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.

The book aims to integrate the aspects of IoT, Cloud computing and data analytics from diversified perspectives. The book also plans to discuss the recent research trends and advanced topics in the field which will be of interest to academicians and researchers working in this area. Thus, the book intends to help its readers to understand and explore the spectrum of applications of IoT, cloud computing and data analytics.
Here, it is also worth mentioning that the book is believed to draw attention on the applications of said technology in various disciplines in order to obtain enhanced understanding of the readers. Also, this book focuses on the researches and challenges in the domain of IoT, Cloud computing and Data analytics from perspectives of various stakeholders.

Preface xv
Acknowledgement xvii
1 Internet of Things: A Key to Unfasten Mundane Repetitive Tasks 1
Hemanta 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 25
Richa 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 41
V. 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 57
Subhasish 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 71
Prakash, 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 87
Ravi 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 111
Daksh 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 131
Ravi 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 153
Yogeshwaran, 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 165
Ahin 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 183
Seema 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 197
Farhana 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 211
Aarti 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 233
Ranjan 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 251
K. 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 277
Bithika 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 299
Sudhansu 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 319
Sambhabi 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 343
Pulkit 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