Convergence of Cloud with AI for Big Data Analytics

Danda B Rawat, PhD, is a Full Professor in the Department of Electrical Engineering & Computer Science (EECS), Founder and Director of the Howard University Data Science and Cybersecurity Center, Director of DoD Center of Excellence in Artificial Intelligence & Machine Learning, Director of Cyber-security and Wireless Networking Innovations Research Lab, Graduate Program Director of Howard CS Graduate Programs, and Director of Graduate Cybersecurity Certificate Program at Howard University, Washington, DC, USA. Dr. Rawat has published more than 250 scientific/technical articles and 11 books. Lalit K Awasthi, PhD, is the Director of Dr. B. R. Ambedkar National Institute of Technology Jalandhar, India). He received his PhD degree from the Indian Institute of Technology Roorkee in computer science and engineering. He has published more than 150 research papers in various journals and conferences of international repute and guided many PhDs in these areas. Valentina E Ballas, PhD, is a Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. Dr. Ballas is the author of more than 280 research papers in refereed journals and international conferences. She is the Editor-in-Chief of International Journal of Advanced Intelligence Paradigms and International Journal of Computational Systems Engineering. 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 received his PhD degree from the Indian Institute of Technology Roorkee in the field of cloud computing in 2018. His research topics cover the areas of cloud computing, fog computing, edge computing, Internet of Things, soft computing, and blockchain. He has published more than 25 research articles in international journals and conferences. Jitendra Kumar Samriya, PhD, has a faculty position in the Department of Information Technology, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar. His research interest is cloud computing, artificial intelligence, and multi-objective evolutionary optimization techniques. He has published 15 research articles in international journals and has published five Indian and international patents.

Preface xv

1 Integration of Artificial Intelligence, Big Data, and Cloud Computing with Internet of Things 1
Jaydip Kumar

1.1 Introduction 2

1.2 Roll of Artificial Intelligence, Big Data and Cloud Computing in IoT 3

1.3 Integration of Artificial Intelligence with the Internet of Things Devices 4

1.4 Integration of Big Data with the Internet of Things 6

1.5 Integration of Cloud Computing with the Internet of Things 6

1.6 Security of Internet of Things 8

1.7 Conclusion 10

References 10

2 Cloud Computing and Virtualization 13
Sudheer Mangalampalli, Pokkuluri Kiran Sree, Sangram K. Swain and Ganesh Reddy Karri

2.1 Introduction to Cloud Computing 14

2.1.1 Need of Cloud Computing 14

2.1.2 History of Cloud Computing 14

2.1.3 Definition of Cloud Computing 15

2.1.4 Different Architectures of Cloud Computing 16

2.1.4.1 Generic Architecture of Cloud Computing 16

2.1.4.2 Market Oriented Architecture of Cloud Computing 17

2.1.5 Applications of Cloud Computing in Different Domains 18

2.1.5.1 Cloud Computing in Healthcare 18

2.5.1.2 Cloud Computing in Education 19

2.5.1.3 Cloud Computing in Entertainment Services 19

2.5.1.4 Cloud Computing in Government Services 19

2.1.6 Service Models in Cloud Computing 19

2.1.7 Deployment Models in Cloud Computing 21

2.2 Virtualization 22

2.2.1 Need of Virtualization in Cloud Computing 22

2.2.2 Architecture of a Virtual Machine 23

2.2.3 Advantages of Virtualization 24

2.2.4 Different Implementation Levels of Virtualization 25

2.2.4.1 Instruction Set Architecture Level 25

2.2.4.2 Hardware Level 26

2.2.4.3 Operating System Level 26

2.2.4.4 Library Level 26

2.2.4.5 Application Level 26

2.2.5 Server Consolidation Using Virtualization 26

2.2.6 Task Scheduling in Cloud Computing 27

2.2.7 Proposed System Architecture 31

2.2.8 Mathematical Modeling of Proposed Task Scheduling Algorithm 31

2.2.9 Multi Objective Optimization 34

2.2.10 Chaotic Social Spider Algorithm 34

2.2.11 Proposed Task Scheduling Algorithm 35

2.2.12 Simulation and Results 36

2.2.12.1 Calculation of Makespan 36

2.2.12.2 Calculation of Energy Consumption 37

2.3 Conclusion 37

References 38

3 Time and Cost-Effective Multi-Objective Scheduling Technique for Cloud Computing Environment 41
Aida A. Nasr, Kalka Dubey, Nirmeen El-Bahnasawy, Gamal Attiya and Ayman El-Sayed

3.1 Introduction 42

3.2 Literature Survey 44

3.3 Cloud Computing and Cloudlet Scheduling Problem 46

3.4 Problem Formulation 47

3.5 Cloudlet Scheduling Techniques 49

3.5.1 Heuristic Methods 50

3.5.2 Meta-Heuristic Methods 51

3.6 Cloudlet Scheduling Approach (CSA) 52

3.6.1 Proposed CSA 52

3.6.2 Time Complexity 53

3.6.3 Case Study 54

3.7 Simulation Results 56

3.7.1 Simulation Environment 56

3.7.2 Evaluation Metrics 56

3.7.2.1 Performance Evaluation with Small Number of Cloudlets 57

3.7.2.2 Performance Evaluation with Large Number of Cloudlets 57

3.8 Conclusion 64

References 64

4 Cloud-Based Architecture for Effective Surveillance and Diagnosis of COVID- 19 69
Shweta Singh, Aditya Bhardwaj, Ishan Budhiraja, Umesh Gupta and Indrajeet Gupta

4.1 Introduction 70

4.2 Related Work 71

4.2.1 Proposed Cloud-Based Network for Management of COVID- 19 73

4.3 Research Methodology 75

4.3.1 Sample Size and Target 76

4.3.1.1 Sampling Procedures 77

4.3.1.2 Response Rate 77

4.3.1.3 Instrument and Measures 77

4.3.2 Reliability and Validity Test 78

4.3.3 Exploratory Factor Analysis 78

4.4 Survey Findings 80

4.4.1 Outcomes of the Proposed Scenario 82

4.4.1.1 Online Monitoring 82

4.4.1.2 Location Tracking 82

4.4.1.3 Alarm Linkage 82

4.4.1.4 Command and Control 82

4.4.1.5 Plan Management 82

4.4.1.6 Security Privacy 83

4.4.1.7 Remote Maintenance 83

4.4.1.8 Online Upgrade 83

4.4.1.9 Command Management 83

4.4.1.10 Statistical Decision 83

4.4.2 Experimental Setup 83

4.5 Conclusion and Future Scope 85

References 86

5 Smart Agriculture Applications Using Cloud and IoT 89
Keshav Kaushik

5.1 Role of IoT and Cloud in Smart Agriculture 89

5.2 Applications of IoT and Cloud in Smart Agriculture 94

5.3 Security Challenges in Smart Agriculture 97

5.4 Open Research Challenges for IoT and Cloud in Smart Agriculture 100

5.5 Conclusion 103

References 103

6 Applications of Federated Learning in Computing Technologies 107
Sambit Kumar Mishra, Kotipalli Sindhu, Mogaparthi Surya Teja, Vutukuri Akhil, Ravella Hari Krishna, Pakalapati Praveen and Tapas Kumar Mishra

6.1 Introduction 108

6.1.1 Federated Learning in Cloud Computing 108

6.1.1.1 Cloud-Mobile Edge Computing 109

6.1.1.2 Cloud Edge Computing 111

6.1.2 Federated Learning in Edge Computing 112

6.1.2.1 Vehicular Edge Computing 113

6.1.2.2 Intelligent Recommendation 113

6.1.3 Federated Learning in IoT (Internet of Things) 114

6.1.3.1 Federated Learning for Wireless Edge Intelligence 114

6.1.3.2 Federated Learning for Privacy Protected Information 115

6.1.4 Federated Learning in Medical Computing Field 116

6.1.4.1 Federated Learning in Medical Healthcare 117

6.1.4.2 Data Privacy in Healthcare 117

6.1.5 Federated Learning in Blockchain 118

6.1.5.1 Blockchain-Based Federated Learning Against End-Point Adversarial Data 118

6.2 Advantages of Federated Learning 119

6.3 Conclusion 119

References 119

7 Analyzing the Application of Edge Computing in Smart Healthcare 121
Parul Verma and Umesh Kumar

7.1 Internet of Things (IoT) 122

7.1.1 IoT Communication Models 122

7.1.2 IoT Architecture 124

7.1.3 Protocols for IoT 125

7.1.3.1 Physical/Data Link Layer Protocols 125

7.1.3.2 Network Layer Protocols 127

7.1.3.3 Transport Layer Protocols 128

7.1.3.4 Application Layer Protocols 129

7.1.4 IoT Applications 130

7.1.5 IoT Challenges 132

7.2 Edge Computing 133

7.2.1 Cloud vs. Fog vs. Edge 134

7.2.2 Existing Edge Computing Reference Architecture 135

7.2.2.1 FAR-EDGE Reference Architecture 135

7.2.2.2 Intel-SAP Joint Reference Architecture (RA) 135

7.2.3 Integrated Architecture for IoT and Edge 136

7.2.4 Benefits of Edge Computing Based IoT Architecture 138

7.3 Edge Computing and Real Time Analytics in Healthcare 140

7.4 Edge Computing Use Cases in Healthcare 148

7.5 Future of Healthcare and Edge Computing 151

7.6 Conclusion 151

References 152

8 Fog-IoT Assistance-Based Smart Agriculture Application 157
Pawan Whig, Arun Velu and Rahul Reddy Nadikattu

8.1 Introduction 158

8.1.1 Difference Between Fog and Edge Computing 159

8.1.1.1 Bandwidth 163

8.1.1.2 Confidence 164

8.1.1.3 Agility 164

8.1.2 Relation of Fog with IoT 165

8.1.3 Fog Computing in Agriculture 167

8.1.4 Fog Computing in Smart Cities 169

8.1.5 Fog Computing in Education 170

8.1.6 Case Study 171

Conclusion and Future Scope 173

References 173

9 Internet of Things in the Global Impacts of COVID-19: A Systematic Study 177
Shalini Sharma Goel, Anubhav Goel, Mohit Kumar and Sachin Sharma

9.1 Introduction 178

9.2 COVID-19 – Misconceptions 181

9.3 Global Impacts of COVID-19 and Significant Contributions of IoT in Respective Domains to Counter the Pandemic 183

9.3.1 Impact on Healthcare and Major Contributions of IoT 183

9.3.2 Social Impacts of COVID-19 and Role of IoT 187

9.3.3 Financial and Economic Impact and How IoT Can Help to Shape Businesses 188

9.3.4 Impact on Education and Part Played by IoT 191

9.3.5 Impact on Climate and Environment and Indoor Air Quality Monitoring Using IoT 194

9.3.6 Impact on Travel and Tourism and Aviation Industry and How IoT is Shaping its Future 197

9.4 Conclusions 198

References 198

10 An Efficient Solar Energy Management Using IoT-Enabled Arduino-Based MPPT Techniques 205
Rita Banik and Ankur Biswas

List of Symbols 206

10.1 Introduction 206

10.2 Impact of Irradiance on PV Efficiency 210

10.2.1 PV Reliability and Irradiance Optimization 211

10.2.1.1 PV System Level Reliability 211

10.2.1.2 PV Output with Varying Irradiance 211

10.2.1.3 PV Output with Varying Tilt 212

10.3 Design and Implementation 212

10.3.1 The DC to DC Buck Converter 215

10.3.2 The Arduino Microcontroller 217

10.3.3 Dynamic Response 219

10.4 Result and Discussions 220

10.5 Conclusions 223

References 224

11 Axiomatic Analysis of Pre-Processing Methodologies Using Machine Learning in Text Mining: A Social Media Perspective in Internet of Things 229
Tajinder Singh, Madhu Kumari, Daya Sagar Gupta and Nikolai Siniak

11.1 Introduction 230

11.2 Text Pre-Processing – Role and Characteristics 232

11.3 Modern Pre-Processing Methodologies and Their Scope 234

11.4 Text Stream and Role of Clustering in Social Text Stream 241

11.5 Social Text Stream Event Analysis 242

11.6 Embedding 244

11.6.1 Type of Embeddings 244

11.7 Description of Twitter Text Stream 250

11.8 Experiment and Result 251

11.9 Applications of Machine Learning in IoT (Internet of Things) 251

11.10 Conclusion 252

References 252

12 APP-Based Agriculture Information System for Rural Farmers in India 257
Ashwini Kumar, Dilip Kumar Choubey, Manish Kumar and Santosh Kumar

12.1 Introduction 258

12.2 Motivation 259

12.3 Related Work 260

12.4 Proposed Methodology and Experimental Results Discussion 262

12.4.1 Mobile Cloud Computing 266

12.4.2 XML Parsing and Computation Offloading 266

12.4.3 Energy Analysis for Computation Offloading 267

12.4.4 Virtual Database 269

12.4.5 App Engine 270

12.4.6 User Interface 272

12.4.7 Securing Data 273

12.5 Conclusion and Future Work 274

References 274

13 SSAMH – A Systematic Survey on AI-Enabled Cyber Physical Systems in Healthcare 277
Kamalpreet Kaur, Renu Dhir and Mariya Ouaissa

13.1 Introduction 278

13.2 The Architecture of Medical Cyber-Physical Systems 278

13.3 Artificial Intelligence-Driven Medical Devices 282

13.3.1 Monitoring Devices 282

13.3.2 Delivery Devices 283

13.3.3 Network Medical Device Systems 283

13.3.4 IT-Based Medical Device Systems 284

13.3.5 Wireless Sensor Network-Based Medical Driven Systems 285

13.4 Certification and Regulation Issues 285

13.5 Big Data Platform for Medical Cyber-Physical Systems 286

13.6 The Emergence of New Trends in Medical Cyber-Physical Systems 288

13.7 Eminence Attributes and Challenges 289

13.8 High-Confidence Expansion of a Medical Cyber-Physical Expansion 290

13.9 Role of the Software Platform in the Interoperability of Medical Devices 291

13.10 Clinical Acceptable Decision Support Systems 291

13.11 Prevalent Attacks in the Medical Cyber-Physical Systems 292

13.12 A Suggested Framework for Medical Cyber-Physical System 294

13.13 Conclusion 295

References 296

14 ANN-Aware Methanol Detection Approach with CuO-Doped SnO 2 in Gas Sensor 299
Jitendra K. Srivastava, Deepak Kumar Verma, Bholey Nath Prasad and Chayan Kumar Mishra

14.1 Introduction 300

14.1.1 Basic ANN Model 300

14.1.2 ANN Data Pre- and Post-Processing 303

14.1.2.1 Activation Function 304

14.2 Network Architectures 305

14.2.1 Feed Forward ANNs 305

14.2.2 Recurrent ANNs Topologies 307

14.2.3 Learning Processes 308

14.2.3.1 Supervised Learning 308

14.2.3.2 Unsupervised Learning 308

14.2.4 ANN Methodology 309

14.2.5 1%CuO–Doped SnO 2 Sensor for Methanol 309

14.2.6 Experimental Result 311

References 327

15 Detecting Heart Arrhythmias Using Deep Learning Algorithms 331
Dilip Kumar Choubey, Chandan Kumar Jha, Niraj Kumar, Neha Kumari and Vaibhav Soni

15.1 Introduction 332

15.1.1 Deep Learning 333

15.2 Motivation 334

15.3 Literature Review 334

15.4 Proposed Approach 366

15.4.1 Dataset Descriptions 367

15.4.2 Algorithms Description 369

15.4.2.1 Dense Neural Network 369

15.4.2.2 Convolutional Neural Network 370

15.4.2.3 Long Short-Term Memory 372

15.5 Experimental Results of Proposed Approach 376

15.6 Conclusion and Future Scope 379

References 380

16 Artificial Intelligence Approach for Signature Detection 387
Amar Shukla, Rajeev Tiwari, Saurav Raghuvanshi, Shivam Sharma and Shridhar Avinash

16.1 Introduction 387

16.2 Literature Review 390

16.3 Problem Definition 392

16.4 Methodology 392

16.4.1 Data Flow Process 394

16.4.2 Algorithm 395

16.5 Result Analysis 397

16.6 Conclusion 399

References 399

17 Comparison of Various Classification Models Using Machine Learning to Predict Mobile Phones Price Range 401
Chinu Singla and Chirag Jindal

17.1 Introduction 402

17.2 Materials and Methods 403

17.2.1 Dataset 403

17.2.2 Decision Tree 403

17.2.2.1 Basic Algorithm 404

17.2.3 Gaussian Naive Bayes (GNB) 404

17.2.3.1 Basic Algorithm 405

17.2.4 Support Vector Machine 405

17.2.4.1 Basic Algorithm 406

17.2.5 Logistic Regression (LR) 407

17.2.5.1 Basic Algorithm 407

17.2.6 K-Nearest Neighbor 408

17.2.6.1 Basic Algorithm 409

17.2.7 Evaluation Metrics 409

17.3 Application of the Model 410

17.3.1 Decision Tree (DT) 411

17.3.2 Gaussian Naive Bayes 411

17.3.3 Support Vector Machine 412

17.3.4 Logistic Regression 412

17.3.5 K Nearest Neighbor 413

17.4 Results and Comparison 413

17.5 Conclusion and Future Scope 418

References 418

Index 421