Preface xv -- 1 Enhancing Digital Learning Pedagogy for Lecture Video Recommendation Using Brain Wave Signal 1 Rabi Shaw, Simanjeet Kalia and Sourabh Mohanty -- 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 Anuja Ajay, Saji M. S. and Subhasis Dash -- 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 Arpita Nayak, Atmika Patnaik, Ipseeta Satpathy, Veena Goswami and B.C.M. Patnaik -- 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 Avik Das, Shatyaki Ghosh and Arindam Basak -- 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 Ch Sambasiva Rao, Pabbathi Manobhi Ram, Viswanadhapalli Siva and Motakatla Satya Sai Krishna Reddy -- 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 Ayushi Gupta -- 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 Priyanshu Sonthalia and Doddi Puneet -- 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 Barat Nikhita, Siddhant Prateek Mahanayak and Kunal Anand -- 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 M. Avrit, G. P.

Siranjeevi, Shruti Mishra, Sandeep Kumar Satapathy, Priyanka Mishra, Pradeep Kumar Mallick and Gyoo Soo Chae -- 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 Teja Pasonri, Saurav Singh, Vedant Shirapure, Sandeep Kumar Satapathy, Sung-Bae Cho, Shruti Mishra and Pradeep Kumar Mallick -- 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 Mi ...