Integration of Cloud Computing with Internet of Things

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.

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 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