AI-Based Advanced Optimization Techniques for Edge Computing

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.

• Preface xvAcknowledgement xvii1 Navigating Next-Generation Network Architecture: Unleashing the Power of SDN, NFV, NS, and AI Convergence 1Monika Dubey, Snehlata, Ashutosh Kumar Singh, Richa Mishra and Mohit Kumar1.1 Introduction 21.2 Revolutionizing Infrastructure with SDN, NFV, and NS 41.2.1 SDN: Definition and Architecture 61.2.2 NFV: Definition and Architecture 91.2.3 NS: Conceptual Abstractions 111.3 Realizing NS Potential with SDN and NFV 131.4 Artificial Intelligence: Pivotal Role in Networking Transformation 151.4.1 Supervised Learning 161.4.2 Unsupervised Learning 181.4.3 Reinforcement Learning 181.4.4 Deep Learning 211.5 Navigating Challenges and Solutions 231.5.1 Performance Issues in Network Structure 231.5.2 Management and Orchestration Issues 241.5.3 Security and Privacy 241.5.4 New Business Models 251.6 Conclusion 26Disclosure Statement 26References 262 OctoEdge: An Octopus-Inspired Adaptive Edge Computing Architecture 35Sashi Tarun2.1 Introduction 362.1.1 Edge Computing as Resource Manager 362.1.2 Edge Computing Hurdles 372.1.3 Edge Computing and the Need for Adaptability 382.2 Problem Statement 392.3 Motivations 402.4 Related Work 412.5 OctoEdge Proposed Architecture 452.5.1 OctoEdge Working Principles 482.5.2 Benefits of OctoEdge 492.6 OctoEdge Architecture Functional Components 532.7 Results and Discussion 592.8 OctoEdge Architecture: Scope and Scientific Merits 602.9 Use Cases and Applications 642.10 Challenges and Future Directions 682.11 Conclusion 68References 693 Development of Optimized Machine Learning Oriented Models 71Ratnesh Kumar Dubey, Dilip Kumar Choubey and Shubha Mishra3.1 Introduction 723.1.1 NSL-KDD Dataset 753.2 Literature Review 763.3 Problem Definition 783.4 Proposed Work 803.4.1 Machine Learning 823.5 Experimental Analysis 863.6 Conclusion 903.7 Future Scope 91References 914 Leveraging Multimodal Data and Deep Learning for Enhanced Stock Market Prediction 93Pinky Gangwani and Vikas Panthi4.1 Introduction 944.1.1 Motivation and Contribution 964.1.2 Rationale for Selecting the Methods 984.2 Literature Review 1004.3 Proposed Design of an Efficient Model that Leverages Multimodal Data and Deep Learning for Enhanced Stock Market Prediction 1074.3.1 Discussion on Selection Criteria 1144.4 Statistical Analysis and Comparison 1164.5 Acknowledging Limitations and Potential Challenges 1224.6 Mitigation Strategies and Future Directions 1234.7 Conclusion 1244.8 Future Scope 125References 1255 Context Dependent Sentiments Analysis Using Machine Learning 129Mahima Shanker Pandey, Bihari Nandan Pandey, Abhishek Singh, Ashish Kumar Mishra and Brijesh Pandey5.1 Introduction 1305.1.1 Motivation 1315.2 Literature Review 1315.2.1 Text Sentiment 1325.2.2 Audio Sentiment 1325.2.3 Video Sentiment 1335.3 Methodology 1355.3.1 System Architecture 1355.4 Proposed Model 1375.4.1 Proposed Algorithm 1375.4.2 Data Set Sources 1385.4.3 Text Sentiment 1405.4.4 Audio Sentiment 1415.4.5 Video Sentiment 1425.5 Implementations and Results 1425.5.1 Results 1425.5.2 Text Sentiment 1435.5.3 Audio Sentiment 1445.5.4 Video Sentiment 1465.5.5 Applications 1495.6 Conclusion 149References 1506 Thyroid Cancer Prediction Using Optimizations 153Swati Sharma, Vijay Kumar Sharma, Punit Mittal, Pradeep Pant and Nitin Rakesh6.1 Introduction 1546.2 Background and Related Work 1556.3 Proposed Methodology 1606.4 Architecture 1656.5 Materials and Methods 1696.6 Results and Discussion 1716.7 Conclusion 175References 1777 An LSTM-Oriented Approach for Next Word Prediction Using Deep Learning 181Nidhi Shukla, Ashutosh Kumar Singh, Vijay Kumar Dwivedi, Pallavi Shukla, Jeetesh Srivastava and Vivek Srivastava7.1 Introduction 1827.2 Related Work 1847.3 Design and Implementation 1867.3.1 Background 1867.4 Proposed Model Architecture 1907.4.1 Experimental Setup 1927.4.2 Dataset Specification 1927.5 Results and Discussions 1937.6 Conclusion 198References 1998 Churn Prediction in Social Networks Using Modified BiLSTM-CNN Model 203Himanshu Rai and Jyoti Kesarwani8.1 Introduction 2048.2 Customer Behavior in Social Networks 2098.3 Proposed Methodology 2188.3.1 Churn Dataset Acquisition 2188.3.2 Data Preprocessing 2208.3.3 Proposed Model 2208.4 Result 2218.5 Conclusion 225References 2269 Fog Computing Security Concerns in Healthcare Using IoT and Blockchain 231Ruchi Mittal, Shikha Gupta and Shefali Arora9.1 Introduction 2329.1.1 Types of Security Concerns in Healthcare 2369.2 Related Work 2399.3 Open Questions and Research Challenges 2419.4 Problem Definition 2429.5 Objectives 2429.6 Research Methodology 2439.6.1 The Three-Tier Blockchain Design 2439.6.2 System Architecture 2439.6.3 Workflow in Different Scenarios 2459.7 Conclusion and Future Work 249References 24910 Smart Agriculture Revolution: Cloud and IoT-Based Solutions for Sustainable Crop Management and Precision Farming 253Shrawan Kumar Sharma10.1 Introduction 25510.1.1 IoT in Agriculture 25710.1.2 Cloud Computing in Agriculture 25910.1.3 Precision Farming 26310.1.4 Sustainable Agricultural and Remote Sensing 26510.2 Data Analytics and Decision Support 26710.2.1 Remote Monitoring 26910.3 Challenges and Solutions Smart Agriculture 27010.3.1 (AI) Approach in Agriculture and Needs 27010.3.2 Needs of AI Farm 27310.3.3 Role of AI in Agriculture 27410.4 AI for Soybean (Glycine max) Crop 27510.4.1 Soybean Disease Image Acquisition and Pretreatment 27610.5 Result Discussion 28110.5.1 Emerging Trends and Technologies in Smart Agriculture 28110.6 Conclusion 283References 28511 Greedy Particle Swarm Optimization Approach Using Leaky ReLU Function for Minimum Spanning Tree Problem 289Ashish Kumar Singh and Anoj Kumar11.1 Introduction 29011.1.1 Goal 29111.1.2 Research Contribution are Below Listed 29211.2 Background 29211.2.1 Minimum Spanning Tree 29411.2.2 Particle Swarm Optimization 29611.2.3 Firefly Algorithm 29711.2.4 Leaky ReLU Activation Function 29811.3 Population-Based Proposed Optimization Approach 29811.3.1 Motivation 29911.3.2 Greedy Particle Swarm Optimization Using Leaky ReLU (LR-GPSO) 30011.4 Experimental Setup and Result Analysis of Proposed Work (LR-GPSO) 30711.4.1 Complexity 30711.4.2 Simulation Experiments 30811.4.3 Convergence Curve 31111.5 Conclusion and Future Work 313References 31412 SDN Deployed Secure Application Design Framework for IoT Using Game Theory 317Madhukrishna Priyadarsini and Padmalochan Bera12.1 Introduction 31812.1.1 IoT Overview 31812.1.2 SDN Overview 31912.1.3 Game Theory Overview 32112.2 Background Study 32212.2.1 IoT Security Using SDN 32212.2.2 IoT Security Using Game Theory 32312.3 SDN-Deployed Design Framework for IoT Using Game-Theoretic Solutions 32412.3.1 Trust Verification 32412.4 Case Study: SDN Deployed Design Framework in Robot Manufacturing Industry 33412.4.1 Working Procedure of a Robot Manufacturing Industry 33412.4.2 Integration of SDN-Deployed Design Framework in Robot Manufacturing Industry 33512.4.3 Experimental Results 33612.5 Discussion 33812.6 Conclusion 339References 33913 Framework for PLM in Industry 4.0 Based on Industrial Blockchain 341Ali Zaheer Agha, Rajesh Kumar Shukla, Ratnesh Mishra and Ravi Shankar Shukla13.1 Introduction 34213.1.1 What is Blockchain? 34313.1.2 Blockchain Technology’s Integration with Industry 4.0 34313.1.3 Blockchain Applications in Industry 4.0 34313.1.4 A Consensus Algorithm 34413.1.5 Product Lifecycle Management 34513.1.6 Benefits of Smart Contracts in Addressing PLM Challenges 34713.2 Related Work 34813.2.1 Product Lifecycle Management 34913.2.2 Industrial Blockchain 35113.2.3 The On-Chain vs. Off-Chain Principle 35313.3 The Recommended Architecture’s Methodology 35413.3.1 The Suggested Platform’s Architecture 35413.3.2 The Suggested Platform’s Technological Solution 35813.4 Key Services That are Suggested 36013.4.1 A Co-Creation Service Enabled by Blockchain 36013.4.2 Blockchain-Enabled QAT2 Service 36313.4.3 Proactive Upkeep Service Facilitated by Blockchain 36413.4.4 Smart Recycling Program Driven by Blockchain 36513.5 Modelling and Assessment 36613.5.1 Overview of the Investigation 36613.5.2 Experimental Evaluation and Comparison 36813.5.3 Discussion 37213.6 Conclusion and Future Work 373A Statement of Competing Interests 374References 37514 Machine Learning Enabled Smart Agriculture Classification Technique for Edge Devices Using Remote Sensing Platform 381Priyanka Gupta, Suraj Kumar Singh, Neetish Kumar and Bhavna ThakurList of Abbreviations 38214.1 Introduction 38214.2 Related Works 38414.3 Methods and Dataset 38614.3.1 Research Area and Dataset 38614.3.2 Pre-Processing and Image Dataset 38714.3.3 Classifiers 39014.4 Proposed Algorithm 39114.5 Results and Discussions 39214.5.1 Classified Crop Map 39414.6 Conclusion 395References 39615 A Lightweight Intelligent Detection Approach for Interest Flooding Attack 401Naveen Kumar, Brijendra Pratap Singh and Rohit15.1 Introduction 40215.2 NDN Background 40515.2.1 NDN Architecture 40515.2.2 NDN Security 40815.3 Related Work 40915.4 IFA Feature Selection and Detection 41115.4.1 IFA Modelling 41215.4.2 Data Collection 41315.4.3 Balancing the Dataset 41415.4.4 Feature Selection 41515.4.5 Dimensionality Reduction 42115.4.6 Classification 42415.5 Conclusion 428References 42916 An Internet of Vehicles Model Architecture with Seven Layers 433Sujata Negi Thakur, Manisha Koranga, Sandeep Abhishek, Richa Pandey and Mayurika Joshi16.1 Introduction 43416.2 Literature Review 43516.3 Proposed Architecture of Internet of Vehicles 43916.4 Applications, Characteristics, and Challenges of the Internet of Vehicles (IoV) 451Conclusion 455References 455Index 457