CONVERGENCE of CLOUD with AI for BIG DATA ANALYTICS This book covers the foundations and applications of cloud computing, AI, and Big Data and analyses their convergence for improved development and services.
The 17 chapters of the book masterfully and comprehensively cover the intertwining concepts of artificial intelligence, cloud computing, and big data, all of which have recently emerged as the next-generation paradigms. There has been rigorous growth in their applications and the hybrid blend of AI Cloud and IoT (Ambient-intelligence technology) also relies on input from wireless devices. Despite the multitude of applications and advancements, there are still some limitations and challenges to overcome, such as security, latency, energy consumption, service allocation, healthcare services, network lifetime, etc. Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation details all these technologies and how they are related to state-of-the-art applications, and provides a comprehensive overview for readers interested in advanced technologies, identifying the challenges, proposed solutions, as well as how to enhance the framework.
Audience
Researchers and post-graduate students in computing as well as engineers and practitioners in software engineering, electrical engineers, data analysts, and cyber security professionals.
Table of Contents:
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
