Role of Edge Analytics in Sustainable Smart City Development
Challenges and Solutions
1. Edition September 2020
352 Pages, Hardcover
Wiley & Sons Ltd
ISBN:
978-1-119-68128-1
John Wiley & Sons
Further versions
Efficient Single Board Computers (SBCs) and advanced VLSI systems have resulted in edge analytics and faster decision making. The QoS parameters like energy, delay, reliability, security, and throughput should be improved on seeking better intelligent expert systems. The resource constraints in the Edge devices, challenges the researchers to meet the required QoS. Since these devices and components work in a remote unattended environment, an optimum methodology to improve its lifetime has become mandatory. Continuous monitoring of events is mandatory to avoid tragic situations; it can only be enabled by providing high QoS. The applications of IoT in digital twin development, health care, traffic analysis, home surveillance, intelligent agriculture monitoring, defense and all common day to day activities have resulted in pioneering embedded devices, which can offer high computational facility without much latency and delay. The book address industrial problems in designing expert system and IoT applications. It provides novel survey and case study report on recent industrial approach towards Smart City development.
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
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
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.
