Role of Edge Analytics in Sustainable Smart City Development

G. R. Kanagachidambaresan received his PhD from Anna University Chennai in 2017. He is currently an associate professor in the Department of Computer Science Engineering, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India. His main research interests include Industry 4.0, smart city projects, Body Sensor Network and Fault Tolerant Wireless Sensor Network. He has published several articles in SCI journals and is an associate editor of Wireless Networks.

Preface xv

1 Smart Health Care Development: Challenges and Solutions 1
R. Sujatha, E.P. Ephzibah and S. Sree Dharinya

1.1 Introduction 2

1.2 ICT Explosion 3

1.2.1 RFID 4

1.2.2 IoT and Big Data 5

1.2.3 Wearable Sensors—Head to Toe 7

1.2.4 Cloud Computing 8

1.3 Intelligent Healthcare 10

1.4 Home Healthcare 11

1.5 Data Analytics 11

1.6 Technologies—Data Cognitive 13

1.6.1 Machine Learning 13

1.6.2 Image Processing 14

1.6.3 Deep Learning 14

1.7 Adoption Technologies 15

1.8 Conclusion 15

References 15

2 Working of Mobile Intelligent Agents on the Web—A Survey 21
P.R. Joe Dhanith and B. Surendiran

2.1 Introduction 21

2.2 Mobile Crawler 23

2.3 Comparative Study of the Mobile Crawlers 47

2.4 Conclusion 47

References 47

3 Power Management Scheme for Photovoltaic/Battery Hybrid System in Smart Grid 49
T. Bharani Prakash and S. Nagakumararaj

3.1 Power Management Scheme 50

3.2 Internal Power Flow Management 50

3.2.1 PI Controller 51

3.2.2 State of Charge 53

3.3 Voltage Source Control 54

3.3.1 Phase-Locked Loop 55

3.3.2 Space Vector Pulse Width Modulation 56

3.3.3 Park Transformation (abc to dq0) 57

3.4 Simulation Diagram and Results 58

3.4.1 Simulation Diagram 58

3.4.2 Simulation Results 63

Conclusion 65

4 Analysis: A Neural Network Equalizer for Channel Equalization by Particle Swarm Optimization for Various Channel Models 67
M. Muthumari, D.C. Diana and C. Ambika Bhuvaneswari

4.1 Introduction 68

4.2 Channel Equalization 72

4.2.1 Channel Models 73

4.2.1.1 Tapped Delay Line Model 74

4.2.1.2 Stanford University Interim (SUI) Channel Models 75

4.2.2 Artificial Neural Network 75

4.3 Functional Link Artificial Neural Network 76

4.4 Particle Swarm Optimization 76

4.5 Result and Discussion 77

4.5.1 Convergence Analysis 77

4.5.2 Comparison Between Different Parameters 79

4.5.3 Comparison Between Different Channel Models 80

4.6 Conclusion 81

References 82

5 Implementing Hadoop Container Migrations in OpenNebula Private Cloud Environment 85
P. Kalyanaraman, K.R. Jothi, P. Balakrishnan, R.G. Navya, A. Shah and V. Pandey

5.1 Introduction 86

5.1.1 Hadoop Architecture 86

5.1.2 Hadoop and Big Data 88

5.1.3 Hadoop and Virtualization 88

5.1.4 What is OpenNebula? 89

5.2 Literature Survey 90

5.2.1 Performance Analysis of Hadoop 90

5.2.2 Evaluating Map Reduce on Virtual Machines 91

5.2.3 Virtualizing Hadoop Containers 94

5.2.4 Optimization of Hadoop Cluster Using Cloud Platform 95

5.2.5 Heterogeneous Clusters in Cloud Computing 96

5.2.6 Performance Analysis and Optimization in Hadoop 97

5.2.7 Virtual Technologies 97

5.2.8 Scheduling 98

5.2.9 Scheduling of Hadoop VMs 98

5.3 Discussion 99

5.4 Conclusion 100

References 101

6 Transmission Line Inspection Using Unmanned Aerial Vehicle 105
A. Mahaboob Subahani, M. Kathiresh and S. Sanjeev

6.1 Introduction 106

6.1.1 Unmanned Aerial Vehicle 106

6.1.2 Quadcopter 106

6.2 Literature Survey 107

6.3 System Architecture 108

6.4 ArduPilot 109

6.5 Arduino Mega 111

6.6 Brushless DC Motor 111

6.7 Battery 112

6.8 CMOS Camera 113

6.9 Electronic Speed Control 113

6.10 Power Module 115

6.11 Display Shield 116

6.12 Navigational LEDS 116

6.13 Role of Sensors in the Proposed System 118

6.13.1 Accelerometer and Gyroscope 118

6.13.2 Magnetometer 118

6.13.3 Barometric Pressure Sensor 119

6.13.4 Global Positioning System 119

6.14 Wireless Communication 120

6.15 Radio Controller 120

6.16 Telemetry Radio 121

6.17 Camera Transmitter 121

6.18 Results and Discussion 121

6.19 Conclusion 124

References 125

7 Smart City Infrastructure Management System Using IoT 127
S. Ramamoorthy, M. Kowsigan, P. Balasubramanie and P. John Paul

7.1 Introduction 128

7.2 Major Challenges in IoT-Based Technology 129

7.2.1 Peer to Peer Communication Security 129

7.2.2 Objective of Smart Infrastructure 130

7.3 Internet of Things (IoT) 131

7.3.1 Key Components of Components of IoT 131

7.3.1.1 Network Gateway 132

7.3.1.2 HTTP (HyperText Transfer Protocol) 132

7.3.1.3 LoRaWan (Long Range Wide Area Network) 133

7.3.1.4 Bluetooth 133

7.3.1.5 ZigBee 133

7.3.2 IoT Data Protocols 133

7.3.2.1 Message Queue Telemetry Transport (MQTT) 133

7.3.2.2 Constrained Application Protocol (CoAP) 134

7.3.2.3 Advanced Message Queuing Protocol (AMQP) 134

7.3.2.4 Data Analytics 134

7.4 Machine Learning-Based Smart Decision-Making Process 135

7.5 Cloud Computing 136

References 138

8 Lightweight Cryptography Algorithms for IoT Resource-Starving Devices 139
S. Aruna, G. Usha, P. Madhavan and M.V. Ranjith Kumar

8.1 Introduction 139

8.1.1 Need of the Cryptography 140

8.2 Challenges on Lightweight Cryptography 141

8.3 Hashing Techniques on Lightweight Cryptography 142

8.4 Applications on Lighweight Cryptography 152

8.5 Conclusion 167

References 168

9 Pre-Learning-Based Semantic Segmentation for LiDAR Point Cloud Data Using Self-Organized Map 171
K. Rajathi and P. Sarasu

9.1 Introduction 172

9.2 Related Work 173

9.2.1 Semantic Segmentation for Images 173

9.3 Semantic Segmentation for LiDAR Point Cloud 173

9.4 Proposed Work 175

9.4.1 Data Acquisition 175

9.4.2 Our Approach 175

9.4.3 Pre-Learning Processing 179

9.5 Region of Interest (RoI) 180

9.6 Registration of Point Cloud 181

9.7 Semantic Segmentation 181

9.8 Self-Organized Map (SOM) 182

9.9 Experimental Result 183

9.10 Conclusion 186

References 187

10 Smart Load Balancing Algorithms in Cloud Computing—A Review 189
K.R. Jothi, S. Anto, M. Kohar, M. Chadha and P. Madhavan

10.1 Introduction 189

10.2 Research Challenges 192

10.2.1 Security & Routing 192

10.2.2 Storage/Replication 192

10.2.3 Spatial Spread of the Cloud Nodes 192

10.2.4 Fault Tolerance 193

10.2.5 Algorithm Complexity 193

10.3 Literature Survey 193

10.4 Survey Table 201

10.5 Discussion & Comparison 202

10.6 Conclusion 202

References 216

11 A Low-Cost Wearable Remote Healthcare Monitoring System 219
Konguvel Elango and Kannan Muniandi

11.1 Introduction 219

11.1.1 Problem Statement 220

11.1.2 Objective of the Study 221

11.2 Related Works 222

11.2.1 Remote Healthcare Monitoring Systems 222

11.2.2 Pulse Rate Detection 224

11.2.3 Temperate Measurement 225

11.2.4 Fall Detection 225

11.3 Methodology 226

11.3.1 NodeMCU 226

11.3.2 Pulse Rate Detection System 227

11.3.3 Fall Detection System 230

11.3.4 Temperature Detection System 231

11.3.5 LCD Specification 234

11.3.6 ADC Specification 234

11.4 Results and Discussions 236

11.4.1 System Implementation 236

11.4.2 Fall Detection Results 236

11.4.3 ThingSpeak 236

11.5 Conclusion 239

11.6 Future Scope 240

References 241

12 IoT-Based Secure Smart Infrastructure Data Management 243
R. Poorvadevi, M. Kowsigan, P. Balasubramanie and J. Rajeshkumar

12.1 Introduction 244

12.1.1 List of Security Threats Related to the Smart IoT Network 244

12.1.2 Major Application Areas of IoT 244

12.1.3 IoT Threats and Security Issues 245

12.1.4 Unpatched Vulnerabilities 245

12.1.5 Weak Authentication 245

12.1.6 Vulnerable API’s 245

12.2 Types of Threats to Users 245

12.3 Internet of Things Security Management 246

12.3.1 Managing IoT Devices 246

12.3.2 Role of External Devices in IoT Platform 247

12.3.3 Threats to Other Computer Networks 248

12.4 Significance of IoT Security 249

12.4.1 Aspects of Workplace Security 249

12.4.2 Important IoT Security Breaches and IoT Attacks 250

12.5 IoT Security Tools and Legislation 250

12.6 Protection of IoT Systems and Devices 251

12.6.1 IoT Issues and Security Challenges 251

12.6.2 Providing Secured Connections 252

12.7 Five Ways to Secure IoT Devices 253

12.8 Conclusion 255

References 255

13 A Study of Addiction Behavior for Smart Psychological Health Care System 257
V. Sabapathi and K.P. Vijayakumar

13.1 Introduction 258

13.2 Basic Criteria of Addiction 258

13.3 Influencing Factors of Addiction Behavior 259

13.3.1 Peers Influence 259

13.3.2 Environment Influence 260

13.3.3 Media Influence 262

13.3.4 Family Group and Society 262

13.4 Types of Addiction and Their Effects 262

13.4.1 Gaming Addiction 263

13.4.2 Pornography Addiction 264

13.4.3 Smart Phone Addiction 265

13.4.4 Gambling Addiction 267

13.4.5 Food Addiction 267

13.4.6 Sexual Addiction 268

13.4.7 Cigarette and Alcohol Addiction 268

13.4.8 Status Expressive Addiction 269

13.4.9 Workaholic Addiction 269

13.5 Conclusion 269

References 270

14 A Custom Cluster Design With Raspberry Pi for Parallel Programming and Deployment of Private Cloud 273
Sukesh, B., Venkatesh, K. and Srinivas, L.N.B.

14.1 Introduction 274

14.2 Cluster Design with Raspberry Pi 276

14.2.1 Assembling Materials for Implementing Cluster 276

14.2.1.1 Raspberry Pi4 277

14.2.1.2 RPi 4 Model B Specifications 277

14.2.2 Setting Up Cluster 278

14.2.2.1 Installing Raspbian and Configuring Master Node 279

14.2.2.2 Installing MPICH and MPI4PY 279

14.2.2.3 Cloning the Slave Nodes 279

14.3 Parallel Computing and MPI on Raspberry Pi Cluster 279

14.4 Deployment of Private Cloud on Raspberry Pi Cluster 281

14.4.1 NextCloud Software 281

14.5 Implementation 281

14.5.1 NextCloud on RPi Cluster 281

14.5.2 Parallel Computing on RPi Cluster 282

14.6 Results and Discussions 286

14.7 Conclusion 287

References 287

15 Energy Efficient Load Balancing Technique for Distributed Data Transmission Using Edge Computing 289
Karthikeyan, K. and Madhavan, P.

15.1 Introduction 290

15.2 Energy Efficiency Offloading Data Transmission 290

15.2.1 Web-Based Offloading 291

15.3 Energy Harvesting 291

15.3.1 LODCO Algorithm 292

15.4 User-Level Online Offloading Framework (ULOOF) 293

15.5 Frequency Scaling 294

15.6 Computation Offloading and Resource Allocation 295

15.7 Communication Technology 296

15.8 Ultra-Dense Network 297

15.9 Conclusion 299

References 299

16 Blockchain-Based SDR Signature Scheme With Time-Stamp 303
Swathi Singh, Divya Satish and Sree Rathna Lakshmi

16.1 Introduction 303

16.2 Literature Study 304

16.2.1 Signatures With Hashes 304

16.2.2 Signature Scheme With Server Support 305

16.2.3 Signatures Scheme Based on Interaction 305

16.3 Methodology 306

16.3.1 Preliminaries 306

16.3.1.1 Hash Trees 306

16.3.1.2 Chains of Hashes 306

16.3.2 Interactive Hash-Based Signature Scheme 307

16.3.3 Significant Properties of Hash-Based Signature Scheme 309

16.3.4 Proposed SDR Scheme Structure 310

16.3.4.1 One-Time Keys 310

16.3.4.2 Server Behavior Authentication 310

16.3.4.3 Pre-Authentication by Repository 311

16.4 SDR Signature Scheme 311

16.4.1 Pre-Requisites 311

16.4.2 Key Generation Algorithm 312

16.4.2.1 Server 313

16.4.3 Sign Algorithm 313

16.4.3.1 Signer 313

16.4.3.2 Server 313

16.4.3.3 Repository 314

16.4.4 Verification Algorithm 314

16.5 Supportive Theory 315

16.5.1 Signing Algorithm Supported by Server 315

16.5.2 Repository Deployment 316

16.5.3 SDR Signature Scheme Setup 316

16.5.4 Results and Observation 316

16.6 Conclusion 317

References 317

Index 321