125 5.7 Screenshots 126 5.8 Conclusion 130 References 130 6 The Breakthrough of Future Delivery: Delivery Robots 133 6.1 Introduction 133 6.2 Related Work 136 6.3 Evolution of Delivery Robot 138 6.4 Working Principal/Model of Delivery Robots 141 6.5 Benefits of Delivery Robots 143 6.6 Applications of Delivery Robots 149 6.7 Development Projects 153 6.8 Challenging Issues with Delivery Robots 158 6.9 Conclusion and Future Work 165 References 166 7 Emergence of Cloud Computing in IoT Applications 169 7.1 Introduction 170 7.1.1 Characteristics of Cloud Computing 170 7.1.2 Types of Cloud Deployment Models 171 7.1.3 Categories of Cloud Computing Architectures 172 7.1.4 Types of Cloud Service Models 173 7.2 Benefits of IoT and Cloud Integration 174 7.2.1 Scalability and Elasticity of Cloud Resources for Managing IoT Data 174 7.2.2 Reduced Infrastructure Costs with Cloud-Based Solutions 174 7.2.3 Improved Accessibility and Availability of IoT Services with Cloud Deployment 175 7.2.4 Enhanced Processing Power and Analytics Capabilities with Cloud Computing 175 7.2.5 Reduced Time to Market and Increased Innovation with Cloud-Based IoT Development 175 7.3 Cloud-Based IoT Architecture 175 7.3.1 Four Layers of Cloud-Based IoT Architecture 175 7.3.2 Role of Gateways in Linking IoT Devices to the Cloud 176 7.3.3 Overview of Cloud-Based IoT Platforms and Services 177 7.3.4 Cloud-Based IoT Standards and Protocols, such as MQTT, CoAP, AMQP, and HTTP 177 7.4 Cloud-Based IoT Applications 180 7.5 Challenges in IoT Cloud Integration 181 7.5.1 Security Risks and Challenges Associated with Cloud-Based IoT Solutions 181 7.5.2 Latency and Bandwidth Constraints of IoT Systems Hosted in the Cloud 181 7.5.3 Interoperability Issues Between Different IoT Devices and Cloud Platforms 182 7.5.4 Legal and Regulatory Challenges Associated with IoT Using Cloud Solutions 182 7.6 Open Issues and Research Directions 182 7.6.1 Future Trends and Developments in Cloud-Based IoT Solutions 182 7.6.2 Opportunities for Research in Cloud-Based IoT Solutions 182 7.6.3 Overview of Emerging Cloud-Based IoT Standards and Protocols 183 7.7 Case Study 1: Smart Home Automation Using Cloud-Based IoT 183 7.8 Case Study 2: Industrial IoT Optimization Using Cloud-Based IoT 184 7.9 Conclusion 185 References 186 8 Conceptual Assessment of Sensory Networks and Its Functional Aspects 189 8.1 Introduction 189 8.2 Evolution of IoT 191 8.2.1 Phase 1: Early Adopters (Pre-2010) 192 8.2.2 Phase 2: Connectivity and Smart Devices (2010–2015) 193 8.2.3 Phase 3: Big Data and Cloud Computing (2015 to Present) 194 8.2.4 Phase 4: Artificial Intelligence and Edge Computing (Present and Future) 195 8.3 Features of IoT 196 8.4 Architectural Framework of IoT 199 8.4.1 Device Layer 200 8.4.2 Network Layer 201 8.4.3 Platform Layer 202 8.4.4 Application Layer 203 8.5 Components of IoT 204 8.6 Applications of IoT 206 8.7 Case Study 211 8.7.1 Overview of Barcelona Smart City Project 211 8.7.2 Methodology 212 8.8 Conclusion 213 References 214 9 System Security Using Artificial Intelligence and Reduction of Data Breach 221 9.1 Introduction 222 9.2 Related Work 224 9.3 Methodology 224 9.3.1 Implementation of Socket Programming Concept 224 9.3.2 Machine Learning 225 9.3.3 Deep Learning 225 9.3.4 Human Assistance 225 9.4 Proposed Model 225 9.5 Experimental Result/Result Analysis 227 9.6 Conclusion and Future Work 231 References 231 10 Mitigating DDoS Attacks: Empowering Network Infrastructure Resilience with AI and ML 233 10.1 Introduction 234 10.1.1 Categories of DDoS Attack 235 10.1.1.1 SYN Flood Attacks 235 10.1.1.2 UDP Flood Attacks 235 10.1.1.3 MSSQL Attacks 235 10.1.1.4 LDAP Attacks 235 10.1.1.5 Portmap Attacks 236 10.1.1.6 NetBIOS Attacks 236 10.1.2 Harnessing Machine Learning for DDoS Threat Detection 236 10.1.3 AI Models for DDoS Threat Detection 236 10.1.4 Beyond Classification: AI for Real-Time Detection and Mitigation 237 10.1.5 Collaboration and Knowledge Sharing 237 10.2 Related Work 237 10.3 Methodology 239 10.3.1 Pseudocode-1: Jupyter Project Code 240 10.3.2 Pseudocode-2: Project KNN Model 241 10.3.3 Hyperparameter Tuning and Evaluation 242 10.3.4 Enhancing Model Accuracy 242 10.3.5 Ping Request and DDoS Attack 242 10.4 Proposed Model 243 10.5 Experimental Result/Result Analysis 245 10.5.1 Demo of DDoS Attack 245 10.5.2 Packet Sniffing and Detecting Traffic 246 10.5.3 Accuracy Graph 246 10.5.4 Precision Graph 247 10.6 Conclusion/Future Work 248 References 248 11 CyberEDU: An Interactive Educational Tool for DDoS Attack Simulation and Prevention 251 11.1 Introduction 252 11.2 Related Work 255 11.3 Methodology 257 11.4 Proposed Model 260 11.5 Experimental Result/Result Analysis 263 11.6 Conclusion and Future Work 267 References 267 12 Resource Management and Performance Optimization in Constraint Network Systems 269 12.1 Introduction 270 12.2 Resource Allocation Principles 271 12.3 Network Capacity and Utilization 274 12.4 Performance Optimization Strategies 280 12.4.1 Resource Management in Physical Networks 281 12.4.2 Resource Management in Virtual Networks 297 12.4.3 Resource Management in Software-Defined Networking (SDN) 300 12.5 Real-World Applications 302 12.5.1 Data Plane Development Kit Libraries 306 12.5.2 Virtual Machine Device Queues (VMDQ) 309 12.6 Conclusion and Future Directions 311 References 312 13 Resource-Constrained Network Management Using Software-Defined Networks 315 13.1 Introduction 315 13.2 Software-Defined Network Architecture and Its Key Components 317 13.2.1 Application Plane 319 13.2.1.1 Network Application 320 13.2.1.2 Language-Level Virtualization 320 13.2.2 Control Plane 320 13.2.2.1 Network Operating System (NOS) 320 13.2.2.2 Network Hypervisor 320 13.2.3 Data Plane 321 13.2.3.1 Network Infrastructure 321 13.2.4 SDN Protocols 321 13.2.4.1 Northbound Protocol 321 13.2.4.2 Southbound Protocol 322 13.2.4.3 Eastbound Protocol 322 13.2.4.4 Westbound Protocol 323 13.2.5 SDN Workflows 324 13.3 Challenges and Opportunities of SDN in Resource- Constrained Scenarios 326 13.4 State-of-the-Art Techniques and Tools for Efficient Network Resource Management in SDN Environments 327 13.5 Performance of the Existing Techniques and Tools with Use Case 329 13.6 Conclusion and Future Scope 330 References 331 14 Vehicles Smoke Monitoring Using Internet of Things and Machine Learning 337 14.1 Introduction 337 14.2 Vehicle CO 2 Emissions 338 14.2.1 Impacts of CO 2 Emissions 339 14.3 Recommended Solutions with Internet of Things 340 14.3.1 IoT System and CO 2 Sensors 340 14.3.2 Benefits of the IoT System 342 14.3.3 Air Quality Monitoring System (AQMS) 343 14.4 ml Algorithms 346 14.4.1 K-Means Algorithm (KM) 346 14.4.2 Decision Tree Algorithm (DT) 347 14.4.3 Naive Bayes Algorithm (NB) 347 14.4.4 Controlling Carbon Unlimited Flow Operation with Machine Learning Approach (CULTML) 347 14.5 Proposed System Architectures and Designs 348 14.5.1 Vehicular Unit 349 14.5.2 Software Unit 350 14.5.3 Road Transport Office (RTO) Unit 351 14.6 Logical Design of the Proposed System 352 14.6.1 Summation Detector Using Artificial Intelligence 352 14.6.2 Digit Recognition 352 14.7 Experimental Results 354 14.8 Physical Design of the Proposed System 356 14.9 Conclusion 357 References 357 15 Enhancing Home Security through IoT Innovation: Recommendations for Biometric Door Lock System to Deter Break-Ins 359 15.1 Introduction 360 15.2 Literature Review 361 15.2.1 Home Security Concerns in Malaysia 362 15.2.2 Introduction to Biometric Solutions 363 15.2.3 Enhancing Biometrics with Machine Learning 364 15.2.4 Biometrics in the Realm of Smart Home Security 365 15.2.5 Review of Existing Commercial Systems 367 15.2.5.1 Samsung Smart Door Lock 367 15.2.5.2 Philips EasyKey 369 15.2.5.3 Comparison of Systems 371 15.3 Recommendations for the Implementation of the Proposed Biometric Door Lock System 372 15.3.1 Software Requirements 373 15.3.2 Key Hardware Requirements 374 15.3.2.1 Arduino Nano 374 15.3.2.2 DFRobot HuskyLens 375 15.3.2.3 DFRobot UART Fingerprint Scanner 375 15.3.2.4 Five-Volt Single-Channel Relay Module 376 15.3.2.5 12VDC Solenoid Lock 376 15.3.3 Workflow of the Proposed System 376 15.3.4 Key Features of the Proposed System 378 15.3.5 Testing the Biometric Door Lock System 380 15.3.5.1 Fingerprint Authentication Test 380 15.3.5.2 Facial Recognition Test 381 15.3.5.3 Dual Authentication Test 382 15.3.5.4 Access Log Test 384 15.3.5.5 Mobile Application Integration Test 385 15.3.5.6 Scalability Test 386 15.3.5.7 Accuracy Result Analysis 387 15.4 Conclusion and Future Recommendations 389 References 390 Index 393
Rabi Shaw, Simanjeet Kalia and Sourabh Mohanty
Anuja Ajay, Saji M. S. and Subhasis Dash
Arpita Nayak, Atmika Patnaik, Ipseeta Satpathy, Veena Goswami and B.C.M. Patnaik
Avik Das, Shatyaki Ghosh and Arindam Basak
Ch Sambasiva Rao, Pabbathi Manobhi Ram, Viswanadhapalli Siva and Motakatla Satya Sai Krishna Reddy
Ayushi Gupta
Priyanshu Sonthalia and Doddi Puneet
Barat Nikhita, Siddhant Prateek Mahanayak and Kunal Anand
M. Avrit, G. P. Siranjeevi, Shruti Mishra, Sandeep Kumar Satapathy, Priyanka Mishra, Pradeep Kumar Mallick and Gyoo Soo Chae
Teja Pasonri, Saurav Singh, Vedant Shirapure, Sandeep Kumar Satapathy, Sung-Bae Cho, Shruti Mishra and Pradeep Kumar Mallick
Pulkit Srivastava, Vedant Shah, Priyanshu Singh, Sandeep Kumar Satapathy, Sung-Bae Cho, Shruti Mishra and Pradeep Kumar Mallick
Amarendra Kumar Mohanty
Sayan Bhattacharyya, Manas Ranjan Lenka and Subhasis Dash
Dhavakumar P. and Selvakumar Samuel
Muhammad Ehsan Rana, Kamalanathan Shanmugam, Lim Enya and Hrudaya Kumar Tripathy
Subject Areas: Electronics & communications engineering [TJ]
