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 xv1 Integration of Artificial Intelligence, Big Data, and Cloud Computing with Internet of Things 1Jaydip Kumar1.1 Introduction 21.2 Roll of Artificial Intelligence, Big Data and Cloud Computing in IoT 31.3 Integration of Artificial Intelligence with the Internet of Things Devices 41.4 Integration of Big Data with the Internet of Things 61.5 Integration of Cloud Computing with the Internet of Things 61.6 Security of Internet of Things 81.7 Conclusion 10References 102 Cloud Computing and Virtualization 13Sudheer Mangalampalli, Pokkuluri Kiran Sree, Sangram K. Swain and Ganesh Reddy Karri2.1 Introduction to Cloud Computing 142.1.1 Need of Cloud Computing 142.1.2 History of Cloud Computing 142.1.3 Definition of Cloud Computing 152.1.4 Different Architectures of Cloud Computing 162.1.4.1 Generic Architecture of Cloud Computing 162.1.4.2 Market Oriented Architecture of Cloud Computing 172.1.5 Applications of Cloud Computing in Different Domains 182.1.5.1 Cloud Computing in Healthcare 182.5.1.2 Cloud Computing in Education 192.5.1.3 Cloud Computing in Entertainment Services 192.5.1.4 Cloud Computing in Government Services 192.1.6 Service Models in Cloud Computing 192.1.7 Deployment Models in Cloud Computing 212.2 Virtualization 222.2.1 Need of Virtualization in Cloud Computing 222.2.2 Architecture of a Virtual Machine 232.2.3 Advantages of Virtualization 242.2.4 Different Implementation Levels of Virtualization 252.2.4.1 Instruction Set Architecture Level 252.2.4.2 Hardware Level 262.2.4.3 Operating System Level 262.2.4.4 Library Level 262.2.4.5 Application Level 262.2.5 Server Consolidation Using Virtualization 262.2.6 Task Scheduling in Cloud Computing 272.2.7 Proposed System Architecture 312.2.8 Mathematical Modeling of Proposed Task Scheduling Algorithm 312.2.9 Multi Objective Optimization 342.2.10 Chaotic Social Spider Algorithm 342.2.11 Proposed Task Scheduling Algorithm 352.2.12 Simulation and Results 362.2.12.1 Calculation of Makespan 362.2.12.2 Calculation of Energy Consumption 372.3 Conclusion 37References 383 Time and Cost-Effective Multi-Objective Scheduling Technique for Cloud Computing Environment 41Aida A. Nasr, Kalka Dubey, Nirmeen El-Bahnasawy, Gamal Attiya and Ayman El-Sayed3.1 Introduction 423.2 Literature Survey 443.3 Cloud Computing and Cloudlet Scheduling Problem 463.4 Problem Formulation 473.5 Cloudlet Scheduling Techniques 493.5.1 Heuristic Methods 503.5.2 Meta-Heuristic Methods 513.6 Cloudlet Scheduling Approach (CSA) 523.6.1 Proposed CSA 523.6.2 Time Complexity 533.6.3 Case Study 543.7 Simulation Results 563.7.1 Simulation Environment 563.7.2 Evaluation Metrics 563.7.2.1 Performance Evaluation with Small Number of Cloudlets 573.7.2.2 Performance Evaluation with Large Number of Cloudlets 573.8 Conclusion 64References 644 Cloud-Based Architecture for Effective Surveillance and Diagnosis of COVID- 19 69Shweta Singh, Aditya Bhardwaj, Ishan Budhiraja, Umesh Gupta and Indrajeet Gupta4.1 Introduction 704.2 Related Work 714.2.1 Proposed Cloud-Based Network for Management of COVID- 19 734.3 Research Methodology 754.3.1 Sample Size and Target 764.3.1.1 Sampling Procedures 774.3.1.2 Response Rate 774.3.1.3 Instrument and Measures 774.3.2 Reliability and Validity Test 784.3.3 Exploratory Factor Analysis 784.4 Survey Findings 804.4.1 Outcomes of the Proposed Scenario 824.4.1.1 Online Monitoring 824.4.1.2 Location Tracking 824.4.1.3 Alarm Linkage 824.4.1.4 Command and Control 824.4.1.5 Plan Management 824.4.1.6 Security Privacy 834.4.1.7 Remote Maintenance 834.4.1.8 Online Upgrade 834.4.1.9 Command Management 834.4.1.10 Statistical Decision 834.4.2 Experimental Setup 834.5 Conclusion and Future Scope 85References 865 Smart Agriculture Applications Using Cloud and IoT 89Keshav Kaushik5.1 Role of IoT and Cloud in Smart Agriculture 895.2 Applications of IoT and Cloud in Smart Agriculture 945.3 Security Challenges in Smart Agriculture 975.4 Open Research Challenges for IoT and Cloud in Smart Agriculture 1005.5 Conclusion 103References 1036 Applications of Federated Learning in Computing Technologies 107Sambit Kumar Mishra, Kotipalli Sindhu, Mogaparthi Surya Teja, Vutukuri Akhil, Ravella Hari Krishna, Pakalapati Praveen and Tapas Kumar Mishra6.1 Introduction 1086.1.1 Federated Learning in Cloud Computing 1086.1.1.1 Cloud-Mobile Edge Computing 1096.1.1.2 Cloud Edge Computing 1116.1.2 Federated Learning in Edge Computing 1126.1.2.1 Vehicular Edge Computing 1136.1.2.2 Intelligent Recommendation 1136.1.3 Federated Learning in IoT (Internet of Things) 1146.1.3.1 Federated Learning for Wireless Edge Intelligence 1146.1.3.2 Federated Learning for Privacy Protected Information 1156.1.4 Federated Learning in Medical Computing Field 1166.1.4.1 Federated Learning in Medical Healthcare 1176.1.4.2 Data Privacy in Healthcare 1176.1.5 Federated Learning in Blockchain 1186.1.5.1 Blockchain-Based Federated Learning Against End-Point Adversarial Data 1186.2 Advantages of Federated Learning 1196.3 Conclusion 119References 1197 Analyzing the Application of Edge Computing in Smart Healthcare 121Parul Verma and Umesh Kumar7.1 Internet of Things (IoT) 1227.1.1 IoT Communication Models 1227.1.2 IoT Architecture 1247.1.3 Protocols for IoT 1257.1.3.1 Physical/Data Link Layer Protocols 1257.1.3.2 Network Layer Protocols 1277.1.3.3 Transport Layer Protocols 1287.1.3.4 Application Layer Protocols 1297.1.4 IoT Applications 1307.1.5 IoT Challenges 1327.2 Edge Computing 1337.2.1 Cloud vs. Fog vs. Edge 1347.2.2 Existing Edge Computing Reference Architecture 1357.2.2.1 FAR-EDGE Reference Architecture 1357.2.2.2 Intel-SAP Joint Reference Architecture (RA) 1357.2.3 Integrated Architecture for IoT and Edge 1367.2.4 Benefits of Edge Computing Based IoT Architecture 1387.3 Edge Computing and Real Time Analytics in Healthcare 1407.4 Edge Computing Use Cases in Healthcare 1487.5 Future of Healthcare and Edge Computing 1517.6 Conclusion 151References 1528 Fog-IoT Assistance-Based Smart Agriculture Application 157Pawan Whig, Arun Velu and Rahul Reddy Nadikattu8.1 Introduction 1588.1.1 Difference Between Fog and Edge Computing 1598.1.1.1 Bandwidth 1638.1.1.2 Confidence 1648.1.1.3 Agility 1648.1.2 Relation of Fog with IoT 1658.1.3 Fog Computing in Agriculture 1678.1.4 Fog Computing in Smart Cities 1698.1.5 Fog Computing in Education 1708.1.6 Case Study 171Conclusion and Future Scope 173References 1739 Internet of Things in the Global Impacts of COVID-19: A Systematic Study 177Shalini Sharma Goel, Anubhav Goel, Mohit Kumar and Sachin Sharma9.1 Introduction 1789.2 COVID-19 – Misconceptions 1819.3 Global Impacts of COVID-19 and Significant Contributions of IoT in Respective Domains to Counter the Pandemic 1839.3.1 Impact on Healthcare and Major Contributions of IoT 1839.3.2 Social Impacts of COVID-19 and Role of IoT 1879.3.3 Financial and Economic Impact and How IoT Can Help to Shape Businesses 1889.3.4 Impact on Education and Part Played by IoT 1919.3.5 Impact on Climate and Environment and Indoor Air Quality Monitoring Using IoT 1949.3.6 Impact on Travel and Tourism and Aviation Industry and How IoT is Shaping its Future 1979.4 Conclusions 198References 19810 An Efficient Solar Energy Management Using IoT-Enabled Arduino-Based MPPT Techniques 205Rita Banik and Ankur BiswasList of Symbols 20610.1 Introduction 20610.2 Impact of Irradiance on PV Efficiency 21010.2.1 PV Reliability and Irradiance Optimization 21110.2.1.1 PV System Level Reliability 21110.2.1.2 PV Output with Varying Irradiance 21110.2.1.3 PV Output with Varying Tilt 21210.3 Design and Implementation 21210.3.1 The DC to DC Buck Converter 21510.3.2 The Arduino Microcontroller 21710.3.3 Dynamic Response 21910.4 Result and Discussions 22010.5 Conclusions 223References 22411 Axiomatic Analysis of Pre-Processing Methodologies Using Machine Learning in Text Mining: A Social Media Perspective in Internet of Things 229Tajinder Singh, Madhu Kumari, Daya Sagar Gupta and Nikolai Siniak11.1 Introduction 23011.2 Text Pre-Processing – Role and Characteristics 23211.3 Modern Pre-Processing Methodologies and Their Scope 23411.4 Text Stream and Role of Clustering in Social Text Stream 24111.5 Social Text Stream Event Analysis 24211.6 Embedding 24411.6.1 Type of Embeddings 24411.7 Description of Twitter Text Stream 25011.8 Experiment and Result 25111.9 Applications of Machine Learning in IoT (Internet of Things) 25111.10 Conclusion 252References 25212 APP-Based Agriculture Information System for Rural Farmers in India 257Ashwini Kumar, Dilip Kumar Choubey, Manish Kumar and Santosh Kumar12.1 Introduction 25812.2 Motivation 25912.3 Related Work 26012.4 Proposed Methodology and Experimental Results Discussion 26212.4.1 Mobile Cloud Computing 26612.4.2 XML Parsing and Computation Offloading 26612.4.3 Energy Analysis for Computation Offloading 26712.4.4 Virtual Database 26912.4.5 App Engine 27012.4.6 User Interface 27212.4.7 Securing Data 27312.5 Conclusion and Future Work 274References 27413 SSAMH – A Systematic Survey on AI-Enabled Cyber Physical Systems in Healthcare 277Kamalpreet Kaur, Renu Dhir and Mariya Ouaissa13.1 Introduction 27813.2 The Architecture of Medical Cyber-Physical Systems 27813.3 Artificial Intelligence-Driven Medical Devices 28213.3.1 Monitoring Devices 28213.3.2 Delivery Devices 28313.3.3 Network Medical Device Systems 28313.3.4 IT-Based Medical Device Systems 28413.3.5 Wireless Sensor Network-Based Medical Driven Systems 28513.4 Certification and Regulation Issues 28513.5 Big Data Platform for Medical Cyber-Physical Systems 28613.6 The Emergence of New Trends in Medical Cyber-Physical Systems 28813.7 Eminence Attributes and Challenges 28913.8 High-Confidence Expansion of a Medical Cyber-Physical Expansion 29013.9 Role of the Software Platform in the Interoperability of Medical Devices 29113.10 Clinical Acceptable Decision Support Systems 29113.11 Prevalent Attacks in the Medical Cyber-Physical Systems 29213.12 A Suggested Framework for Medical Cyber-Physical System 29413.13 Conclusion 295References 29614 ANN-Aware Methanol Detection Approach with CuO-Doped SnO 2 in Gas Sensor 299Jitendra K. Srivastava, Deepak Kumar Verma, Bholey Nath Prasad and Chayan Kumar Mishra14.1 Introduction 30014.1.1 Basic ANN Model 30014.1.2 ANN Data Pre- and Post-Processing 30314.1.2.1 Activation Function 30414.2 Network Architectures 30514.2.1 Feed Forward ANNs 30514.2.2 Recurrent ANNs Topologies 30714.2.3 Learning Processes 30814.2.3.1 Supervised Learning 30814.2.3.2 Unsupervised Learning 30814.2.4 ANN Methodology 30914.2.5 1%CuO–Doped SnO 2 Sensor for Methanol 30914.2.6 Experimental Result 311References 32715 Detecting Heart Arrhythmias Using Deep Learning Algorithms 331Dilip Kumar Choubey, Chandan Kumar Jha, Niraj Kumar, Neha Kumari and Vaibhav Soni15.1 Introduction 33215.1.1 Deep Learning 33315.2 Motivation 33415.3 Literature Review 33415.4 Proposed Approach 36615.4.1 Dataset Descriptions 36715.4.2 Algorithms Description 36915.4.2.1 Dense Neural Network 36915.4.2.2 Convolutional Neural Network 37015.4.2.3 Long Short-Term Memory 37215.5 Experimental Results of Proposed Approach 37615.6 Conclusion and Future Scope 379References 38016 Artificial Intelligence Approach for Signature Detection 387Amar Shukla, Rajeev Tiwari, Saurav Raghuvanshi, Shivam Sharma and Shridhar Avinash16.1 Introduction 38716.2 Literature Review 39016.3 Problem Definition 39216.4 Methodology 39216.4.1 Data Flow Process 39416.4.2 Algorithm 39516.5 Result Analysis 39716.6 Conclusion 399References 39917 Comparison of Various Classification Models Using Machine Learning to Predict Mobile Phones Price Range 401Chinu Singla and Chirag Jindal17.1 Introduction 40217.2 Materials and Methods 40317.2.1 Dataset 40317.2.2 Decision Tree 40317.2.2.1 Basic Algorithm 40417.2.3 Gaussian Naive Bayes (GNB) 40417.2.3.1 Basic Algorithm 40517.2.4 Support Vector Machine 40517.2.4.1 Basic Algorithm 40617.2.5 Logistic Regression (LR) 40717.2.5.1 Basic Algorithm 40717.2.6 K-Nearest Neighbor 40817.2.6.1 Basic Algorithm 40917.2.7 Evaluation Metrics 40917.3 Application of the Model 41017.3.1 Decision Tree (DT) 41117.3.2 Gaussian Naive Bayes 41117.3.3 Support Vector Machine 41217.3.4 Logistic Regression 41217.3.5 K Nearest Neighbor 41317.4 Results and Comparison 41317.5 Conclusion and Future Scope 418References 418Index 421