Preface xv 1 Smart Health Care Development: Challenges and Solutions 1R. 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 21P.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 49T. 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 67M. 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 85P. 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 105A. 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 127S. 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 139S. 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 171K. 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 189K.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 219Konguvel 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 243R. 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 257V. 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 273Sukesh, 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 289Karthikeyan, 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 303Swathi 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

This book addresses the recent real time challenges faced in sustainable smart city development and provides novel solutions to such problems. Smart cities are tasked to adopt new technologies for resources and infrastructure such as water management, grid system, transport system and health care industries. The challenge is to provide the experience and facilities in an economic way and to reach all levels of society in the cities. The rapid growth of technology and new smart city development initiatives have made Internet of Things and Edge analytics an inevitable platform for all engineering domains. The need of sophisticated and ambient environment has resulted in an exponential growth of automation, robustness and artificial intelligence. The involvement of multi-domain technology creates new problems, making the development a challenging undertaking for researchers. Role of Edge Analytics in Sustainable Smart City Development comprising 16 chapters written by subject matter experts, deeply discusses the challenges such as the applications of IoT in digital twin development, health care, traffic analysis, home surveillance, intelligent agriculture monitoring, problems in smart grid, data management in cloud system, security concerns and algorithms for edge devices' defense. All these common day-to-day activities have resulted in pioneering embedded devices offering high computational facilities without much latency and delay. Along with addressing these issues, the book provides many solutions. It also provides a novel literature survey to show a path for researchers new to Edge analytics and expert systems. Audience The book will be used by electrical and computer science engineers who are working on Industry 4.0 standards, Internet of Things, mechatronics, nanotechnology, sensors, transportation, artificial intelligence, Edge analytics and expert systems. The book can also be used in post-graduate courses.