Integration of Cloud Computing with Internet of Things

Monika Mangla PhD is an Assistant Professor in the Department of Computer Engineering at Lokmanya Tilak College of Engineering (LTCoE), Mumbai, India. Her research areas include IoT, cloud computing, algorithms and optimization, location modelling and machine learning.Suneeta Satpathy PhD is an Associate Professor in the Department of Computer Science & Engineering at College of Engineering Bhubaneswar (CoEB), Bhubaneswar. Her research interests include computer forensics, cybersecurity, data fusion, data mining, big data analysis, and decision mining.Bhagirathi Nayak has 25 years of experience in the areas of computer science and engineering and database designing. Prof. Nayak earned his PhD in Computer Science from IIT Kharagpur. He is currently associated with Sri Sri University, Cuttack as head of the Department of Information & Communication Technology. He has obtained five patents in the area of computer science and engineering and his areas of interest are data mining, big data analytics, artificial intelligence and machine learning.Sachi Nandan Mohanty obtained his PhD from IIT Kharagpur in 2015 and is now an Associate Professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education Hyderabad. Dr. Mohanty’s research areas include data mining, big data analysis, cognitive science, fuzzy decision making, brain-computer interface, and computational intelligence.

• Preface xvAcknowledgement xvii1 Internet of Things: A Key to Unfasten Mundane Repetitive Tasks 1Hemanta Kumar Palo and Limali Sahoo1.1 Introduction 11.2 The IoT Scenario 21.3 The IoT Domains 31.3.1 The IoT Policy Domain 31.3.2 The IoT Software Domain 51.3.2.1 IoT in Cloud Computing (CC) 51.3.2.2 IoT in Edge Computing (EC) 61.3.2.3 IoT in Fog Computing (FC) 101.3.2.4 IoT in Telecommuting 111.3.2.5 IoT in Data-Center 121.3.2.6 Virtualization-Based IoT (VBIoT) 121.4 Green Computing (GC) in IoT Framework 121.5 Semantic IoT (SIoT) 131.5.1 Standardization Using oneM2M 151.5.2 Semantic Interoperability (SI) 181.5.3 Semantic Interoperability (SI) 191.5.4 Semantic IoT vs Machine Learning 201.6 Conclusions 21References 212 Measures for Improving IoT Security 25Richa Goel, Seema Sahai, Gurinder Singh and Saurav Lall2.1 Introduction 252.2 Perceiving IoT Security 262.3 The IoT Safety Term 272.4 Objectives 282.4.1 Enhancing Personal Data Access in Public Repositories 282.4.2 Develop and Sustain Ethicality 282.4.3 Maximize the Power of IoT Access 292.4.4 Understanding Importance of Firewalls 292.5 Research Methodology 302.6 Security Challenges 312.6.1 Challenge of Data Management 322.7 Securing IoT 332.7.1 Ensure User Authentication 332.7.2 Increase User Autonomy 332.7.3 Use of Firewalls 342.7.4 Firewall Features 352.7.5 Mode of Camouflage 352.7.6 Protection of Data 352.7.7 Integrity in Service 362.7.8 Sensing of Infringement 362.8 Monitoring of Firewalls and Good Management 362.8.1 Surveillance 362.8.2 Forensics 372.8.3 Secure Firewalls for Private 372.8.4 Business Firewalls for Personal 372.8.5 IoT Security Weaknesses 372.9 Conclusion 37References 383 An Efficient Fog-Based Model for Secured Data Communication 41V. Lakshman Narayana and R. S. M. Lakshmi Patibandla3.1 Introduction 413.1.1 Fog Computing Model 423.1.2 Correspondence in IoT Devices 433.2 Attacks in IoT 453.2.1 Botnets 453.2.2 Man-In-The-Middle Concept 453.2.3 Data and Misrepresentation 463.2.4 Social Engineering 463.2.5 Denial of Service 463.2.6 Concerns 473.3 Literature Survey 483.4 Proposed Model for Attack Identification Using Fog Computing 493.5 Performance Analysis 523.6 Conclusion 54References 544 An Expert System to Implement Symptom Analysis in Healthcare 57Subhasish Mohapatra and Kunal Anand4.1 Introduction 574.2 Related Work 594.3 Proposed Model Description and Flow Chart 604.3.1 Flowchart of the Model 604.3.1.1 Value of Symptoms 604.3.1.2 User Interaction Web Module 604.3.1.3 Knowledge-Base 604.3.1.4 Convolution Neural Network 604.3.1.5 CNN-Fuzzy Inference Engine 614.4 UML Analysis of Expert Model 624.4.1 Expert Module Activity Diagram 634.4.2 Ontology Class Collaboration Diagram 654.5 Ontology Model of Expert Systems 664.6 Conclusion and Future Scope 67References 685 An IoT-Based Gadget for Visually Impaired People 71Prakash, N., Udayakumar, E., Kumareshan, N., Srihari, K. and Sachi Nandan Mohanty5.1 Introduction 715.2 Related Work 735.3 System Design 745.4 Results and Discussion 825.5 Conclusion 845.6 Future Work 84References 846 IoT Protocol for Inferno Calamity in Public Transport 87Ravi Babu Devareddi, R. Shiva Shankar and Gadiraju Mahesh6.1 Introduction 876.2 Literature Survey 896.3 Methodology 946.3.1 IoT Message Exchange With Cloud MQTT Broker Based on MQTT Protocol 986.3.2 Hardware Requirement 986.4 Implementation 1036.4.1 Interfacing Diagram 1056.5 Results 1066.6 Conclusion and Future Work 108References 1097 Traffic Prediction Using Machine Learning and IoT 111Daksh Pratap Singh and Dolly Sharma7.1 Introduction 1117.1.1 Real Time Traffic 1117.1.2 Traffic Simulation 1127.2 Literature Review 1127.3 Methodology 1137.4 Architecture 1167.4.1 API Architecture 1177.4.2 File Structure 1177.4.3 Simulator Architecture 1187.4.4 Workflow in Application 1227.4.5 Workflow of Google APIs in the Application 1227.5 Results 1227.5.1 Traffic Scenario 1227.5.1.1 Low Traffic 1247.5.1.2 Moderate Traffic 1247.5.1.3 High Traffic 1257.5.2 Speed Viewer 1257.5.3 Traffic Simulator 1267.5.3.1 1st View 1267.5.3.2 2nd View 1287.5.3.3 3rd View 1287.6 Conclusion and Future Scope 128References 1298 Application of Machine Learning in Precision Agriculture 131Ravi Sharma and Nonita Sharma8.1 Introduction 1318.2 Machine Learning 1328.2.1 Supervised Learning 1338.2.2 Unsupervised Learning 1338.2.3 Reinforcement Learning 1348.3 Agriculture 1348.4 ML Techniques Used in Agriculture 1358.4.1 Soil Mapping 1358.4.2 Seed Selection 1408.4.3 Irrigation/Water Management 1418.4.4 Crop Quality 1438.4.5 Disease Detection 1448.4.6 Weed Detection 1458.4.7 Yield Prediction 1478.5 Conclusion 148References 1499 An IoT-Based Multi Access Control and Surveillance for Home Security 153Yogeshwaran, K., Ramesh, C., Udayakumar, E., Srihari, K. and Sachi Nandan Mohanty9.1 Introduction 1539.2 Related Work 1559.3 Hardware Description 1569.3.1 Float Sensor 1589.3.2 Map Matching 1589.3.3 USART Cable 1599.4 Software Design 1619.5 Conclusion 162References 16210 Application of IoT in Industry 4.0 for Predictive Analytics 165Ahin Banerjee, Debanshee Datta and Sanjay K. Gupta10.1 Introduction 16510.2 Past Literary Works 16810.2.1 Maintenance-Based Monitoring 16810.2.2 Data Driven Approach to RUL Finding in Industry 16910.2.3 Philosophy of Industrial-IoT Systems and its Advantages in Different Domain 17310.3 Methodology and Results 17610.4 Conclusion 179References 18011 IoT and Its Role in Performance Enhancement in Business Organizations 183Seema Sahai, Richa Goel, Parul Bajaj and Gurinder Singh11.1 Introduction 18311.1.1 Scientific Issues in IoT 18411.1.2 IoT in Organizations 18511.1.3 Technology and Business 18711.1.4 Rewards of Technology in Business 18711.1.5 Shortcomings of Technology in Business 18811.1.6 Effect of IoT on Work and Organization 18811.2 Technology and Productivity 19011.3 Technology and Future of Human Work 19311.4 Technology and Employment 19411.5 Conclusion 195References 19512 An Analysis of Cloud Computing Based on Internet of Things 197Farhana Ajaz, Mohd Naseem, Ghulfam Ahamad, Sparsh Sharma and Ehtesham Abbasi12.1 Introduction 19712.1.1 Generic Architecture 19912.2 Challenges in IoT 20212.3 Technologies Used in IoT 20312.4 Cloud Computing 20312.4.1 Service Models of Cloud Computing 20412.5 Cloud Computing Characteristics 20512.6 Applications of Cloud Computing 20612.7 Cloud IoT 20712.8 Necessity for Fusing IoT and Cloud Computing 20712.9 Cloud-Based IoT Architecture 20812.10 Applications of Cloud-Based IoT 20812.11 Conclusion 209References 20913 Importance of Fog Computing in Emerging Technologies-IoT 211Aarti Sahitya13.1 Introduction 21113.2 IoT Core 21213.3 Need of Fog Computing 227References 23014 Convergence of Big Data and Cloud Computing Environment 233Ranjan Ganguli14.1 Introduction 23314.2 Big Data: Historical View 23414.2.1 Big Data: Definition 23514.2.2 Big Data Classification 23614.2.3 Big Data Analytics 23614.3 Big Data Challenges 23714.4 The Architecture 23814.4.1 Storage or Collection System 24014.4.2 Data Care 24014.4.3 Analysis 24014.5 Cloud Computing: History in a Nutshell 24114.5.1 View on Cloud Computing and Big Data 24114.6 Insight of Big Data and Cloud Computing 24114.6.1 Cloud-Based Services 24214.6.2 At a Glance: Cloud Services 24414.7 Cloud Framework 24514.7.1 Hadoop 24514.7.2 Cassandra 24614.7.2.1 Features of Cassandra 24614.7.3 Voldemort 24714.7.3.1 A Comparison With Relational Databases and Benefits 24714.8 Conclusions 24814.9 Future Perspective 248References 24815 Data Analytics Framework Based on Cloud Environment 251K. Kanagaraj and S. Geetha15.1 Introduction 25115.2 Focus Areas of the Chapter 25215.3 Cloud Computing 25215.3.1 Cloud Service Models 25315.3.1.1 Software as a Service (SaaS) 25315.3.1.2 Platform as a Service (PaaS) 25415.3.1.3 Infrastructure as a Service (IaaS) 25515.3.1.4 Desktop as a Service (DaaS) 25615.3.1.5 Analytics as a Service (AaaS) 25715.3.1.6 Artificial Intelligence as a Service (AIaaS) 25815.3.2 Cloud Deployment Models 25915.3.3 Virtualization of Resources 26015.3.4 Cloud Data Centers 26115.4 Data Analytics 26315.4.1 Data Analytics Types 26315.4.1.1 Descriptive Analytics 26315.4.1.2 Diagnostic Analytics 26415.4.1.3 Predictive Analytics 26515.4.1.4 Prescriptive Analytics 26515.4.1.5 Big Data Analytics 26515.4.1.6 Augmented Analytics 26615.4.1.7 Cloud Analytics 26615.4.1.8 Streaming Analytics 26615.4.2 Data Analytics Tools 26615.5 Real-Time Data Analytics Support in Cloud 26615.6 Framework for Data Analytics in Cloud 26815.6.1 Data Analysis Software as a Service (DASaaS) 26815.6.2 Data Analysis Platform as a Service (DAPaaS) 26815.6.3 Data Analysis Infrastructure as a Service (DAIaaS) 26915.7 Data Analytics Work-Flow 26915.8 Cloud-Based Data Analytics Tools 27015.8.1 Amazon Kinesis Services 27115.8.2 Amazon Kinesis Data Firehose 27115.8.3 Amazon Kinesis Data Streams 27115.8.4 Amazon Textract 27115.8.5 Azure Stream Analytics 27115.9 Experiment Results 27215.10 Conclusion 272References 27416 Neural Networks for Big Data Analytics 277Bithika Bishesh16.1 Introduction 27716.2 Neural Networks—An Overview 27816.3 Why Study Neural Networks? 27916.4 Working of Artificial Neural Networks 27916.4.1 Single-Layer Perceptron 27916.4.2 Multi-Layer Perceptron 28016.4.3 Training a Neural Network 28116.4.4 Gradient Descent Algorithm 28216.4.5 Activation Functions 28416.5 Innovations in Neural Networks 28816.5.1 Convolutional Neural Network (ConvNet) 28816.5.2 Recurrent Neural Network 28916.5.3 LSTM 29116.6 Applications of Deep Learning Neural Networks 29216.7 Practical Application of Neural Networks Using Computer Codes 29316.8 Opportunities and Challenges of Using Neural Networks 29316.9 Conclusion 296References 29617 Meta-Heuristic Algorithms for Best IoT Cloud Service Platform Selection 299Sudhansu Shekhar Patra, Sudarson Jena, G.B. Mund, Mahendra Kumar Gourisaria and Jugal Kishor Gupta17.1 Introduction 29917.2 Selection of a Cloud Provider in Federated Cloud 30117.3 Algorithmic Solution 30717.3.1 TLBO Algorithm (Teaching-Learning-Based Optimization Algorithm) 30717.3.1.1 Teacher Phase: Generation of a New Solution 30817.3.1.2 Learner Phase: Generation of New Solution 30917.3.1.3 Representation of the Solution 30917.3.2 JAYA Algorithm 30917.3.2.1 Representation of the Solution 31117.3.3 Bird Swarm Algorithm 31117.3.3.1 Forging Behavior 31317.3.3.2 Vigilance Behavior 31317.3.3.3 Flight Behavior 31317.3.3.4 Representation of the Solution 31317.4 Analyzing the Algorithms 31417.5 Conclusion 316References 31618 Legal Entanglements of Cloud Computing In India 319Sambhabi Patnaik and Lipsa Dash18.1 Cloud Computing Technology 31918.2 Cyber Security in Cloud Computing 32218.3 Security Threats in Cloud Computing 32318.3.1 Data Breaches 32318.3.2 Denial of Service (DoS) 32318.3.3 Botnets 32318.3.4 Crypto Jacking 32418.3.5 Insider Threats 32418.3.6 Hijacking Accounts 32418.3.7 Insecure Applications 32418.3.8 Inadequate Training 32518.3.9 General Vulnerabilities 32518.4 Cloud Security Probable Solutions 32518.4.1 Appropriate Cloud Model for Business 32518.4.2 Dedicated Security Policies Plan 32518.4.3 Multifactor Authentication 32518.4.4 Data Accessibility 32618.4.5 Secure Data Destruction 32618.4.6 Encryption of Backups 32618.4.7 Regulatory Compliance 32618.4.8 External Third-Party Contracts and Agreements 32718.5 Cloud Security Standards 32718.6 Cyber Security Legal Framework in India 32718.7 Privacy in Cloud Computing—Data Protection Standards 32918.8 Recognition of Right to Privacy 33018.9 Government Surveillance Power vs Privacy of Individuals 33218.10 Data Ownership and Intellectual Property Rights 33318.11 Cloud Service Provider as an Intermediary 33518.12 Challenges in Cloud Computing 33718.12.1 Classification of Data 33718.12.2 Jurisdictional Issues 33718.12.3 Interoperability of the Cloud 33818.12.4 Vendor Agreements 33918.13 Conclusion 339References 34119 Securing the Pharma Supply Chain Using Blockchain 343Pulkit Arora, Chetna Sachdeva and Dolly Sharma19.1 Introduction 34319.2 Literature Review 34519.2.1 Current Scenario 34619.2.2 Proposal 34719.3 Methodology 34919.4 Results 35419.5 Conclusion and Future Scope 358References 358Index 361