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Fog Computing for Intelligent Cloud IoT Systems

Edited by Chandan Banerjee, Anupam Ghosh, Rajdeep Chakraborty and Ahmed A. Elngar
Series: Advances in Learning Analytics for Intelligent Cloud-IoT Systems
Copyright: 2024   |   Status: Published
ISBN: 9781394174614  |  Hardcover  |  
453 pages
Price: $225 USD
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One Line Description
This book is a comprehensive guide on fog computing and how it facilitates computing, storage, and networking services

Audience
The book will be read by researchers and engineers in computer science, information technology, electronics, and communication specializing in machine learning, deep learning, the cyber world, IoT, and security systems.

Description
Fog computing is a decentralized computing structure that connects data, devices, and the cloud. It is an extension of cloud computing and is an essential concept in IoT (Internet of Things), as it reduces the burden of processing in cloud computing. It brings intelligence and processing closer to where the data is created and transmitted to other sources.
Fog computing has many benefits, such as reduced latency in processing data, better response time that helps the user’s experience, and security and privacy compliance that assures protecting the vital data in the cloud. It also reduces the cost of bandwidth, because the processing is achieved in the cloud, which reduces network bandwidth usage and increases efficiency as user devices share data in the local processing infrastructure rather than the cloud service.
Fog computing has various applications across industries, such as agriculture and farming, the healthcare industry, smart cities, education, and entertainment. For example, in the agriculture industry, a very prominent example is the SWAMP project, which stands for Smart Water Management Platform. With fog computing’s help, SWAMP develops a precision-based smart irrigation system concept used in agriculture, minimizing water wastage.
This book is divided into three sections. The first section studies fog computing and machine learning, covering fog computing architecture, application perspective, computational offloading in mobile cloud computing, intelligent Cloud-IoT systems, machine learning fundamentals, and data visualization. The second section focuses on applications and analytics, spanning various applications of fog computing, such as in healthcare, Industry 4.0, cancer cell detection systems, smart farming, and precision farming. This section also covers analytics in fog computing using big data and patient monitoring systems, and the emergence of fog computing concerning applications and potentialities in traditional and digital educational systems. Security aspects in fog computing through blockchain and IoT, and fine-grained access through attribute-based encryption for fog computing are also covered.

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Author / Editor Details
Chandan Banerjee, PhD, is a professor in the Department of Information Technology, Netaji Subhash Engineering College, West Bengal, India. His research interests include cloud computing, computer networks, fog computing, data structure, and algorithms. With about 35 publications in referred international journals, he serves as a reviewer for many peer-reviewed international journals and international conferences. Banerjee is the recipient of several awards including the top-performing mentor award and the Silver Partner Faculty Recognition award.

Anupam Ghosh, PhD, is a professor in the Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, India. He has published more than 100 international papers in reputed international journals and conferences. His fields of interest are mainly AI, machine learning, deep learning, image processing, soft computing, bioinformatics, IoT, and data mining.

Rajdeep Chakraborty, PhD, is an assistant professor in the Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, India. His fields of interest are mainly in cryptography and computer security. He was awarded the Adarsh Vidya Saraswati Rashtriya Puraskar, National Award of Excellence 2019 conferred by Glacier Journal Research Foundation,

Ahmed A. Elngar, PhD, is an associate professor in the Department of Computer Science and Engineering, University Institute of Engineering, Chandigarh University, Punjab, India. His field of interest focuses on cryptology and computer security. He has several publications in reputed journals along with a book on hardware cryptography. In 2019, Elngar was awarded the Adarsh Vidya Sarawati Rashtriya Puraskar, National Award of Excellence.

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Table of Contents
Preface
Part I: Study of Fog Computing and Machine Learning
1 Fog Computing: Architecture and Application

Soumen Swarnakar
1.1 Introduction
1.2 Fog Computing: An Overview
1.3 Fog Computing for Intelligent Cloud-IoT System
1.4 Fog Computing Architecture
1.5 Basic Modules of Fog Computing
1.6 Cloud Computing vs. Fog Computing
1.7 Fog Computing vs. IoT
1.8 Applications of Fog Computing
1.9 Will the Fog Be Taken Over by the Cloud?
1.10 Challenges in Fog Computing
1.11 Future of Fog Computing
1.12 Conclusion
References
2. A Comparative Review on Different Techniques of Computation Offloading in Mobile Cloud Computing
Priyajit Sen, Tamanna Islam, Rajat Pandit and Debabrata Sarddar
2.1 Introduction
2.2 Related Works
2.3 Computation Offloading Techniques
2.3.1 MAUI Architecture
2.3.2 Clone-Cloud Based Model
2.3.3 Cuckoo Design
2.3.4 MACS Architecture
2.3.5 AHP and TOPSIS Design Technique
2.3.6 Energy Aware Design for Workflows
2.3.7 MCSOS Architecture
2.3.8 Cloudlet
2.3.9 Jade
2.3.10 Phone2Cloud
2.4 Conclusion
2.5 Future Scope
2.6 Acknowledgement
References
3. Fog Computing for Intelligent Cloud–IoT System: Optimization of Fog Computing in Industry 4.0
Mayank Patel, Monika Bhatt and Ankush Patel
3.1 Introduction
3.1.1 Industry 4.0
3.1.2 Fog Computing
3.1.3 Fog Nodes
3.2 How Fog Computing with IIoT Brings Revolution
3.2.1 Hierarchical Fog Computing Architecture
3.2.2 Layered Fog Computing Architecture
3.3 Applications of Fog Computing on Which Industries Rely
3.3.1 In the Field of Agriculture
3.3.2 In Healthcare Industry
3.3.3 In Smart Cities
3.3.4 In Education
3.3.5 In Entertainment
3.4 Data Analysis
3.5 Illustration of Fog Computing and Application
3.5.1 Figures
3.6 Conclusion
3.7 Future Scope/Acknowledgement
References
4. Machine Learning Integration in Agriculture Domain: Concepts and Applications
Ankur Biswas and Rita Banik
4.1 Introduction
4.2 Fog Computing in Agriculture
4.2.1 Smart Farming
4.3 Methodology
4.3.1 Data Source
4.3.2 Data Analysis and Pre-Processing
4.3.3 Feature Extraction
4.3.4 Model Selection
4.3.5 Hyper-Parameter Tuning
4.3.6 Train-Test Split
4.4 Results and Discussion
4.4.1 Modeling Algorithms
4.5 Conclusion
4.6 Future Scope
References
5. Role of Intelligent IoT Applications in Fog Computing
Pawan Whig, Dhaya Sindhu Battina, Srinivas Venkata, Ashima Bhatnagar Bhatia and Yusuf Jibrin Alkali
5.1 Introduction
5.1.1 PaaS/SaaS Platforms Have Various Benefits That are Crucial to the Success of Many Small IoT Startup Businesses
5.2 Cloud Service Model’s Drawbacks
5.3 Fog Computation
5.3.1 Standardization
5.3.2 Growing Use Cases for Fog Computing
5.3.3 IoT Applications with Intelligence
5.3.4 Graphics Processing Units
5.4 Recompenses of FoG
5.5 Limitation of Fog Computing
5.6 Fog Computing with IoT
5.6.1 Benefits of Fog Computing with IoT
5.6.2 Challenges of Fog Computing with IoT
5.7 Edge AI Embedded
5.7.1 Key Software Characteristics in Fog Computing
5.7.2 Fog Cluster Management
5.7.3 Technology for Computing in the Fog
5.7.4 Concentrating Intelligence
5.7.5 Device-Driven Intelligence
5.8 Network Intelligence Objectives
5.9 Farming with Fog Computation (Case Study)
5.10 Conclusion
References
6. SaaS-Based Data Visualization Platform—A Study in COVID-19 Perspective
S. Majumder, A. Shaw, R. Keshri and A. Chakraborty
6.1 Introduction
6.1.1 Motivation and the Problem of Interest
6.2 Summary of Objectives
6.3 What is a Pandemic?
6.4 COVID-19 and Information Gap
6.5 Data Visualization and its Importance
6.6 Data Management with Data Visualization
6.7 What is Power BI?
6.7.1 Data Collection & Wrangling
6.7.2 Data Description & Source
6.7.3 Data Transformation
6.8 Output Data
6.9 Design & Implementation
6.9.1 Integration Design
6.9.2 High-Level Process Flow
6.9.3 Solution Flow
6.10 Dashboard Development
6.10.1 Landing Page
6.10.2 Approach and Design
6.10.3 Helpline Information
6.10.3.1 Approach and Design
6.10.4 Symptom Detection
6.10.4.1 Approach and Design
6.10.5 Testing Lab Information
6.10.5.1 Approach and Design
6.10.6 Hospital Information
6.10.6.1 Approach and Design
6.10.7 Oxygen Suppliers Information
6.10.7.1 Approach and Design
6.10.8 COVID Cases Information
6.10.8.1 Approach and Design
6.10.9 Vaccination Information
6.10.9.1 Approach and Design
6.10.10 Patients’ Information
6.10.10.1 Approach and Design
6.11 Advantages and its Impact
6.12 Conclusion and Future Scope
References
7. A Complete Study on Machine Learning Algorithms for Medical Data Analysis
Inderdeep Kaur and Aleem Ali
7.1 Introduction
7.1.1 Importance of Machine Learning Algorithms in Medical Data Analysis
7.2 Pre-Processing Medical Data for Machine Learning
7.3 Supervised Learning Algorithms for Medical Data Analysis
7.3.1 Linear Regression Algorithm
7.3.2 Logistic Regression Algorithm
7.3.3 Decision Trees Algorithm
7.3.3.1 Advantages of Decision Tree Algorithm
7.3.3.2 Limitations of Decision Tree Algorithm
7.3.4 Random Forest Algorithm
7.3.4.1 Advantages of Random Forest Algorithm
7.3.4.2 Limitations of Random Forest Algorithm
7.3.4.3 Applications of Random Forest Algorithm in Medical Data Analysis
7.3.5 Support Vector Machine Algorithm
7.3.5.1 Advantages of SVM Algorithm
7.3.5.2 Limitations of SVM Algorithm
7.3.5.3 Applications of SVM Algorithm in Medical Data Analysis
7.3.6 Naive Bayes Algorithm
7.3.7 KNN (K-Nearest Neighbor Algorithm)
7.3.7.1 Applications of K-NN Algorithm
7.3.8 Deep Learning Algorithm
7.3.9 Deep Learning Application
7.4 Unsupervised Learning Algorithms for Medical Data Analysis
7.4.1 Clustering Algorithm
7.4.2 Principal Component Analysis Algorithm
7.4.3 Independent Component Analysis Algorithm
7.4.4 Association Rule Mining Algorithm
7.5 Applications of Machine-Learning Algorithms in Medical Data Analysis
7.6 Limitations and Challenges of Machine Learning Algorithms in Medical Data Analysis
7.7 Future Research Directions and Machine Learning Developments in the Realm of Medical Data Analysis
7.8 Conclusion
References
Part II: Applications and Analytics
8. Fog Computing in Healthcare: Application Taxonomy, Challenges and Opportunities

Subrata Datta and Priyanka Datta
8.1 Introduction
8.2 Research Methodology
8.3 Application Taxonomy in FC-Based Healthcare
8.3.1 Diagnosis
8.3.2 Monitoring
8.3.3 Notification
8.3.4 Zest of Applications of FC in Healthcare
8.4 Challenges in FC-Based Healthcare
8.4.1 QoS Optimization
8.4.2 Patient Authentication and Access Control
8.4.3 Data Processing
8.4.4 Data Privacy Preservation
8.4.5 Energy Efficiency
8.5 Research Opportunities
8.5.1 Research Opportunity in Computing
8.5.2 Research Opportunity in Security
8.5.3 Research Opportunity in Services
8.5.4 Research Opportunity in Implementation
8.6 Conclusion
References
9. IoT-Driven Predictive Maintenance Approach in Industry 4.0: A Fiber Bragg Grating (FBG) Sensor Application
Dipak Ranjan Nayak, Pramod Sharma, Ambarish G. Mohapatra, Narayan Nayak, Bright Keswani and Ashish Khanna
9.1 Introduction
9.2 Review of Related Research Articles
9.2.1 Studies on FBG Sensors and Their Role in Industry 4.0
9.2.1.1 Magnetostrictive Material
9.2.1.2 Magneto-Optical (MO) Materials
9.2.1.3 Magnetic Fluid (MF) Materials
9.2.1.4 Magnetically Sensitive Materials and Their Application
9.2.1.5 Optical Fiber Current Sensors
9.3 Research Gaps
9.4 Emerging Research Directions
9.5 The Broad Concept of FBG Sensor Applications in Industry 4.0
9.6 Conclusion
References
10. Fog Computing-Enabled Cancer Cell Detection System Using Convolution Neural Network in Internet of Medical Things
Soumen Santra, Dipankar Majumdar and Surajit Mandal
10.1 Introduction
10.2 Fog Computing: Approach of IoMT
10.3 Relationship Between IoMT and Deep Neural Network
10.4 Fog Computing Enabled CNN for Medical Imaging
10.5 Algorithm Approach of Proposed Model
10.6 Result and Analysis
10.7 Conclusion
References
11. Application of IoT in Smart Farming and Precision Farming: A Review
Suparna Biswas and Soumik Podder
11.1 Introduction
11.2 Methodologies Used in Precision Agriculture
11.3 Contribution of IoT in Agriculture
11.4 IoT Enabled Smart Farming
11.5 IoT Enabled Precision Farming
11.6 Machine Learning Enable Precision Farming
11.7 Application of Operational Research Method in Farming System
11.8 Conclusion
11.9 Future Scope
References
12. Big IoT Data Analytics in Fog Computing
Manash Kumar Mondal, Riman Mandal and Utpal Biswas
12.1 Introduction
12.2 Literature Review
12.3 Motivation
12.4 Fog Computing
12.4.1 Fog Node
12.4.2 Characteristics of Fog Computing
12.4.3 Attributes of Fog Node
12.4.4 Fog Computing Service Model
12.4.5 Fog Computing Architecture
12.4.6 Data Flow and Control Flow in Fog Architecture
12.4.7 Fog Deployment Models
12.5 Big Data
12.5.1 What is Big Data?
12.5.2 Source of Big Data
12.5.3 Characteristic of Big Data
12.6 Big Data Analytics Using Fog Computing
12.7 Conclusion
References
13. IOT-Based Patient Monitoring System in Real Time
Suparna Biswas, Tirtha Chakraborty, Souvik Mitra, Shubham Banerjee, Tuhin Sarkar and Sourav Paul
13.1 Introduction
13.2 Components Used
13.2.1 Node MCU
13.2.2 Heart Rate/Pulse Sensor
13.2.3 Temperature Sensor (LM35)
13.3 IoT Platform
13.3.1 ThingSpeak—IoT Platform Used in This Work
13.4 Proposed Method
13.5 Experimental Setup and Result
13.6 Conclusion
References
14. Fog Computing and Its Emergence with Reference to Applications and Potentialities in Traditional and Digital Educational Systems: A Scientific Review
P. K. Paul
14.1 Background
14.2 Objectives
14.3 Methods
14.4 Fog Computing: Basics and Advantages
14.4.1 Existing Major Works
14.4.2 Fog Computing Advantages
14.4.3 Fog Computing: Applications
14.4.3.1 In Smart Homes and Residences
14.4.3.2 In Smart Cities and Township Projects
14.4.3.3 In Monitoring Video Surveillance
14.4.3.4 In Intelligent Healthcare and Medical Systems
14.5 Growing Fog Computing Applications Emphasizing Education
14.6 Impact of Fog Computing in Education
14.6.1 Cases of Fog in Education
14.6.1.1 In Collaborative Teaching–Learning Process
14.6.1.2 In the Promotion of On-Campus Education
14.6.1.3 In Developing and Managing Online Education
14.6.1.4 In Continuing Workshop and Training
14.6.1.5 In Promoting Blended and Hybrid Modes of Education
14.6.1.6 In Uses of Cloud Computing for the Library and for Archival
14.6.1.7 In Developing Examination and Allied Evaluation Processes
14.7 Education Industry and Fog: Future Context
14.8 Fog Computing and Its Role in IOT Security: The Context of Campus
14.8.1 Issues and Disadvantages of Fog in General and in Educational Systems
14.9 Concluding Remarks
References
Part III: Security in Fog Computing
15. Blockchain Security for Fog Computing

Saiyam Varshney and Gur Mauj Saran Srivastava
15.1 Introduction
15.2 State of the Art
15.3 Security Issues in the Fog Computing Environments
15.3.1 Trust and Authentications in Fog Computing
15.3.2 Data Protection, Privacy, and Access Controls in Fog Computing
15.4 Blockchain Technology
15.4.1 Features of Blockchain to Increase the Transparency
15.4.1.1 Peer-to-Peer (P2P) Networks
15.4.1.2 Decentralized
15.4.1.3 Immutable and Incorruptible
15.5 Blockchain Security for Fog Computing Environment
15.6 Summary and Conclusion
References
16. Blockchain Security for Fog Computing and Internet of Things
Awais Khan Jumani, Waqas Ahmed Siddique, Muhammad Farhan Siddiqui and Kanwal
16.1 Introduction
16.1.1 Simply Vital Health
16.1.2 Blockchain-Based Frameworks for IoT Security and Privacy
16.1.3 Permissioned Blockchain in IoT
16.1.4 Multi-Layer Security Framework
16.1.5 Blockchain-Based Supply Chain
16.1.5.1 Decentralized
16.1.5.2 Transparent
16.1.5.3 Open Source
16.1.5.4 Autonomy
16.1.5.5 Immutable
16.1.5.6 Secrecy
16.2 Pros and Cons of Blockchain
16.3 The Properties of Blockchain
16.3.1 Proof of Work (PoW)
16.3.2 Proof of Stake (PoS)
16.3.3 Smart Contracts
16.4 The Attacks on Blockchain
16.4.1 Attack of 51%
16.4.2 Double-Spending
16.4.3 Sybil’s Attack
16.4.4 DDos’s Attack
16.4.5 Cracking of the Cryptographic
16.4.6 Blockchain 1.0 – Digital Currency
16.4.7 Blockchain 2.0 – Digital Economy
16.4.8 Blockchain 3.0 – Digital Society
16.5 Application of Blockchain Technology in Healthcare
16.5.1 Electronic Health Records
16.5.2 Public Health
16.5.3 Education
16.6 Fog Computing
16.7 Confidentiality Concerns in Fog Computing
16.7.1 Security
16.7.2 Confidentiality in Fog Computing
16.7.3 Concerns Regarding Identification and Reliability
16.8 Cloud Computing Security
16.9 Fog Computing Security Breaches
16.10 Optimized Fog Computing
16.11 Open Research Issues in Blockchain and Fog Computing Security
16.12 Conclusion
References
17. Fine-Grained Access Through Attribute-Based Encryption for Fog Computing
Malabika Das
17.1 Introduction
17.2 Attribute-Based Encryption
17.3 Fine-Grained Access Through ABE
17.4 ABE Model for Fine-Grained Access
17.5 Application of ABE on Fog Computing
17.6 A Comparison of ABE Scheme
17.7 Conclusion
References
Index

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