{"product_id":"sustainable-resource-management-in-next-generation-computational-constrained-networks-hardback-9781394212569","title":"Sustainable Resource Management in Next-Generation Computational Constrained Networks (Hardback) 9781394212569","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eSustainable Resource Management in Next-Generation Computational Constrained Networks\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eSubhasis Dash (Edited by), Manas Ranjan Lenka (Edited by), S. Balamurugan (Edited by), Ambika Prasad Tripathy (Edited by), Amarendra Mohanty (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394212569, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 27 August 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e416 pages\u003cbr\u003e28 x 19 x 2.7 cm, 0.862 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eThe 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.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eThe 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. \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eSustainable Resource Management in Next-Generation Computational Constrained Networks\u003c\/i\u003e 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. \u003c\/p\u003e\n\u003cp\u003eReaders will find this book: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eProvides thorough reviews on a wide range of cutting-edge network technologies contributing to resource management in computationally constrained networks;\u003c\/li\u003e \u003cli\u003eExplores the role of various network technologies for the development of sustainable applications;\u003c\/li\u003e \u003cli\u003eDetails architectural viewpoints of integrating emerging network technologies with real-world applications to manage network resources efficiently;\u003c\/li\u003e \u003cli\u003eHighlights challenges in integrating the latest network technologies with sustainable real-world applications;\u003c\/li\u003e \u003cli\u003eDiscusses real-world case studies of various network technologies in leveraging sustainable resource management for the fulfillment of different industrial and societal needs.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eSoftware engineers, electronic engineers, and policymakers in the networking and security domain.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Enhancing Digital Learning Pedagogy for Lecture Video Recommendation Using Brain Wave Signal 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRabi Shaw, Simanjeet Kalia and Sourabh Mohanty\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 Related Work 4\u003c\/p\u003e \u003cp\u003e1.2.1 E-Learning, M-Learning, and T-Learning 4\u003c\/p\u003e \u003cp\u003e1.2.2 Involvement of Networking Reforms in Education 6\u003c\/p\u003e \u003cp\u003e1.2.3 Literature Review for Use of NeuroSky Headset in Education Domain 6\u003c\/p\u003e \u003cp\u003e1.3 Background 10\u003c\/p\u003e \u003cp\u003e1.4 Dataset 10\u003c\/p\u003e \u003cp\u003e1.5 Proposed Method and Result 11\u003c\/p\u003e \u003cp\u003e1.5.1 Collaborative Filtering Using Brain Signal–Induced Preferences 11\u003c\/p\u003e \u003cp\u003e1.5.1.1 Neurophysiological Experiment 11\u003c\/p\u003e \u003cp\u003e1.5.1.2 Deducing Preferences from Brain Signals 14\u003c\/p\u003e \u003cp\u003e1.5.2 Proposed Methodology for FlipRec Model 16\u003c\/p\u003e \u003cp\u003e1.5.2.1 Module for Data Preparation 16\u003c\/p\u003e \u003cp\u003e1.5.2.2 FlipRec: Preferred Recommendation Model 19\u003c\/p\u003e \u003cp\u003e1.5.3 Using Brain Signal Technology, a Cognitively Aware Lecture Video Recommendation System in Flipped Learning 20\u003c\/p\u003e \u003cp\u003e1.5.3.1 Finding Successful Cognitive States with a Clustering Method 20\u003c\/p\u003e \u003cp\u003e1.5.3.2 Feature Derivation for Estimating Attention 22\u003c\/p\u003e \u003cp\u003e1.6 Result Analysis 23\u003c\/p\u003e \u003cp\u003e1.7 Conclusion and Future Research 25\u003c\/p\u003e \u003cp\u003eReferences 25\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Blockchain-Based Sustainable Supply Chain Management 31\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnuja Ajay, Saji M. S. and Subhasis Dash\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 32\u003c\/p\u003e \u003cp\u003e2.1.1 Significance of Blockchain for SCM 34\u003c\/p\u003e \u003cp\u003e2.1.2 Introduction to Blockchain Interoperability 35\u003c\/p\u003e \u003cp\u003e2.2 Blockchain for Supply Chain Management 35\u003c\/p\u003e \u003cp\u003e2.2.1 Characteristics and Requirements of Blockchain-Based Supply Chain 37\u003c\/p\u003e \u003cp\u003e2.2.1.1 Characteristics of Supply Chain 37\u003c\/p\u003e \u003cp\u003e2.2.1.2 Requirements of Supply Chain 40\u003c\/p\u003e \u003cp\u003e2.2.2 Blockchain-Based Data Sharing for Supply Chain 41\u003c\/p\u003e \u003cp\u003e2.2.3 Access Control and Trust Management in Blockchain- Based SCM 43\u003c\/p\u003e \u003cp\u003e2.2.3.1 Access Control Mechanisms in SCM 43\u003c\/p\u003e \u003cp\u003e2.2.3.2 Trust Management in Supply Chain 44\u003c\/p\u003e \u003cp\u003e2.3 Interoperability in Blockchain 45\u003c\/p\u003e \u003cp\u003e2.3.1 Overview of Blockchain Interoperability Approaches 45\u003c\/p\u003e \u003cp\u003e2.3.1.1 Public Connectors 45\u003c\/p\u003e \u003cp\u003e2.3.1.2 Blockchain of Blockchains (BoB) 46\u003c\/p\u003e \u003cp\u003e2.3.1.3 Hybrid Connectors 46\u003c\/p\u003e \u003cp\u003e2.3.2 Gateways for Interoperability and Manageability 48\u003c\/p\u003e \u003cp\u003e2.3.3 Interoperability Approaches 49\u003c\/p\u003e \u003cp\u003e2.4 Design Considerations and Open Challenges 50\u003c\/p\u003e \u003cp\u003e2.5 Summary 51\u003c\/p\u003e \u003cp\u003e2.5.1 Advantages of Blockchain for SSCM 51\u003c\/p\u003e \u003cp\u003e2.6 Scope of Future Work Emphasis 52\u003c\/p\u003e \u003cp\u003eReferences 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Revolutionizing Aquaculture With the Internet of Things (IoT): An Insightful Learning 59\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eArpita Nayak, Atmika Patnaik, Ipseeta Satpathy, Veena Goswami and B.C.M. Patnaik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 60\u003c\/p\u003e \u003cp\u003e3.2 Environmental Monitoring via IoT for Sustainable Aquaculture 63\u003c\/p\u003e \u003cp\u003e3.3 The Primacy of IoT in Enhancing Fish Health Monitoring 67\u003c\/p\u003e \u003cp\u003e3.4 Delving Into IoT: Improving Agricultural Water Quality Management 70\u003c\/p\u003e \u003cp\u003e3.5 Connecting the Dots: Using IoT Fish Behavior Monitoring to Improve Aquaculture Practices 74\u003c\/p\u003e \u003cp\u003e3.6 The Worldwide Deployment of IoT in Aquaculture: Advantages and Success Factors 79\u003c\/p\u003e \u003cp\u003e3.7 Conclusion 81\u003c\/p\u003e \u003cp\u003eAcknowledgment 81\u003c\/p\u003e \u003cp\u003eReferences 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Energy Consumption Optimization in Wireless Sensor Networks 87\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAvik Das, Shatyaki Ghosh and Arindam Basak\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 87\u003c\/p\u003e \u003cp\u003e4.1.1 WSN Application and Hardware Characteristics 90\u003c\/p\u003e \u003cp\u003e4.2 MAC Layer Approaches 93\u003c\/p\u003e \u003cp\u003e4.2.1 IEEE 802.15.4 Standard along with the ZigBee Technology 94\u003c\/p\u003e \u003cp\u003e4.2.2 Different Other MAC Approaches 95\u003c\/p\u003e \u003cp\u003e4.3 Routing Approaches 98\u003c\/p\u003e \u003cp\u003e4.4 Transmission Power Control Approaches 99\u003c\/p\u003e \u003cp\u003e4.5 Autonomic Approaches 102\u003c\/p\u003e \u003cp\u003e4.6 Application of ZigBee in a WSN 105\u003c\/p\u003e \u003cp\u003e4.7 WSN with Cloud Computing 106\u003c\/p\u003e \u003cp\u003e4.8 Final Considerations and Future Directions 109\u003c\/p\u003e \u003cp\u003eReferences 110\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Airline Prediction Using Customer Feedback and Rating Using Machine Learning and Deep Learning 115\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eCh Sambasiva Rao, Pabbathi Manobhi Ram, Viswanadhapalli Siva and Motakatla Satya Sai Krishna Reddy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 116\u003c\/p\u003e \u003cp\u003e5.1.1 Customer Ratings and Recommendation 116\u003c\/p\u003e \u003cp\u003e5.2 Literature Survey 117\u003c\/p\u003e \u003cp\u003e5.3 System Design 119\u003c\/p\u003e \u003cp\u003e5.4 Methodology 120\u003c\/p\u003e \u003cp\u003e5.4.1 Modules 120\u003c\/p\u003e \u003cp\u003e5.4.1.1 Data Collection 120\u003c\/p\u003e \u003cp\u003e5.4.1.2 Review-Based Airline Prediction 120\u003c\/p\u003e \u003cp\u003e5.4.1.3 Rating-Based Airline Prediction 121\u003c\/p\u003e \u003cp\u003e5.5 Algorithm Used: Random Forest, Convolutional Neural Network, and AdaBoost 121\u003c\/p\u003e \u003cp\u003e5.5.1 Random Forest System 121\u003c\/p\u003e \u003cp\u003e5.5.2 Convolutional 1D Neural Network–Based Training 122\u003c\/p\u003e \u003cp\u003e5.5.2.1 Sequential Model 122\u003c\/p\u003e \u003cp\u003e5.5.2.2 Add 1D Convolutional Layer 123\u003c\/p\u003e \u003cp\u003e5.5.2.3 Adding 1D Max Pooling Layer 123\u003c\/p\u003e \u003cp\u003e5.5.2.4 Adding Dense Layer 123\u003c\/p\u003e \u003cp\u003e5.5.2.5 Neural Network Training 123\u003c\/p\u003e \u003cp\u003e5.5.3 AdaBoost Algorithm 124\u003c\/p\u003e \u003cp\u003e5.6 Experimental Results and Evaluations 125\u003c\/p\u003e \u003cp\u003e5.7 Screenshots 126\u003c\/p\u003e \u003cp\u003e5.8 Conclusion 130\u003c\/p\u003e \u003cp\u003eReferences 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 The Breakthrough of Future Delivery: Delivery Robots 133\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAyushi Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 133\u003c\/p\u003e \u003cp\u003e6.2 Related Work 136\u003c\/p\u003e \u003cp\u003e6.3 Evolution of Delivery Robot 138\u003c\/p\u003e \u003cp\u003e6.4 Working Principal\/Model of Delivery Robots 141\u003c\/p\u003e \u003cp\u003e6.5 Benefits of Delivery Robots 143\u003c\/p\u003e \u003cp\u003e6.6 Applications of Delivery Robots 149\u003c\/p\u003e \u003cp\u003e6.7 Development Projects 153\u003c\/p\u003e \u003cp\u003e6.8 Challenging Issues with Delivery Robots 158\u003c\/p\u003e \u003cp\u003e6.9 Conclusion and Future Work 165\u003c\/p\u003e \u003cp\u003eReferences 166\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Emergence of Cloud Computing in IoT Applications 169\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePriyanshu Sonthalia and Doddi Puneet\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 170\u003c\/p\u003e \u003cp\u003e7.1.1 Characteristics of Cloud Computing 170\u003c\/p\u003e \u003cp\u003e7.1.2 Types of Cloud Deployment Models 171\u003c\/p\u003e \u003cp\u003e7.1.3 Categories of Cloud Computing Architectures 172\u003c\/p\u003e \u003cp\u003e7.1.4 Types of Cloud Service Models 173\u003c\/p\u003e \u003cp\u003e7.2 Benefits of IoT and Cloud Integration 174\u003c\/p\u003e \u003cp\u003e7.2.1 Scalability and Elasticity of Cloud Resources for Managing IoT Data 174\u003c\/p\u003e \u003cp\u003e7.2.2 Reduced Infrastructure Costs with Cloud-Based Solutions 174\u003c\/p\u003e \u003cp\u003e7.2.3 Improved Accessibility and Availability of IoT Services with Cloud Deployment 175\u003c\/p\u003e \u003cp\u003e7.2.4 Enhanced Processing Power and Analytics Capabilities with Cloud Computing 175\u003c\/p\u003e \u003cp\u003e7.2.5 Reduced Time to Market and Increased Innovation with Cloud-Based IoT Development 175\u003c\/p\u003e \u003cp\u003e7.3 Cloud-Based IoT Architecture 175\u003c\/p\u003e \u003cp\u003e7.3.1 Four Layers of Cloud-Based IoT Architecture 175\u003c\/p\u003e \u003cp\u003e7.3.2 Role of Gateways in Linking IoT Devices to the Cloud 176\u003c\/p\u003e \u003cp\u003e7.3.3 Overview of Cloud-Based IoT Platforms and Services 177\u003c\/p\u003e \u003cp\u003e7.3.4 Cloud-Based IoT Standards and Protocols, such as MQTT, CoAP, AMQP, and HTTP 177\u003c\/p\u003e \u003cp\u003e7.4 Cloud-Based IoT Applications 180\u003c\/p\u003e \u003cp\u003e7.5 Challenges in IoT Cloud Integration 181\u003c\/p\u003e \u003cp\u003e7.5.1 Security Risks and Challenges Associated with Cloud-Based IoT Solutions 181\u003c\/p\u003e \u003cp\u003e7.5.2 Latency and Bandwidth Constraints of IoT Systems Hosted in the Cloud 181\u003c\/p\u003e \u003cp\u003e7.5.3 Interoperability Issues Between Different IoT Devices and Cloud Platforms 182\u003c\/p\u003e \u003cp\u003e7.5.4 Legal and Regulatory Challenges Associated with IoT Using Cloud Solutions 182\u003c\/p\u003e \u003cp\u003e7.6 Open Issues and Research Directions 182\u003c\/p\u003e \u003cp\u003e7.6.1 Future Trends and Developments in Cloud-Based IoT Solutions 182\u003c\/p\u003e \u003cp\u003e7.6.2 Opportunities for Research in Cloud-Based IoT Solutions 182\u003c\/p\u003e \u003cp\u003e7.6.3 Overview of Emerging Cloud-Based IoT Standards and Protocols 183\u003c\/p\u003e \u003cp\u003e7.7 Case Study 1: Smart Home Automation Using Cloud-Based IoT 183\u003c\/p\u003e \u003cp\u003e7.8 Case Study 2: Industrial IoT Optimization Using Cloud-Based IoT 184\u003c\/p\u003e \u003cp\u003e7.9 Conclusion 185\u003c\/p\u003e \u003cp\u003eReferences 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Conceptual Assessment of Sensory Networks and Its Functional Aspects 189\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBarat Nikhita, Siddhant Prateek Mahanayak and Kunal Anand\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 189\u003c\/p\u003e \u003cp\u003e8.2 Evolution of IoT 191\u003c\/p\u003e \u003cp\u003e8.2.1 Phase 1: Early Adopters (Pre-2010) 192\u003c\/p\u003e \u003cp\u003e8.2.2 Phase 2: Connectivity and Smart Devices (2010–2015) 193\u003c\/p\u003e \u003cp\u003e8.2.3 Phase 3: Big Data and Cloud Computing (2015 to Present) 194\u003c\/p\u003e \u003cp\u003e8.2.4 Phase 4: Artificial Intelligence and Edge Computing (Present and Future) 195\u003c\/p\u003e \u003cp\u003e8.3 Features of IoT 196\u003c\/p\u003e \u003cp\u003e8.4 Architectural Framework of IoT 199\u003c\/p\u003e \u003cp\u003e8.4.1 Device Layer 200\u003c\/p\u003e \u003cp\u003e8.4.2 Network Layer 201\u003c\/p\u003e \u003cp\u003e8.4.3 Platform Layer 202\u003c\/p\u003e \u003cp\u003e8.4.4 Application Layer 203\u003c\/p\u003e \u003cp\u003e8.5 Components of IoT 204\u003c\/p\u003e \u003cp\u003e8.6 Applications of IoT 206\u003c\/p\u003e \u003cp\u003e8.7 Case Study 211\u003c\/p\u003e \u003cp\u003e8.7.1 Overview of Barcelona Smart City Project 211\u003c\/p\u003e \u003cp\u003e8.7.2 Methodology 212\u003c\/p\u003e \u003cp\u003e8.8 Conclusion 213\u003c\/p\u003e \u003cp\u003eReferences 214\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 System Security Using Artificial Intelligence and Reduction of Data Breach 221\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eM. Avrit, G. P. Siranjeevi, Shruti Mishra, Sandeep Kumar Satapathy, Priyanka Mishra, Pradeep Kumar Mallick and Gyoo Soo Chae\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 222\u003c\/p\u003e \u003cp\u003e9.2 Related Work 224\u003c\/p\u003e \u003cp\u003e9.3 Methodology 224\u003c\/p\u003e \u003cp\u003e9.3.1 Implementation of Socket Programming Concept 224\u003c\/p\u003e \u003cp\u003e9.3.2 Machine Learning 225\u003c\/p\u003e \u003cp\u003e9.3.3 Deep Learning 225\u003c\/p\u003e \u003cp\u003e9.3.4 Human Assistance 225\u003c\/p\u003e \u003cp\u003e9.4 Proposed Model 225\u003c\/p\u003e \u003cp\u003e9.5 Experimental Result\/Result Analysis 227\u003c\/p\u003e \u003cp\u003e9.6 Conclusion and Future Work 231\u003c\/p\u003e \u003cp\u003eReferences 231\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Mitigating DDoS Attacks: Empowering Network Infrastructure Resilience with AI and ML 233\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTeja Pasonri, Saurav Singh, Vedant Shirapure, Sandeep Kumar Satapathy, Sung-Bae Cho, Shruti Mishra and Pradeep Kumar Mallick\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 234\u003c\/p\u003e \u003cp\u003e10.1.1 Categories of DDoS Attack 235\u003c\/p\u003e \u003cp\u003e10.1.1.1 SYN Flood Attacks 235\u003c\/p\u003e \u003cp\u003e10.1.1.2 UDP Flood Attacks 235\u003c\/p\u003e \u003cp\u003e10.1.1.3 MSSQL Attacks 235\u003c\/p\u003e \u003cp\u003e10.1.1.4 LDAP Attacks 235\u003c\/p\u003e \u003cp\u003e10.1.1.5 Portmap Attacks 236\u003c\/p\u003e \u003cp\u003e10.1.1.6 NetBIOS Attacks 236\u003c\/p\u003e \u003cp\u003e10.1.2 Harnessing Machine Learning for DDoS Threat Detection 236\u003c\/p\u003e \u003cp\u003e10.1.3 AI Models for DDoS Threat Detection 236\u003c\/p\u003e \u003cp\u003e10.1.4 Beyond Classification: AI for Real-Time Detection and Mitigation 237\u003c\/p\u003e \u003cp\u003e10.1.5 Collaboration and Knowledge Sharing 237\u003c\/p\u003e \u003cp\u003e10.2 Related Work 237\u003c\/p\u003e \u003cp\u003e10.3 Methodology 239\u003c\/p\u003e \u003cp\u003e10.3.1 Pseudocode-1: Jupyter Project Code 240\u003c\/p\u003e \u003cp\u003e10.3.2 Pseudocode-2: Project KNN Model 241\u003c\/p\u003e \u003cp\u003e10.3.3 Hyperparameter Tuning and Evaluation 242\u003c\/p\u003e \u003cp\u003e10.3.4 Enhancing Model Accuracy 242\u003c\/p\u003e \u003cp\u003e10.3.5 Ping Request and DDoS Attack 242\u003c\/p\u003e \u003cp\u003e10.4 Proposed Model 243\u003c\/p\u003e \u003cp\u003e10.5 Experimental Result\/Result Analysis 245\u003c\/p\u003e \u003cp\u003e10.5.1 Demo of DDoS Attack 245\u003c\/p\u003e \u003cp\u003e10.5.2 Packet Sniffing and Detecting Traffic 246\u003c\/p\u003e \u003cp\u003e10.5.3 Accuracy Graph 246\u003c\/p\u003e \u003cp\u003e10.5.4 Precision Graph 247\u003c\/p\u003e \u003cp\u003e10.6 Conclusion\/Future Work 248\u003c\/p\u003e \u003cp\u003eReferences 248\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 CyberEDU: An Interactive Educational Tool for DDoS Attack Simulation and Prevention 251\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePulkit Srivastava, Vedant Shah, Priyanshu Singh, Sandeep Kumar Satapathy, Sung-Bae Cho, Shruti Mishra and Pradeep Kumar Mallick\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 252\u003c\/p\u003e \u003cp\u003e11.2 Related Work 255\u003c\/p\u003e \u003cp\u003e11.3 Methodology 257\u003c\/p\u003e \u003cp\u003e11.4 Proposed Model 260\u003c\/p\u003e \u003cp\u003e11.5 Experimental Result\/Result Analysis 263\u003c\/p\u003e \u003cp\u003e11.6 Conclusion and Future Work 267\u003c\/p\u003e \u003cp\u003eReferences 267\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Resource Management and Performance Optimization in Constraint Network Systems 269\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAmarendra Kumar Mohanty\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 270\u003c\/p\u003e \u003cp\u003e12.2 Resource Allocation Principles 271\u003c\/p\u003e \u003cp\u003e12.3 Network Capacity and Utilization 274\u003c\/p\u003e \u003cp\u003e12.4 Performance Optimization Strategies 280\u003c\/p\u003e \u003cp\u003e12.4.1 Resource Management in Physical Networks 281\u003c\/p\u003e \u003cp\u003e12.4.2 Resource Management in Virtual Networks 297\u003c\/p\u003e \u003cp\u003e12.4.3 Resource Management in Software-Defined Networking (SDN) 300\u003c\/p\u003e \u003cp\u003e12.5 Real-World Applications 302\u003c\/p\u003e \u003cp\u003e12.5.1 Data Plane Development Kit Libraries 306\u003c\/p\u003e \u003cp\u003e12.5.2 Virtual Machine Device Queues (VMDQ) 309\u003c\/p\u003e \u003cp\u003e12.6 Conclusion and Future Directions 311\u003c\/p\u003e \u003cp\u003eReferences 312\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Resource-Constrained Network Management Using Software-Defined Networks 315\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSayan Bhattacharyya, Manas Ranjan Lenka and Subhasis Dash\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 315\u003c\/p\u003e \u003cp\u003e13.2 Software-Defined Network Architecture and Its Key Components 317\u003c\/p\u003e \u003cp\u003e13.2.1 Application Plane 319\u003c\/p\u003e \u003cp\u003e13.2.1.1 Network Application 320\u003c\/p\u003e \u003cp\u003e13.2.1.2 Language-Level Virtualization 320\u003c\/p\u003e \u003cp\u003e13.2.2 Control Plane 320\u003c\/p\u003e \u003cp\u003e13.2.2.1 Network Operating System (NOS) 320\u003c\/p\u003e \u003cp\u003e13.2.2.2 Network Hypervisor 320\u003c\/p\u003e \u003cp\u003e13.2.3 Data Plane 321\u003c\/p\u003e \u003cp\u003e13.2.3.1 Network Infrastructure 321\u003c\/p\u003e \u003cp\u003e13.2.4 SDN Protocols 321\u003c\/p\u003e \u003cp\u003e13.2.4.1 Northbound Protocol 321\u003c\/p\u003e \u003cp\u003e13.2.4.2 Southbound Protocol 322\u003c\/p\u003e \u003cp\u003e13.2.4.3 Eastbound Protocol 322\u003c\/p\u003e \u003cp\u003e13.2.4.4 Westbound Protocol 323\u003c\/p\u003e \u003cp\u003e13.2.5 SDN Workflows 324\u003c\/p\u003e \u003cp\u003e13.3 Challenges and Opportunities of SDN in Resource- Constrained Scenarios 326\u003c\/p\u003e \u003cp\u003e13.4 State-of-the-Art Techniques and Tools for Efficient Network Resource Management in SDN Environments 327\u003c\/p\u003e \u003cp\u003e13.5 Performance of the Existing Techniques and Tools with Use Case 329\u003c\/p\u003e \u003cp\u003e13.6 Conclusion and Future Scope 330\u003c\/p\u003e \u003cp\u003eReferences 331\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Vehicles Smoke Monitoring Using Internet of Things and Machine Learning 337\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDhavakumar P. and Selvakumar Samuel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 337\u003c\/p\u003e \u003cp\u003e14.2 Vehicle CO 2 Emissions 338\u003c\/p\u003e \u003cp\u003e14.2.1 Impacts of CO 2 Emissions 339\u003c\/p\u003e \u003cp\u003e14.3 Recommended Solutions with Internet of Things 340\u003c\/p\u003e \u003cp\u003e14.3.1 IoT System and CO 2 Sensors 340\u003c\/p\u003e \u003cp\u003e14.3.2 Benefits of the IoT System 342\u003c\/p\u003e \u003cp\u003e14.3.3 Air Quality Monitoring System (AQMS) 343\u003c\/p\u003e \u003cp\u003e14.4 ml Algorithms 346\u003c\/p\u003e \u003cp\u003e14.4.1 K-Means Algorithm (KM) 346\u003c\/p\u003e \u003cp\u003e14.4.2 Decision Tree Algorithm (DT) 347\u003c\/p\u003e \u003cp\u003e14.4.3 Naive Bayes Algorithm (NB) 347\u003c\/p\u003e \u003cp\u003e14.4.4 Controlling Carbon Unlimited Flow Operation with Machine Learning Approach (CULTML) 347\u003c\/p\u003e \u003cp\u003e14.5 Proposed System Architectures and Designs 348\u003c\/p\u003e \u003cp\u003e14.5.1 Vehicular Unit 349\u003c\/p\u003e \u003cp\u003e14.5.2 Software Unit 350\u003c\/p\u003e \u003cp\u003e14.5.3 Road Transport Office (RTO) Unit 351\u003c\/p\u003e \u003cp\u003e14.6 Logical Design of the Proposed System 352\u003c\/p\u003e \u003cp\u003e14.6.1 Summation Detector Using Artificial Intelligence 352\u003c\/p\u003e \u003cp\u003e14.6.2 Digit Recognition 352\u003c\/p\u003e \u003cp\u003e14.7 Experimental Results 354\u003c\/p\u003e \u003cp\u003e14.8 Physical Design of the Proposed System 356\u003c\/p\u003e \u003cp\u003e14.9 Conclusion 357\u003c\/p\u003e \u003cp\u003eReferences 357\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Enhancing Home Security through IoT Innovation: Recommendations for Biometric Door Lock System to Deter Break-Ins 359\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMuhammad Ehsan Rana, Kamalanathan Shanmugam, Lim Enya and Hrudaya Kumar Tripathy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 360\u003c\/p\u003e \u003cp\u003e15.2 Literature Review 361\u003c\/p\u003e \u003cp\u003e15.2.1 Home Security Concerns in Malaysia 362\u003c\/p\u003e \u003cp\u003e15.2.2 Introduction to Biometric Solutions 363\u003c\/p\u003e \u003cp\u003e15.2.3 Enhancing Biometrics with Machine Learning 364\u003c\/p\u003e \u003cp\u003e15.2.4 Biometrics in the Realm of Smart Home Security 365\u003c\/p\u003e \u003cp\u003e15.2.5 Review of Existing Commercial Systems 367\u003c\/p\u003e \u003cp\u003e15.2.5.1 Samsung Smart Door Lock 367\u003c\/p\u003e \u003cp\u003e15.2.5.2 Philips EasyKey 369\u003c\/p\u003e \u003cp\u003e15.2.5.3 Comparison of Systems 371\u003c\/p\u003e \u003cp\u003e15.3 Recommendations for the Implementation of the Proposed Biometric Door Lock System 372\u003c\/p\u003e \u003cp\u003e15.3.1 Software Requirements 373\u003c\/p\u003e \u003cp\u003e15.3.2 Key Hardware Requirements 374\u003c\/p\u003e \u003cp\u003e15.3.2.1 Arduino Nano 374\u003c\/p\u003e \u003cp\u003e15.3.2.2 DFRobot HuskyLens 375\u003c\/p\u003e \u003cp\u003e15.3.2.3 DFRobot UART Fingerprint Scanner 375\u003c\/p\u003e \u003cp\u003e15.3.2.4 Five-Volt Single-Channel Relay Module 376\u003c\/p\u003e \u003cp\u003e15.3.2.5 12VDC Solenoid Lock 376\u003c\/p\u003e \u003cp\u003e15.3.3 Workflow of the Proposed System 376\u003c\/p\u003e \u003cp\u003e15.3.4 Key Features of the Proposed System 378\u003c\/p\u003e \u003cp\u003e15.3.5 Testing the Biometric Door Lock System 380\u003c\/p\u003e \u003cp\u003e15.3.5.1 Fingerprint Authentication Test 380\u003c\/p\u003e \u003cp\u003e15.3.5.2 Facial Recognition Test 381\u003c\/p\u003e \u003cp\u003e15.3.5.3 Dual Authentication Test 382\u003c\/p\u003e \u003cp\u003e15.3.5.4 Access Log Test 384\u003c\/p\u003e \u003cp\u003e15.3.5.5 Mobile Application Integration Test 385\u003c\/p\u003e \u003cp\u003e15.3.5.6 Scalability Test 386\u003c\/p\u003e \u003cp\u003e15.3.5.7 Accuracy Result Analysis 387\u003c\/p\u003e \u003cp\u003e15.4 Conclusion and Future Recommendations 389\u003c\/p\u003e \u003cp\u003eReferences 390\u003c\/p\u003e \u003cp\u003eIndex 393\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433207230744,"sku":"9781394212569","price":165.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394212569.jpg?v=1784851829","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/sustainable-resource-management-in-next-generation-computational-constrained-networks-hardback-9781394212569","provider":"Freshly Printed Books","version":"1.0","type":"link"}