Preface xv Acknowledgement xvii 1 Internet of Things: A Key to Unfasten Mundane Repetitive Tasks 1Hemanta Kumar Palo and Limali Sahoo 1.1 Introduction 1 1.2 The IoT Scenario 2 1.3 The IoT Domains 3 1.3.1 The IoT Policy Domain 3 1.3.2 The IoT Software Domain 5 1.3.2.1 IoT in Cloud Computing (CC) 5 1.3.2.2 IoT in Edge Computing (EC) 6 1.3.2.3 IoT in Fog Computing (FC) 10 1.3.2.4 IoT in Telecommuting 11 1.3.2.5 IoT in Data-Center 12 1.3.2.6 Virtualization-Based IoT (VBIoT) 12 1.4 Green Computing (GC) in IoT Framework 12 1.5 Semantic IoT (SIoT) 13 1.5.1 Standardization Using oneM2M 15 1.5.2 Semantic Interoperability (SI) 18 1.5.3 Semantic Interoperability (SI) 19 1.5.4 Semantic IoT vs Machine Learning 20 1.6 Conclusions 21 References 21 2 Measures for Improving IoT Security 25Richa Goel, Seema Sahai, Gurinder Singh and Saurav Lall 2.1 Introduction 25 2.2 Perceiving IoT Security 26 2.3 The IoT Safety Term 27 2.4 Objectives 28 2.4.1 Enhancing Personal Data Access in Public Repositories 28 2.4.2 Develop and Sustain Ethicality 28 2.4.3 Maximize the Power of IoT Access 29 2.4.4 Understanding Importance of Firewalls 29 2.5 Research Methodology 30 2.6 Security Challenges 31 2.6.1 Challenge of Data Management 32 2.7 Securing IoT 33 2.7.1 Ensure User Authentication 33 2.7.2 Increase User Autonomy 33 2.7.3 Use of Firewalls 34 2.7.4 Firewall Features 35 2.7.5 Mode of Camouflage 35 2.7.6 Protection of Data 35 2.7.7 Integrity in Service 36 2.7.8 Sensing of Infringement 36 2.8 Monitoring of Firewalls and Good Management 36 2.8.1 Surveillance 36 2.8.2 Forensics 37 2.8.3 Secure Firewalls for Private 37 2.8.4 Business Firewalls for Personal 37 2.8.5 IoT Security Weaknesses 37 2.9 Conclusion 37 References 38 3 An Efficient Fog-Based Model for Secured Data Communication 41V. Lakshman Narayana and R. S. M. Lakshmi Patibandla 3.1 Introduction 41 3.1.1 Fog Computing Model 42 3.1.2 Correspondence in IoT Devices 43 3.2 Attacks in IoT 45 3.2.1 Botnets 45 3.2.2 Man-In-The-Middle Concept 45 3.2.3 Data and Misrepresentation 46 3.2.4 Social Engineering 46 3.2.5 Denial of Service 46 3.2.6 Concerns 47 3.3 Literature Survey 48 3.4 Proposed Model for Attack Identification Using Fog Computing 49 3.5 Performance Analysis 52 3.6 Conclusion 54 References 54 4 An Expert System to Implement Symptom Analysis in Healthcare 57Subhasish Mohapatra and Kunal Anand 4.1 Introduction 57 4.2 Related Work 59 4.3 Proposed Model Description and Flow Chart 60 4.3.1 Flowchart of the Model 60 4.3.1.1 Value of Symptoms 60 4.3.1.2 User Interaction Web Module 60 4.3.1.3 Knowledge-Base 60 4.3.1.4 Convolution Neural Network 60 4.3.1.5 CNN-Fuzzy Inference Engine 61 4.4 UML Analysis of Expert Model 62 4.4.1 Expert Module Activity Diagram 63 4.4.2 Ontology Class Collaboration Diagram 65 4.5 Ontology Model of Expert Systems 66 4.6 Conclusion and Future Scope 67 References 68 5 An IoT-Based Gadget for Visually Impaired People 71Prakash, N., Udayakumar, E., Kumareshan, N., Srihari, K. and Sachi Nandan Mohanty 5.1 Introduction 71 5.2 Related Work 73 5.3 System Design 74 5.4 Results and Discussion 82 5.5 Conclusion 84 5.6 Future Work 84 References 84 6 IoT Protocol for Inferno Calamity in Public Transport 87Ravi Babu Devareddi, R. Shiva Shankar and Gadiraju Mahesh 6.1 Introduction 87 6.2 Literature Survey 89 6.3 Methodology 94 6.3.1 IoT Message Exchange With Cloud MQTT Broker Based on MQTT Protocol 98 6.3.2 Hardware Requirement 98 6.4 Implementation 103 6.4.1 Interfacing Diagram 105 6.5 Results 106 6.6 Conclusion and Future Work 108 References 109 7 Traffic Prediction Using Machine Learning and IoT 111Daksh Pratap Singh and Dolly Sharma 7.1 Introduction 111 7.1.1 Real Time Traffic 111 7.1.2 Traffic Simulation 112 7.2 Literature Review 112 7.3 Methodology 113 7.4 Architecture 116 7.4.1 API Architecture 117 7.4.2 File Structure 117 7.4.3 Simulator Architecture 118 7.4.4 Workflow in Application 122 7.4.5 Workflow of Google APIs in the Application 122 7.5 Results 122 7.5.1 Traffic Scenario 122 7.5.1.1 Low Traffic 124 7.5.1.2 Moderate Traffic 124 7.5.1.3 High Traffic 125 7.5.2 Speed Viewer 125 7.5.3 Traffic Simulator 126 7.5.3.1 1st View 126 7.5.3.2 2nd View 128 7.5.3.3 3rd View 128 7.6 Conclusion and Future Scope 128 References 129 8 Application of Machine Learning in Precision Agriculture 131Ravi Sharma and Nonita Sharma 8.1 Introduction 131 8.2 Machine Learning 132 8.2.1 Supervised Learning 133 8.2.2 Unsupervised Learning 133 8.2.3 Reinforcement Learning 134 8.3 Agriculture 134 8.4 ML Techniques Used in Agriculture 135 8.4.1 Soil Mapping 135 8.4.2 Seed Selection 140 8.4.3 Irrigation/Water Management 141 8.4.4 Crop Quality 143 8.4.5 Disease Detection 144 8.4.6 Weed Detection 145 8.4.7 Yield Prediction 147 8.5 Conclusion 148 References 149 9 An IoT-Based Multi Access Control and Surveillance for Home Security 153Yogeshwaran, K., Ramesh, C., Udayakumar, E., Srihari, K. and Sachi Nandan Mohanty 9.1 Introduction 153 9.2 Related Work 155 9.3 Hardware Description 156 9.3.1 Float Sensor 158 9.3.2 Map Matching 158 9.3.3 USART Cable 159 9.4 Software Design 161 9.5 Conclusion 162 References 162 10 Application of IoT in Industry 4.0 for Predictive Analytics 165Ahin Banerjee, Debanshee Datta and Sanjay K. Gupta 10.1 Introduction 165 10.2 Past Literary Works 168 10.2.1 Maintenance-Based Monitoring 168 10.2.2 Data Driven Approach to RUL Finding in Industry 169 10.2.3 Philosophy of Industrial-IoT Systems and its Advantages in Different Domain 173 10.3 Methodology and Results 176 10.4 Conclusion 179 References 180 11 IoT and Its Role in Performance Enhancement in Business Organizations 183Seema Sahai, Richa Goel, Parul Bajaj and Gurinder Singh 11.1 Introduction 183 11.1.1 Scientific Issues in IoT 184 11.1.2 IoT in Organizations 185 11.1.3 Technology and Business 187 11.1.4 Rewards of Technology in Business 187 11.1.5 Shortcomings of Technology in Business 188 11.1.6 Effect of IoT on Work and Organization 188 11.2 Technology and Productivity 190 11.3 Technology and Future of Human Work 193 11.4 Technology and Employment 194 11.5 Conclusion 195 References 195 12 An Analysis of Cloud Computing Based on Internet of Things 197Farhana Ajaz, Mohd Naseem, Ghulfam Ahamad, Sparsh Sharma and Ehtesham Abbasi 12.1 Introduction 197 12.1.1 Generic Architecture 199 12.2 Challenges in IoT 202 12.3 Technologies Used in IoT 203 12.4 Cloud Computing 203 12.4.1 Service Models of Cloud Computing 204 12.5 Cloud Computing Characteristics 205 12.6 Applications of Cloud Computing 206 12.7 Cloud IoT 207 12.8 Necessity for Fusing IoT and Cloud Computing 207 12.9 Cloud-Based IoT Architecture 208 12.10 Applications of Cloud-Based IoT 208 12.11 Conclusion 209 References 209 13 Importance of Fog Computing in Emerging Technologies-IoT 211Aarti Sahitya 13.1 Introduction 211 13.2 IoT Core 212 13.3 Need of Fog Computing 227 References 230 14 Convergence of Big Data and Cloud Computing Environment 233Ranjan Ganguli 14.1 Introduction 233 14.2 Big Data: Historical View 234 14.2.1 Big Data: Definition 235 14.2.2 Big Data Classification 236 14.2.3 Big Data Analytics 236 14.3 Big Data Challenges 237 14.4 The Architecture 238 14.4.1 Storage or Collection System 240 14.4.2 Data Care 240 14.4.3 Analysis 240 14.5 Cloud Computing: History in a Nutshell 241 14.5.1 View on Cloud Computing and Big Data 241 14.6 Insight of Big Data and Cloud Computing 241 14.6.1 Cloud-Based Services 242 14.6.2 At a Glance: Cloud Services 244 14.7 Cloud Framework 245 14.7.1 Hadoop 245 14.7.2 Cassandra 246 14.7.2.1 Features of Cassandra 246 14.7.3 Voldemort 247 14.7.3.1 A Comparison With Relational Databases and Benefits 247 14.8 Conclusions 248 14.9 Future Perspective 248 References 248 15 Data Analytics Framework Based on Cloud Environment 251K. Kanagaraj and S. Geetha 15.1 Introduction 251 15.2 Focus Areas of the Chapter 252 15.3 Cloud Computing 252 15.3.1 Cloud Service Models 253 15.3.1.1 Software as a Service (SaaS) 253 15.3.1.2 Platform as a Service (PaaS) 254 15.3.1.3 Infrastructure as a Service (IaaS) 255 15.3.1.4 Desktop as a Service (DaaS) 256 15.3.1.5 Analytics as a Service (AaaS) 257 15.3.1.6 Artificial Intelligence as a Service (AIaaS) 258 15.3.2 Cloud Deployment Models 259 15.3.3 Virtualization of Resources 260 15.3.4 Cloud Data Centers 261 15.4 Data Analytics 263 15.4.1 Data Analytics Types 263 15.4.1.1 Descriptive Analytics 263 15.4.1.2 Diagnostic Analytics 264 15.4.1.3 Predictive Analytics 265 15.4.1.4 Prescriptive Analytics 265 15.4.1.5 Big Data Analytics 265 15.4.1.6 Augmented Analytics 266 15.4.1.7 Cloud Analytics 266 15.4.1.8 Streaming Analytics 266 15.4.2 Data Analytics Tools 266 15.5 Real-Time Data Analytics Support in Cloud 266 15.6 Framework for Data Analytics in Cloud 268 15.6.1 Data Analysis Software as a Service (DASaaS) 268 15.6.2 Data Analysis Platform as a Service (DAPaaS) 268 15.6.3 Data Analysis Infrastructure as a Service (DAIaaS) 269 15.7 Data Analytics Work-Flow 269 15.8 Cloud-Based Data Analytics Tools 270 15.8.1 Amazon Kinesis Services 271 15.8.2 Amazon Kinesis Data Firehose 271 15.8.3 Amazon Kinesis Data Streams 271 15.8.4 Amazon Textract 271 15.8.5 Azure Stream Analytics 271 15.9 Experiment Results 272 15.10 Conclusion 272 References 274 16 Neural Networks for Big Data Analytics 277Bithika Bishesh 16.1 Introduction 277 16.2 Neural Networks—An Overview 278 16.3 Why Study Neural Networks? 279 16.4 Working of Artificial Neural Networks 279 16.4.1 Single-Layer Perceptron 279 16.4.2 Multi-Layer Perceptron 280 16.4.3 Training a Neural Network 281 16.4.4 Gradient Descent Algorithm 282 16.4.5 Activation Functions 284 16.5 Innovations in Neural Networks 288 16.5.1 Convolutional Neural Network (ConvNet) 288 16.5.2 Recurrent Neural Network 289 16.5.3 LSTM 291 16.6 Applications of Deep Learning Neural Networks 292 16.7 Practical Application of Neural Networks Using Computer Codes 293 16.8 Opportunities and Challenges of Using Neural Networks 293 16.9 Conclusion 296 References 296 17 Meta-Heuristic Algorithms for Best IoT Cloud Service Platform Selection 299Sudhansu Shekhar Patra, Sudarson Jena, G.B. Mund, Mahendra Kumar Gourisaria and Jugal Kishor Gupta 17.1 Introduction 299 17.2 Selection of a Cloud Provider in Federated Cloud 301 17.3 Algorithmic Solution 307 17.3.1 TLBO Algorithm (Teaching-Learning-Based Optimization Algorithm) 307 17.3.1.1 Teacher Phase: Generation of a New Solution 308 17.3.1.2 Learner Phase: Generation of New Solution 309 17.3.1.3 Representation of the Solution 309 17.3.2 JAYA Algorithm 309 17.3.2.1 Representation of the Solution 311 17.3.3 Bird Swarm Algorithm 311 17.3.3.1 Forging Behavior 313 17.3.3.2 Vigilance Behavior 313 17.3.3.3 Flight Behavior 313 17.3.3.4 Representation of the Solution 313 17.4 Analyzing the Algorithms 314 17.5 Conclusion 316 References 316 18 Legal Entanglements of Cloud Computing In India 319Sambhabi Patnaik and Lipsa Dash 18.1 Cloud Computing Technology 319 18.2 Cyber Security in Cloud Computing 322 18.3 Security Threats in Cloud Computing 323 18.3.1 Data Breaches 323 18.3.2 Denial of Service (DoS) 323 18.3.3 Botnets 323 18.3.4 Crypto Jacking 324 18.3.5 Insider Threats 324 18.3.6 Hijacking Accounts 324 18.3.7 Insecure Applications 324 18.3.8 Inadequate Training 325 18.3.9 General Vulnerabilities 325 18.4 Cloud Security Probable Solutions 325 18.4.1 Appropriate Cloud Model for Business 325 18.4.2 Dedicated Security Policies Plan 325 18.4.3 Multifactor Authentication 325 18.4.4 Data Accessibility 326 18.4.5 Secure Data Destruction 326 18.4.6 Encryption of Backups 326 18.4.7 Regulatory Compliance 326 18.4.8 External Third-Party Contracts and Agreements 327 18.5 Cloud Security Standards 327 18.6 Cyber Security Legal Framework in India 327 18.7 Privacy in Cloud Computing—Data Protection Standards 329 18.8 Recognition of Right to Privacy 330 18.9 Government Surveillance Power vs Privacy of Individuals 332 18.10 Data Ownership and Intellectual Property Rights 333 18.11 Cloud Service Provider as an Intermediary 335 18.12 Challenges in Cloud Computing 337 18.12.1 Classification of Data 337 18.12.2 Jurisdictional Issues 337 18.12.3 Interoperability of the Cloud 338 18.12.4 Vendor Agreements 339 18.13 Conclusion 339 References 341 19 Securing the Pharma Supply Chain Using Blockchain 343Pulkit Arora, Chetna Sachdeva and Dolly Sharma 19.1 Introduction 343 19.2 Literature Review 345 19.2.1 Current Scenario 346 19.2.2 Proposal 347 19.3 Methodology 349 19.4 Results 354 19.5 Conclusion and Future Scope 358 References 358 Index 361

The book presents a detailed overview of the state-of-the-art in cloud-IoT and its foundations, analytics and applications, as well as addresses various security and privacy challenges facing the use of this technology.The assimilation of cloud computing with IoT, also known as cloud-IoT, has proven potential for enhancement of the quality of life and the efficient utilization of resources for smart homes, cities, education, healthcare, banking, industry and grids, among other applications. Moreover, cloud-IoT technology also benefits from the competence and abundance of big data and its analytical facilities. The aim of this book, therefore, is to present research that integrates aspects of IoT, cloud computing and data analytics from diversified perspectives to provide insight into the use of these technologies in diversified fields of science and areas of application.AudienceThe core audience for this book includes engineering researchers and graduate students in computer science, artificial intelligence, electronic engineering, as well as IT professionals, IT manufacturing industries involved in the associated fields, network administrators, cybersecurity experts and government research agencies.