The book offers cutting-edge insights into AI-driven optimization algorithms and their crucial role in enhancing real-time applications within fog and Edge IoT networks and addresses current challenges and future opportunities in this rapidly evolving field. This book focuses on artificial intelligence-induced adaptive optimization algorithms in fog and Edge IoT networks. Artificial intelligence, fog, and edge computing, together with IoT, are the next generation of paradigms offering services to people to improve existing services for real-time applications. Over the past few years, there has been rigorous growth in AI-based optimization algorithms and Edge and IoT paradigms. However, despite several applications and advancements, there are still some limitations and challenges to address including security, adaptive, complex, and heterogeneous IoT networks, protocols, intelligent offloading decisions, latency, energy consumption, service allocation, and network lifetime. This volume aims to encourage industry professionals to initiate a set of architectural strategies to solve open research computation challenges. The authors achieve this by defining and exploring emerging trends in advanced optimization algorithms, AI techniques, and fog and Edge technologies for IoT applications. Solutions are also proposed to reduce the latency of real-time applications and improve other quality of service parameters using adaptive optimization algorithms in fog and Edge paradigms. The book provides information on the full potential of IoT-based intelligent computing paradigms for the development of suitable conceptual and technological solutions using adaptive optimization techniques when faced with challenges. Additionally, it presents in-depth discussions in emerging interdisciplinary themes and applications reflecting the advancements in optimization algorithms and their usage in computing paradigms. Audience Researchers, industrial engineers, and graduate/post-graduate students in software engineering, computer science, electronic and electrical engineering, data analysts, and security professionals working in the fields of intelligent computing paradigms and similar areas.

Table of Contents:
Preface xv Acknowledgement xvii 1 Navigating Next-Generation Network Architecture: Unleashing the Power of SDN, NFV, NS, and AI Convergence 1 Monika Dubey, Snehlata, Ashutosh Kumar Singh, Richa Mishra and Mohit Kumar 1.1 Introduction 2 1.2 Revolutionizing Infrastructure with SDN, NFV, and NS 4 1.2.1 SDN: Definition and Architecture 6 1.2.2 NFV: Definition and Architecture 9 1.2.3 NS: Conceptual Abstractions 11 1.3 Realizing NS Potential with SDN and NFV 13 1.4 Artificial Intelligence: Pivotal Role in Networking Transformation 15 1.4.1 Supervised Learning 16 1.4.2 Unsupervised Learning 18 1.4.3 Reinforcement Learning 18 1.4.4 Deep Learning 21 1.5 Navigating Challenges and Solutions 23 1.5.1 Performance Issues in Network Structure 23 1.5.2 Management and Orchestration Issues 24 1.5.3 Security and Privacy 24 1.5.4 New Business Models 25 1.6 Conclusion 26 Disclosure Statement 26 References 26 2 OctoEdge: An Octopus-Inspired Adaptive Edge Computing Architecture 35 Sashi Tarun 2.1 Introduction 36 2.1.1 Edge Computing as Resource Manager 36 2.1.2 Edge Computing Hurdles 37 2.1.3 Edge Computing and the Need for Adaptability 38 2.2 Problem Statement 39 2.3 Motivations 40 2.4 Related Work 41 2.5 OctoEdge Proposed Architecture 45 2.5.1 OctoEdge Working Principles 48 2.5.2 Benefits of OctoEdge 49 2.6 OctoEdge Architecture Functional Components 53 2.7 Results and Discussion 59 2.8 OctoEdge Architecture: Scope and Scientific Merits 60 2.9 Use Cases and Applications 64 2.10 Challenges and Future Directions 68 2.11 Conclusion 68 References 69 3 Development of Optimized Machine Learning Oriented Models 71 Ratnesh Kumar Dubey, Dilip Kumar Choubey and Shubha Mishra 3.1 Introduction 72 3.1.1 NSL-KDD Dataset 75 3.2 Literature Review 76 3.3 Problem Definition 78 3.4 Proposed Work 80 3.4.1 Machine Learning 82 3.5 Experimental Analysis 86 3.6 Conclusion 90 3.7 Future Scope 91 References 91 4 Leveraging Multimodal Data and Deep Learning for Enhanced Stock Market Prediction 93 Pinky Gangwani and Vikas Panthi 4.1 Introduction 94 4.1.1 Motivation and Contribution 96 4.1.2 Rationale for Selecting the Methods 98 4.2 Literature Review 100 4.3 Proposed Design of an Efficient Model that Leverages Multimodal Data and Deep Learning for Enhanced Stock Market Prediction 107 4.3.1 Discussion on Selection Criteria 114 4.4 Statistical Analysis and Comparison 116 4.5 Acknowledging Limitations and Potential Challenges 122 4.6 Mitigation Strategies and Future Directions 123 4.7 Conclusion 124 4.8 Future Scope 125 References 125 5 Context Dependent Sentiments Analysis Using Machine Learning 129 Mahima Shanker Pandey, Bihari Nandan Pandey, Abhishek Singh, Ashish Kumar Mishra and Brijesh Pandey 5.1 Introduction 130 5.1.1 Motivation 131 5.2 Literature Review 131 5.2.1 Text Sentiment 132 5.2.2 Audio Sentiment 132 5.2.3 Video Sentiment 133 5.3 Methodology 135 5.3.1 System Architecture 135 5.4 Proposed Model 137 5.4.1 Proposed Algorithm 137 5.4.2 Data Set Sources 138 5.4.3 Text Sentiment 140 5.4.4 Audio Sentiment 141 5.4.5 Video Sentiment 142 5.5 Implementations and Results 142 5.5.1 Results 142 5.5.2 Text Sentiment 143 5.5.3 Audio Sentiment 144 5.5.4 Video Sentiment 146 5.5.5 Applications 149 5.6 Conclusion 149 References 150 6 Thyroid Cancer Prediction Using Optimizations 153 Swati Sharma, Vijay Kumar Sharma, Punit Mittal, Pradeep Pant and Nitin Rakesh 6.1 Introduction 154 6.2 Background and Related Work 155 6.3 Proposed Methodology 160 6.4 Architecture 165 6.5 Materials and Methods 169 6.6 Results and Discussion 171 6.7 Conclusion 175 References 177 7 An LSTM-Oriented Approach for Next Word Prediction Using Deep Learning 181 Nidhi Shukla, Ashutosh Kumar Singh, Vijay Kumar Dwivedi, Pallavi Shukla, Jeetesh Srivastava and Vivek Srivastava 7.1 Introduction 182 7.2 Related Work 184 7.3 Design and Implementation 186 7.3.1 Background 186 7.4 Proposed Model Architecture 190 7.4.1 Experimental Setup 192 7.4.2 Dataset Specification 192 7.5 Results and Discussions 193 7.6 Conclusion 198 References 199 8 Churn Prediction in Social Networks Using Modified BiLSTM-CNN Model 203 Himanshu Rai and Jyoti Kesarwani 8.1 Introduction 204 8.2 Customer Behavior in Social Networks 209 8.3 Proposed Methodology 218 8.3.1 Churn Dataset Acquisition 218 8.3.2 Data Preprocessing 220 8.3.3 Proposed Model 220 8.4 Result 221 8.5 Conclusion 225 References 226 9 Fog Computing Security Concerns in Healthcare Using IoT and Blockchain 231 Ruchi Mittal, Shikha Gupta and Shefali Arora 9.1 Introduction 232 9.1.1 Types of Security Concerns in Healthcare 236 9.2 Related Work 239 9.3 Open Questions and Research Challenges 241 9.4 Problem Definition 242 9.5 Objectives 242 9.6 Research Methodology 243 9.6.1 The Three-Tier Blockchain Design 243 9.6.2 System Architecture 243 9.6.3 Workflow in Different Scenarios 245 9.7 Conclusion and Future Work 249 References 249 10 Smart Agriculture Revolution: Cloud and IoT-Based Solutions for Sustainable Crop Management and Precision Farming 253 Shrawan Kumar Sharma 10.1 Introduction 255 10.1.1 IoT in Agriculture 257 10.1.2 Cloud Computing in Agriculture 259 10.1.3 Precision Farming 263 10.1.4 Sustainable Agricultural and Remote Sensing 265 10.2 Data Analytics and Decision Support 267 10.2.1 Remote Monitoring 269 10.3 Challenges and Solutions Smart Agriculture 270 10.3.1 (AI) Approach in Agriculture and Needs 270 10.3.2 Needs of AI Farm 273 10.3.3 Role of AI in Agriculture 274 10.4 AI for Soybean (Glycine max) Crop 275 10.4.1 Soybean Disease Image Acquisition and Pretreatment 276 10.5 Result Discussion 281 10.5.1 Emerging Trends and Technologies in Smart Agriculture 281 10.6 Conclusion 283 References 285 11 Greedy Particle Swarm Optimization Approach Using Leaky ReLU Function for Minimum Spanning Tree Problem 289 Ashish Kumar Singh and Anoj Kumar 11.1 Introduction 290 11.1.1 Goal 291 11.1.2 Research Contribution are Below Listed 292 11.2 Background 292 11.2.1 Minimum Spanning Tree 294 11.2.2 Particle Swarm Optimization 296 11.2.3 Firefly Algorithm 297 11.2.4 Leaky ReLU Activation Function 298 11.3 Population-Based Proposed Optimization Approach 298 11.3.1 Motivation 299 11.3.2 Greedy Particle Swarm Optimization Using Leaky ReLU (LR-GPSO) 300 11.4 Experimental Setup and Result Analysis of Proposed Work (LR-GPSO) 307 11.4.1 Complexity 307 11.4.2 Simulation Experiments 308 11.4.3 Convergence Curve 311 11.5 Conclusion and Future Work 313 References 314 12 SDN Deployed Secure Application Design Framework for IoT Using Game Theory 317 Madhukrishna Priyadarsini and Padmalochan Bera 12.1 Introduction 318 12.1.1 IoT Overview 318 12.1.2 SDN Overview 319 12.1.3 Game Theory Overview 321 12.2 Background Study 322 12.2.1 IoT Security Using SDN 322 12.2.2 IoT Security Using Game Theory 323 12.3 SDN-Deployed Design Framework for IoT Using Game-Theoretic Solutions 324 12.3.1 Trust Verification 324 12.4 Case Study: SDN Deployed Design Framework in Robot Manufacturing Industry 334 12.4.1 Working Procedure of a Robot Manufacturing Industry 334 12.4.2 Integration of SDN-Deployed Design Framework in Robot Manufacturing Industry 335 12.4.3 Experimental Results 336 12.5 Discussion 338 12.6 Conclusion 339 References 339 13 Framework for PLM in Industry 4.0 Based on Industrial Blockchain 341 Ali Zaheer Agha, Rajesh Kumar Shukla, Ratnesh Mishra and Ravi Shankar Shukla 13.1 Introduction 342 13.1.1 What is Blockchain? 343 13.1.2 Blockchain Technology’s Integration with Industry 4.0 343 13.1.3 Blockchain Applications in Industry 4.0 343 13.1.4 A Consensus Algorithm 344 13.1.5 Product Lifecycle Management 345 13.1.6 Benefits of Smart Contracts in Addressing PLM Challenges 347 13.2 Related Work 348 13.2.1 Product Lifecycle Management 349 13.2.2 Industrial Blockchain 351 13.2.3 The On-Chain vs. Off-Chain Principle 353 13.3 The Recommended Architecture’s Methodology 354 13.3.1 The Suggested Platform’s Architecture 354 13.3.2 The Suggested Platform’s Technological Solution 358 13.4 Key Services That are Suggested 360 13.4.1 A Co-Creation Service Enabled by Blockchain 360 13.4.2 Blockchain-Enabled QAT2 Service 363 13.4.3 Proactive Upkeep Service Facilitated by Blockchain 364 13.4.4 Smart Recycling Program Driven by Blockchain 365 13.5 Modelling and Assessment 366 13.5.1 Overview of the Investigation 366 13.5.2 Experimental Evaluation and Comparison 368 13.5.3 Discussion 372 13.6 Conclusion and Future Work 373 A Statement of Competing Interests 374 References 375 14 Machine Learning Enabled Smart Agriculture Classification Technique for Edge Devices Using Remote Sensing Platform 381 Priyanka Gupta, Suraj Kumar Singh, Neetish Kumar and Bhavna Thakur List of Abbreviations 382 14.1 Introduction 382 14.2 Related Works 384 14.3 Methods and Dataset 386 14.3.1 Research Area and Dataset 386 14.3.2 Pre-Processing and Image Dataset 387 14.3.3 Classifiers 390 14.4 Proposed Algorithm 391 14.5 Results and Discussions 392 14.5.1 Classified Crop Map 394 14.6 Conclusion 395 References 396 15 A Lightweight Intelligent Detection Approach for Interest Flooding Attack 401 Naveen Kumar, Brijendra Pratap Singh and Rohit 15.1 Introduction 402 15.2 NDN Background 405 15.2.1 NDN Architecture 405 15.2.2 NDN Security 408 15.3 Related Work 409 15.4 IFA Feature Selection and Detection 411 15.4.1 IFA Modelling 412 15.4.2 Data Collection 413 15.4.3 Balancing the Dataset 414 15.4.4 Feature Selection 415 15.4.5 Dimensionality Reduction 421 15.4.6 Classification 424 15.5 Conclusion 428 References 429 16 An Internet of Vehicles Model Architecture with Seven Layers 433 Sujata Negi Thakur, Manisha Koranga, Sandeep Abhishek, Richa Pandey and Mayurika Joshi 16.1 Introduction 434 16.2 Literature Review 435 16.3 Proposed Architecture of Internet of Vehicles 439 16.4 Applications, Characteristics, and Challenges of the Internet of Vehicles (IoV) 451 Conclusion 455 References 455 Index 457