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AI-Based Advanced Optimization Techniques for Edge Computing

Edited by Mohit Kumar, Gautam Srivastava, Ashutosh Kumar Singh and Kalka Dubey
Series: Advances in Learning Analytics for Intelligent Cloud-IoT Systems
Copyright: 2024   |   Expected Pub Date:2024/10/30
ISBN: 9781394287031  |  Hardcover  |  
478 pages

One Line Description
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.

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.

Description
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.

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Author / Editor Details
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 co

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.

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Table of Contents
Preface
Acknowledgement
1. Navigating Next-Generation Network Architecture: Unleashing the Power of SDN, NFV, NS, and AI Convergence
Monika Dubey, Snehlata, Ashutosh Kumar Singh, Richa Mishra and Mohit Kumar
1.1 Introduction
1.2 Revolutionizing Infrastructure with SDN, NFV, and NS
1.2.1 SDN: Definition and Architecture
1.2.2 NFV: Definition and Architecture
1.2.3 NS: Conceptual Abstractions
1.3 Realizing NS Potential with SDN and NFV
1.4 Artificial Intelligence: Pivotal Role in Networking Transformation
1.4.1 Supervised Learning
1.4.2 Unsupervised Learning
1.4.3 Reinforcement Learning
1.4.4 Deep Learning
1.5 Navigating Challenges and Solutions
1.5.1 Performance Issues in Network Structure
1.5.2 Management and Orchestration Issues
1.5.3 Security and Privacy
1.5.4 New Business Models
1.6 Conclusion
Disclosure Statement
References
2. OctoEdge: An Octopus-Inspired Adaptive Edge Computing Architecture
Sashi Tarun
2.1 Introduction
2.1.1 Edge Computing as Resource Manager
2.1.2 Edge Computing Hurdles
2.1.3 Edge Computing and the Need for Adaptability
2.2 Problem Statement
2.3 Motivations
2.4 Related Work
2.5 OctoEdge Proposed Architecture
2.5.1 OctoEdge Working Principles
2.5.2 Benefits of OctoEdge
2.6 OctoEdge Architecture Functional Components
2.7 Results and Discussion
2.8 OctoEdge Architecture: Scope and Scientific Merits
2.9 Use Cases and Applications
2.10 Challenges and Future Directions
2.11 Conclusion
References
3. Development of Optimized Machine Learning Oriented Models
Ratnesh Kumar Dubey, Dilip Kumar Choubey and Shubha Mishra
3.1 Introduction
3.1.1 NSL-KDD Dataset
3.2 Literature Review
3.3 Problem Definition
3.4 Proposed Work
3.4.1 Machine Learning
3.5 Experimental Analysis
3.6 Conclusion
3.7 Future Scope
References
4. Leveraging Multimodal Data and Deep Learning for Enhanced Stock Market Prediction
Pinky Gangwani and Vikas Panthi
4.1 Introduction
4.1.1 Motivation and Contribution
4.1.2 Rationale for Selecting the Methods
4.2 Literature Review
4.3 Proposed Design of an Efficient Model that Leverages Multimodal Data and Deep Learning for Enhanced Stock Market Prediction
4.3.1 Discussion on Selection Criteria
4.4 Statistical Analysis and Comparison
4.5 Acknowledging Limitations and Potential Challenges
4.6 Mitigation Strategies and Future Directions
4.7 Conclusion
4.8 Future Scope
References
5. Context Dependent Sentiments Analysis Using Machine Learning
Mahima Shanker Pandey, Bihari Nandan Pandey, Abhishek Singh, Ashish Kumar Mishra and Brijesh Pandey
5.1 Introduction
5.1.1 Motivation
5.2 Literature Review
5.2.1 Text Sentiment
5.2.2 Audio Sentiment
5.2.3 Video Sentiment
5.3 Methodology
5.3.1 System Architecture
5.4 Proposed Model
5.4.1 Proposed Algorithm
5.4.2 Data Set Sources
5.4.3 Text Sentiment
5.4.4 Audio Sentiment
5.4.5 Video Sentiment
5.5 Implementations and Results
5.5.1 Results
5.5.2 Text Sentiment
5.5.3 Audio Sentiment
5.5.4 Video Sentiment
5.5.5 Applications
5.6 Conclusion
References
6. Thyroid Cancer Prediction Using Optimizations
Swati Sharma, Vijay Kumar Sharma, Punit Mittal, Pradeep Pant and Nitin Rakesh
6.1 Introduction
6.2 Background and Related Work
6.3 Proposed Methodology
6.4 Architecture
6.5 Materials and Methods
6.6 Results and Discussion
6.7 Conclusion
References
7. An LSTM-Oriented Approach for Next Word Prediction Using Deep Learning
Nidhi Shukla, Ashutosh Kumar Singh, Vijay Kumar Dwivedi, Pallavi Shukla, Jeetesh Srivastava and Vivek Srivastava
7.1 Introduction
7.2 Related Work
7.3 Design and Implementation
7.3.1 Background
7.3.1.1 LSTM
7.3.1.2 Bi-LSTM
7.4 Proposed Model Architecture
7.4.1 Experimental Setup
7.4.2 Dataset Specification
7.5 Results and Discussions
7.6 Conclusion
References
8. Churn Prediction in Social Networks Using Modified BiLSTM-CNN Model
Himanshu Rai and Jyoti Kesarwani
8.1 Introduction
8.2 Customer Behavior in Social Networks
8.3 Proposed Methodology
8.3.1 Churn Dataset Acquisition
8.3.2 Data Preprocessing
8.3.3 Proposed Model
8.4 Result
8.5 Conclusion
References
9. Fog Computing Security Concerns in Healthcare Using IoT and Blockchain
Ruchi Mittal, Shikha Gupta and Shefali Arora
9.1 Introduction
9.1.1 Types of Security Concerns in Healthcare
9.2 Related Work
9.3 Open Questions and Research Challenges
9.4 Problem Definition
9.5 Objectives
9.6 Research Methodology
9.6.1 The Three-Tier Blockchain Design
9.6.1.1 Model Design
9.6.2 System Architecture
9.6.3 Workflow in Different Scenarios
9.7 Conclusion and Future Work
References
10. Smart Agriculture Revolution: Cloud and IoT-Based Solutions for Sustainable Crop Management and Precision Farming
Shrawan Kumar Sharma
10.1 Introduction
10.1.1 IoT in Agriculture
10.1.2 Cloud Computing in Agriculture
10.1.3 Precision Farming
10.1.4 Sustainable Agricultural and Remote Sensing
10.2 Data Analytics and Decision Support
10.2.1 Remote Monitoring
10.3 Challenges and Solutions Smart Agriculture
10.3.1 (AI) Approach in Agriculture and Needs
10.3.2 Needs of AI Farm
10.3.3 Role of AI in Agriculture
10.4 AI for Soybean (Glycine max) Crop
10.4.1 Soybean Disease Image Acquisition and Pretreatment
10.5 Result Discussion
10.5.1 Emerging Trends and Technologies in Smart Agriculture
10.6 Conclusion
References
11. Greedy Particle Swarm Optimization Approach Using Leaky ReLU Function for Minimum Spanning Tree Problem
Ashish Kumar Singh and Anoj Kumar
11.1 Introduction
11.1.1 Goal
11.1.2 Research Contribution are Below Listed
11.2 Background
11.2.1 Minimum Spanning Tree
11.2.2 Particle Swarm Optimization
11.2.3 Firefly Algorithm
11.2.4 Leaky ReLU Activation Function
11.3 Population-Based Proposed Optimization Approach
11.3.1 Motivation
11.3.2 Greedy Particle Swarm Optimization Using Leaky ReLU (LR-GPSO)
11.3.2.1 Initialization of Parameters
11.3.2.2 Population Initialization
11.3.2.3 Input
11.3.2.4 Evaluation
11.3.2.5 Updating Position of Members of Swarm
11.3.2.6 Role of Leaky ReLU Function
11.3.2.7 Mutation Effect
11.3.2.8 Selection of Edges
11.3.2.9 Output
11.4 Experimental Setup and Result Analysis of Proposed Work (LR-GPSO)
11.4.1 Complexity
11.4.2 Simulation Experiments
11.4.2.1 Result for Vertices (V=20)
11.4.2.2 Result for Vertices (V=40)
11.4.2.3 Result for Vertices (V=60)
11.4.2.4 Result for Vertices (V=80)
11.4.3 Convergence Curve
11.5 Conclusion and Future Work
References
12. SDN Deployed Secure Application Design Framework for IoT Using Game Theory
Madhukrishna Priyadarsini and Padmalochan Bera
12.1 Introduction
12.1.1 IoT Overview
12.1.2 SDN Overview
12.1.3 Game Theory Overview
12.2 Background Study
12.2.1 IoT Security Using SDN
12.2.2 IoT Security Using Game Theory
12.3 SDN-Deployed Design Framework for IoT Using Game-Theoretic Solutions
12.3.1 Trust Verification
12.4 Case Study: SDN Deployed Design Framework in Robot Manufacturing Industry
12.4.1 Working Procedure of a Robot Manufacturing Industry
12.4.2 Integration of SDN-Deployed Design Framework in Robot Manufacturing Industry
12.4.3 Experimental Results
12.5 Discussion
12.6 Conclusion
References
13. Framework for PLM in Industry 4.0 Based on Industrial Blockchain
Ali Zaheer Agha, Rajesh Kumar Shukla, Ratnesh Mishra and Ravi Shankar Shukla
13.1 Introduction
13.1.1 What is Blockchain?
13.1.2 Blockchain Technology’s Integration with Industry 4.0
13.1.3 Blockchain Applications in Industry 4.0
13.1.3.1 Protection of Manufacturing Data
13.1.3.2 Resolution of Quality Issues
13.1.3.3 Supply Chain Development
13.1.4 A Consensus Algorithm
13.1.5 Product Lifecycle Management
13.1.6 Benefits of Smart Contracts in Addressing PLM Challenges
13.2 Related Work
13.2.1 Product Lifecycle Management
13.2.2 Industrial Blockchain
13.2.3 The On-Chain vs. Off-Chain Principle
13.3 The Recommended Architecture’s Methodology
13.3.1 The Suggested Platform’s Architecture
13.3.2 The Suggested Platform’s Technological Solution
13.4 Key Services That are Suggested
13.4.1 A Co-Creation Service Enabled by Blockchain
13.4.2 Blockchain-Enabled QAT2 Service
13.4.3 Proactive Upkeep Service Facilitated by Blockchain
13.4.4 Smart Recycling Program Driven by Blockchain
13.5 Modelling and Assessment
13.5.1 Overview of the Investigation
13.5.2 Experimental Evaluation and Comparison
13.5.3 Discussion
13.6 Conclusion and Future Work
A Statement of Competing Interests
References
14. Machine Learning Enabled Smart Agriculture Classification Technique for Edge Devices Using Remote Sensing Platform
Priyanka Gupta, Suraj Kumar Singh, Neetish Kumar and Bhavna Thakur
List of Abbreviations
14.1 Introduction
14.2 Related Works
14.3 Methods and Dataset
14.3.1 Research Area and Dataset
14.3.2 Pre-Processing and Image Dataset
14.3.3 Classifiers
14.3.3.1 Naïve Bayes Classifier
14.3.3.2 Minimum Distance Classifier
14.4 Proposed Algorithm
14.5 Results and Discussions
14.5.1 Classified Crop Map
14.6 Conclusion
References
15. A Lightweight Intelligent Detection Approach for Interest Flooding Attack
Naveen Kumar, Brijendra Pratap Singh and Rohit
15.1 Introduction
15.2 NDN Background
15.2.1 NDN Architecture
15.2.1.1 NDN Packet
15.2.1.2 NDN Data Structures
15.2.1.3 NDN Forwarding
15.2.2 NDN Security
15.2.2.1 IFA
15.2.2.2 IFA Type
15.3 Related Work
15.4 IFA Feature Selection and Detection
15.4.1 IFA Modelling
15.4.2 Data Collection
15.4.3 Balancing the Dataset
15.4.4 Feature Selection
15.4.4.1 Filter Methods
15.4.4.2 Wrapper Methods
15.4.5 Dimensionality Reduction
15.4.6 Classification
15.5 Conclusion
References
16. An Internet of Vehicles Model Architecture with Seven Layers
Sujata Negi Thakur, Manisha Koranga, Sandeep Abhishek, Richa Pandey and Mayurika Joshi
16.1 Introduction
16.2 Literature Review
16.3 Proposed Architecture of Internet of Vehicles
16.4 Applications, Characteristics, and Challenges of the Internet of Vehicles (IoV)
Conclusion
References
Index

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Description
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Table of Contents
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