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.
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 1.3 Realizing NS Potential with SDN and NFV 13 1.4 Artificial Intelligence: Pivotal Role in Networking Transformation 15 1.5 Navigating Challenges and Solutions 23 1.6 Conclusion 26 2 OctoEdge: An Octopus-Inspired Adaptive Edge Computing Architecture 35 Sashi Tarun 2.1 Introduction 36 2.2 Problem Statement 39 2.3 Motivations 40 2.4 Related Work 41 2.5 OctoEdge Proposed Architecture 45 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 3 Development of Optimized Machine Learning Oriented Models 71 Ratnesh Kumar Dubey, Dilip Kumar Choubey and Shubha Mishra 3.1 Introduction 72 3.2 Literature Review 76 3.3 Problem Definition 78 3.4 Proposed Work 80 3.5 Experimental Analysis 86 3.6 Conclusion 90 3.7 Future Scope 91 4 Leveraging Multimodal Data and Deep Learning for Enhanced Stock Market Prediction 93 Pinky Gangwani and Vikas Panthi 4.1 Introduction 94 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 4.4 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 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 5.2 Literature Review 131 5.3 Methodology 135 5.4 Proposed Model 137 5.5 Implementations and Results 142 5.6 Conclusion 149 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 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 7.4 Proposed Model Architecture 190 7.5 Results and Discussions 193 7.6 Conclusion 198 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 8.4 Result 221 8.5 Conclusion 225 9 Fog Computing Security Concerns in Healthcare Using IoT and Blockchain 231 Ruchi Mittal, Shikha Gupta and Shefali Arora 9.1 Introduction 232 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 9.7 Conclusion and Future Work 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 10.2 Data Analytics and Decision Support 267 10.3 Challenges and Solutions Smart Agriculture 270 10.4 AI for Soybean (Glycine max) Crop 275 10.5 Result Discussion 281 10.6 Conclusion 283 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 11.2 Background 292 11.3 Population-Based Proposed Optimization Approach 298 11.4 Experimental Setup and Result Analysis of Proposed Work (LR-GPSO) 307 11.5 Conclusion and Future Work 313 12 SDN Deployed Secure Application Design Framework for IoT Using Game Theory 317 Madhukrishna Priyadarsini and Padmalochan Bera 12.1 Introduction 318 12.2 Background Study 322 12.3 SDN-Deployed Design Framework for IoT Using Game-Theoretic Solutions 324 12.4 Case Study: SDN Deployed Design Framework in Robot Manufacturing Industry 334 12.5 Discussion 338 12.6 Conclusion 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 13.2 Related Work 348 13.3 The Recommended Architecture’s Methodology 354 13.4 Key Services That are Suggested 360 13.5 Modelling and Assessment 366 13.6 Conclusion and Future Work 373 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 14.1 Introduction 382 14.2 Related Works 384 14.3 Methods and Dataset 386 14.4 Proposed Algorithm 391 14.5 Results and Discussions 392 14.5.1 Classified Crop Map 394 14.6 Conclusion 395 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 15.3 Related Work 409 15.4 IFA Feature Selection and Detection 411 15.5 Conclusion 428 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
Mohit Kumar, PhD, is an assistant professor in the Department of Information Technology at Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, India. He has published more than 60 research articles in reputed international journals and conferences and served as a session chair and keynote speaker for many international conferences and webinars in India. His research interests include cloud computing, soft computing, fog and edge computing, optimization algorithms, artificial Intelligence, and Internet of Things. Gautam Srivastava, PhD, is a professor at Brandon University, Manitoba, Canada with over eight years of academic experience. He has published more than 150 papers in various international journals and conferences and serves as an editor for several international journals. In addition to his written work, he has delivered guest lectures in Taiwan and the Czech Republic. His research interests include data mining, big data, cloud computing, Internet of Things, and cryptography. Ashutosh Kumar Singh, PhD, is an assistant professor in the Department of Computer Science and Engineering, United College of Engineering and Research Allahabad, India. He has published over 25 papers in reputed international journals and conferences and is a reviewer for various reputed journals, conferences, and books. His research interests include network optimization, software-defined networking, machine learning, Internet of Things, and edge computing. Kalka Dubey, PhD, is an assistant professor in the Department of Computer Science and Engineering, Rajiv Gandhi Institute of Petroleum Technology, Amethi, India. He has published more than 20 articles in international journals and conferences. His research interests include task scheduling, virtual machine placement and allocation in cloud-based systems, quantification and monitoring of security metrics, soft computing, and enforcing security in cloud environments.
