Convergence of Cloud with AI for Big Data Analytics

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