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<p>Preface xv</p> <p><b>1 Smart Health Care Development: Challenges and Solutions 1<br /></b><i>R. Sujatha, E.P. Ephzibah and S. Sree Dharinya</i></p> <p>1.1 Introduction 2</p> <p>1.2 ICT Explosion 3</p> <p>1.2.1 RFID 4</p> <p>1.2.2 IoT and Big Data 5</p> <p>1.2.3 Wearable Sensors—Head to Toe 7</p> <p>1.2.4 Cloud Computing 8</p> <p>1.3 Intelligent Healthcare 10</p> <p>1.4 Home Healthcare 11</p> <p>1.5 Data Analytics 11</p> <p>1.6 Technologies—Data Cognitive 13</p> <p>1.6.1 Machine Learning 13</p> <p>1.6.2 Image Processing 14</p> <p>1.6.3 Deep Learning 14</p> <p>1.7 Adoption Technologies 15</p> <p>1.8 Conclusion 15</p> <p>References 15</p> <p><b>2 Working of Mobile Intelligent Agents on the Web—A Survey 21<br /></b><i>P.R. Joe Dhanith and B. Surendiran</i></p> <p>2.1 Introduction 21</p> <p>2.2 Mobile Crawler 23</p> <p>2.3 Comparative Study of the Mobile Crawlers 47</p> <p>2.4 Conclusion 47</p> <p>References 47</p> <p><b>3 Power Management Scheme for Photovoltaic/Battery Hybrid System in Smart Grid 49<br /></b><i>T. Bharani Prakash and S. Nagakumararaj</i></p> <p>3.1 Power Management Scheme 50</p> <p>3.2 Internal Power Flow Management 50</p> <p>3.2.1 PI Controller 51</p> <p>3.2.2 State of Charge 53</p> <p>3.3 Voltage Source Control 54</p> <p>3.3.1 Phase-Locked Loop 55</p> <p>3.3.2 Space Vector Pulse Width Modulation 56</p> <p>3.3.3 Park Transformation (abc to dq0) 57</p> <p>3.4 Simulation Diagram and Results 58</p> <p>3.4.1 Simulation Diagram 58</p> <p>3.4.2 Simulation Results 63</p> <p>Conclusion 65</p> <p><b>4 Analysis: A Neural Network Equalizer for Channel Equalization by Particle Swarm Optimization for Various Channel Models 67<br /></b><i>M. Muthumari, D.C. Diana and C. Ambika Bhuvaneswari</i></p> <p>4.1 Introduction 68</p> <p>4.2 Channel Equalization 72</p> <p>4.2.1 Channel Models 73</p> <p>4.2.1.1 Tapped Delay Line Model 74</p> <p>4.2.1.2 Stanford University Interim (SUI) Channel Models 75</p> <p>4.2.2 Artificial Neural Network 75</p> <p>4.3 Functional Link Artificial Neural Network 76</p> <p>4.4 Particle Swarm Optimization 76</p> <p>4.5 Result and Discussion 77</p> <p>4.5.1 Convergence Analysis 77</p> <p>4.5.2 Comparison Between Different Parameters 79</p> <p>4.5.3 Comparison Between Different Channel Models 80</p> <p>4.6 Conclusion 81</p> <p>References 82</p> <p><b>5 Implementing Hadoop Container Migrations in OpenNebula Private Cloud Environment 85<br /></b><i>P. Kalyanaraman, K.R. Jothi, P. Balakrishnan, R.G. Navya, A. Shah and V. Pandey</i></p> <p>5.1 Introduction 86</p> <p>5.1.1 Hadoop Architecture 86</p> <p>5.1.2 Hadoop and Big Data 88</p> <p>5.1.3 Hadoop and Virtualization 88</p> <p>5.1.4 What is OpenNebula? 89</p> <p>5.2 Literature Survey 90</p> <p>5.2.1 Performance Analysis of Hadoop 90</p> <p>5.2.2 Evaluating Map Reduce on Virtual Machines 91</p> <p>5.2.3 Virtualizing Hadoop Containers 94</p> <p>5.2.4 Optimization of Hadoop Cluster Using Cloud Platform 95</p> <p>5.2.5 Heterogeneous Clusters in Cloud Computing 96</p> <p>5.2.6 Performance Analysis and Optimization in Hadoop 97</p> <p>5.2.7 Virtual Technologies 97</p> <p>5.2.8 Scheduling 98</p> <p>5.2.9 Scheduling of Hadoop VMs 98</p> <p>5.3 Discussion 99</p> <p>5.4 Conclusion 100</p> <p>References 101</p> <p><b>6 Transmission Line Inspection Using Unmanned Aerial Vehicle 105<br /></b><i>A. Mahaboob Subahani, M. Kathiresh and S. Sanjeev</i></p> <p>6.1 Introduction 106</p> <p>6.1.1 Unmanned Aerial Vehicle 106</p> <p>6.1.2 Quadcopter 106</p> <p>6.2 Literature Survey 107</p> <p>6.3 System Architecture 108</p> <p>6.4 ArduPilot 109</p> <p>6.5 Arduino Mega 111</p> <p>6.6 Brushless DC Motor 111</p> <p>6.7 Battery 112</p> <p>6.8 CMOS Camera 113</p> <p>6.9 Electronic Speed Control 113</p> <p>6.10 Power Module 115</p> <p>6.11 Display Shield 116</p> <p>6.12 Navigational LEDS 116</p> <p>6.13 Role of Sensors in the Proposed System 118</p> <p>6.13.1 Accelerometer and Gyroscope 118</p> <p>6.13.2 Magnetometer 118</p> <p>6.13.3 Barometric Pressure Sensor 119</p> <p>6.13.4 Global Positioning System 119</p> <p>6.14 Wireless Communication 120</p> <p>6.15 Radio Controller 120</p> <p>6.16 Telemetry Radio 121</p> <p>6.17 Camera Transmitter 121</p> <p>6.18 Results and Discussion 121</p> <p>6.19 Conclusion 124</p> <p>References 125</p> <p><b>7 Smart City Infrastructure Management System Using IoT 127<br /></b><i>S. Ramamoorthy, M. Kowsigan, P. Balasubramanie and P. John Paul</i></p> <p>7.1 Introduction 128</p> <p>7.2 Major Challenges in IoT-Based Technology 129</p> <p>7.2.1 Peer to Peer Communication Security 129</p> <p>7.2.2 Objective of Smart Infrastructure 130</p> <p>7.3 Internet of Things (IoT) 131</p> <p>7.3.1 Key Components of Components of IoT 131</p> <p>7.3.1.1 Network Gateway 132</p> <p>7.3.1.2 HTTP (HyperText Transfer Protocol) 132</p> <p>7.3.1.3 LoRaWan (Long Range Wide Area Network) 133</p> <p>7.3.1.4 Bluetooth 133</p> <p>7.3.1.5 ZigBee 133</p> <p>7.3.2 IoT Data Protocols 133</p> <p>7.3.2.1 Message Queue Telemetry Transport (MQTT) 133</p> <p>7.3.2.2 Constrained Application Protocol (CoAP) 134</p> <p>7.3.2.3 Advanced Message Queuing Protocol (AMQP) 134</p> <p>7.3.2.4 Data Analytics 134</p> <p>7.4 Machine Learning-Based Smart Decision-Making Process 135</p> <p>7.5 Cloud Computing 136</p> <p>References 138</p> <p><b>8 Lightweight Cryptography Algorithms for IoT Resource-Starving Devices 139<br /></b><i>S. Aruna, G. Usha, P. Madhavan and M.V. Ranjith Kumar</i></p> <p>8.1 Introduction 139</p> <p>8.1.1 Need of the Cryptography 140</p> <p>8.2 Challenges on Lightweight Cryptography 141</p> <p>8.3 Hashing Techniques on Lightweight Cryptography 142</p> <p>8.4 Applications on Lighweight Cryptography 152</p> <p>8.5 Conclusion 167</p> <p>References 168</p> <p><b>9 Pre-Learning-Based Semantic Segmentation for LiDAR Point Cloud Data Using Self-Organized Map 171<br /></b><i>K. Rajathi and P. Sarasu</i></p> <p>9.1 Introduction 172</p> <p>9.2 Related Work 173</p> <p>9.2.1 Semantic Segmentation for Images 173</p> <p>9.3 Semantic Segmentation for LiDAR Point Cloud 173</p> <p>9.4 Proposed Work 175</p> <p>9.4.1 Data Acquisition 175</p> <p>9.4.2 Our Approach 175</p> <p>9.4.3 Pre-Learning Processing 179</p> <p>9.5 Region of Interest (RoI) 180</p> <p>9.6 Registration of Point Cloud 181</p> <p>9.7 Semantic Segmentation 181</p> <p>9.8 Self-Organized Map (SOM) 182</p> <p>9.9 Experimental Result 183</p> <p>9.10 Conclusion 186</p> <p>References 187</p> <p><b>10 Smart Load Balancing Algorithms in Cloud Computing—A Review 189<br /></b><i>K.R. Jothi, S. Anto, M. Kohar, M. Chadha and P. Madhavan</i></p> <p>10.1 Introduction 189</p> <p>10.2 Research Challenges 192</p> <p>10.2.1 Security & Routing 192</p> <p>10.2.2 Storage/Replication 192</p> <p>10.2.3 Spatial Spread of the Cloud Nodes 192</p> <p>10.2.4 Fault Tolerance 193</p> <p>10.2.5 Algorithm Complexity 193</p> <p>10.3 Literature Survey 193</p> <p>10.4 Survey Table 201</p> <p>10.5 Discussion & Comparison 202</p> <p>10.6 Conclusion 202</p> <p>References 216</p> <p><b>11 A Low-Cost Wearable Remote Healthcare Monitoring System 219<br /></b><i>Konguvel Elango and Kannan Muniandi</i></p> <p>11.1 Introduction 219</p> <p>11.1.1 Problem Statement 220</p> <p>11.1.2 Objective of the Study 221</p> <p>11.2 Related Works 222</p> <p>11.2.1 Remote Healthcare Monitoring Systems 222</p> <p>11.2.2 Pulse Rate Detection 224</p> <p>11.2.3 Temperate Measurement 225</p> <p>11.2.4 Fall Detection 225</p> <p>11.3 Methodology 226</p> <p>11.3.1 NodeMCU 226</p> <p>11.3.2 Pulse Rate Detection System 227</p> <p>11.3.3 Fall Detection System 230</p> <p>11.3.4 Temperature Detection System 231</p> <p>11.3.5 LCD Specification 234</p> <p>11.3.6 ADC Specification 234</p> <p>11.4 Results and Discussions 236</p> <p>11.4.1 System Implementation 236</p> <p>11.4.2 Fall Detection Results 236</p> <p>11.4.3 ThingSpeak 236</p> <p>11.5 Conclusion 239</p> <p>11.6 Future Scope 240</p> <p>References 241</p> <p><b>12 IoT-Based Secure Smart Infrastructure Data Management 243<br /></b><i>R. Poorvadevi, M. Kowsigan, P. Balasubramanie and J. Rajeshkumar</i></p> <p>12.1 Introduction 244</p> <p>12.1.1 List of Security Threats Related to the Smart IoT Network 244</p> <p>12.1.2 Major Application Areas of IoT 244</p> <p>12.1.3 IoT Threats and Security Issues 245</p> <p>12.1.4 Unpatched Vulnerabilities 245</p> <p>12.1.5 Weak Authentication 245</p> <p>12.1.6 Vulnerable API’s 245</p> <p>12.2 Types of Threats to Users 245</p> <p>12.3 Internet of Things Security Management 246</p> <p>12.3.1 Managing IoT Devices 246</p> <p>12.3.2 Role of External Devices in IoT Platform 247</p> <p>12.3.3 Threats to Other Computer Networks 248</p> <p>12.4 Significance of IoT Security 249</p> <p>12.4.1 Aspects of Workplace Security 249</p> <p>12.4.2 Important IoT Security Breaches and IoT Attacks 250</p> <p>12.5 IoT Security Tools and Legislation 250</p> <p>12.6 Protection of IoT Systems and Devices 251</p> <p>12.6.1 IoT Issues and Security Challenges 251</p> <p>12.6.2 Providing Secured Connections 252</p> <p>12.7 Five Ways to Secure IoT Devices 253</p> <p>12.8 Conclusion 255</p> <p>References 255</p> <p><b>13 A Study of Addiction Behavior for Smart Psychological Health Care System 257<br /></b><i>V. Sabapathi and K.P. Vijayakumar</i></p> <p>13.1 Introduction 258</p> <p>13.2 Basic Criteria of Addiction 258</p> <p>13.3 Influencing Factors of Addiction Behavior 259</p> <p>13.3.1 Peers Influence 259</p> <p>13.3.2 Environment Influence 260</p> <p>13.3.3 Media Influence 262</p> <p>13.3.4 Family Group and Society 262</p> <p>13.4 Types of Addiction and Their Effects 262</p> <p>13.4.1 Gaming Addiction 263</p> <p>13.4.2 Pornography Addiction 264</p> <p>13.4.3 Smart Phone Addiction 265</p> <p>13.4.4 Gambling Addiction 267</p> <p>13.4.5 Food Addiction 267</p> <p>13.4.6 Sexual Addiction 268</p> <p>13.4.7 Cigarette and Alcohol Addiction 268</p> <p>13.4.8 Status Expressive Addiction 269</p> <p>13.4.9 Workaholic Addiction 269</p> <p>13.5 Conclusion 269</p> <p>References 270</p> <p><b>14 A Custom Cluster Design With Raspberry Pi for Parallel Programming and Deployment of Private Cloud 273<br /></b><i>Sukesh, B., Venkatesh, K. and Srinivas, L.N.B.</i></p> <p>14.1 Introduction 274</p> <p>14.2 Cluster Design with Raspberry Pi 276</p> <p>14.2.1 Assembling Materials for Implementing Cluster 276</p> <p>14.2.1.1 Raspberry Pi4 277</p> <p>14.2.1.2 RPi 4 Model B Specifications 277</p> <p>14.2.2 Setting Up Cluster 278</p> <p>14.2.2.1 Installing Raspbian and Configuring Master Node 279</p> <p>14.2.2.2 Installing MPICH and MPI4PY 279</p> <p>14.2.2.3 Cloning the Slave Nodes 279</p> <p>14.3 Parallel Computing and MPI on Raspberry Pi Cluster 279</p> <p>14.4 Deployment of Private Cloud on Raspberry Pi Cluster 281</p> <p>14.4.1 NextCloud Software 281</p> <p>14.5 Implementation 281</p> <p>14.5.1 NextCloud on RPi Cluster 281</p> <p>14.5.2 Parallel Computing on RPi Cluster 282</p> <p>14.6 Results and Discussions 286</p> <p>14.7 Conclusion 287</p> <p>References 287</p> <p><b>15 Energy Efficient Load Balancing Technique for Distributed Data Transmission Using Edge Computing 289<br /></b><i>Karthikeyan, K. and Madhavan, P.</i></p> <p>15.1 Introduction 290</p> <p>15.2 Energy Efficiency Offloading Data Transmission 290</p> <p>15.2.1 Web-Based Offloading 291</p> <p>15.3 Energy Harvesting 291</p> <p>15.3.1 LODCO Algorithm 292</p> <p>15.4 User-Level Online Offloading Framework (ULOOF) 293</p> <p>15.5 Frequency Scaling 294</p> <p>15.6 Computation Offloading and Resource Allocation 295</p> <p>15.7 Communication Technology 296</p> <p>15.8 Ultra-Dense Network 297</p> <p>15.9 Conclusion 299</p> <p>References 299</p> <p><b>16 Blockchain-Based SDR Signature Scheme With Time-Stamp 303<br /></b><i>Swathi Singh, Divya Satish and Sree Rathna Lakshmi</i></p> <p>16.1 Introduction 303</p> <p>16.2 Literature Study 304</p> <p>16.2.1 Signatures With Hashes 304</p> <p>16.2.2 Signature Scheme With Server Support 305</p> <p>16.2.3 Signatures Scheme Based on Interaction 305</p> <p>16.3 Methodology 306</p> <p>16.3.1 Preliminaries 306</p> <p>16.3.1.1 Hash Trees 306</p> <p>16.3.1.2 Chains of Hashes 306</p> <p>16.3.2 Interactive Hash-Based Signature Scheme 307</p> <p>16.3.3 Significant Properties of Hash-Based Signature Scheme 309</p> <p>16.3.4 Proposed SDR Scheme Structure 310</p> <p>16.3.4.1 One-Time Keys 310</p> <p>16.3.4.2 Server Behavior Authentication 310</p> <p>16.3.4.3 Pre-Authentication by Repository 311</p> <p>16.4 SDR Signature Scheme 311</p> <p>16.4.1 Pre-Requisites 311</p> <p>16.4.2 Key Generation Algorithm 312</p> <p>16.4.2.1 Server 313</p> <p>16.4.3 Sign Algorithm 313</p> <p>16.4.3.1 Signer 313</p> <p>16.4.3.2 Server 313</p> <p>16.4.3.3 Repository 314</p> <p>16.4.4 Verification Algorithm 314</p> <p>16.5 Supportive Theory 315</p> <p>16.5.1 Signing Algorithm Supported by Server 315</p> <p>16.5.2 Repository Deployment 316</p> <p>16.5.3 SDR Signature Scheme Setup 316</p> <p>16.5.4 Results and Observation 316</p> <p>16.6 Conclusion 317</p> <p>References 317</p> <p>Index 321</p>

<p><b>G. R. Kanagachidambaresan</b> 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 <i>Wireless Networks</i>.

<p><b>This book addresses the recent real time challenges faced in sustainable smart city development and provides novel solutions to such problems.</b> <p>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. <p><i>Role of Edge Analytics in Sustainable Smart City Development</i> 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. <p><b>Audience</b> <p>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.

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