Development of 6G Networks and Technology explores the benefits and challenges of 5G and beyond that play a key role in the development of the next generation of internet. 6G is targeted to improve download speeds, eliminate latency, reduce congestion on mobile networks, and support advancements in technology. 6G has the potential to transform how the human, physical, and digital worlds interact with each other and the capability to support advancements in technology, such as virtual reality (VR), augmented reality (AR), the metaverse, and artificial intelligence (AI). Machine learning and deep learning modules are also an integral part of almost all automated systems where signal processing is performed at different levels. Signal processing in the form text, image, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of IC area with embedded bulk memories that lead to power consumption. Trade-offs between power consumption, delay, and IC area are always a concern of designers and researchers. Energy-efficient, high-speed data processing is required in major areas like biomedicine and healthcare, agriculture, transport, climate change, and national security and defense. This book will provide a foundation and initial inputs for researchers, scholars, and students working in the areas of wireless networks and high-speed data processing systems. It also provides techniques, tools, and methodologies to develop next-generation internet and 6G.
Table of Contents
Preface xxi
Acknowledgements xxiii
1 Introduction to AI Techniques for 6G Application 1
Manoj Singh Adhikari, Raju Patel, Manoj Sindhwani and Shippu Sachdeva
1.1 Introduction 2
1.2 Different Generation of Communication: From 1G to 6G 4
1.2.1 First Generation (1G) 4
1.2.2 Second Generation (2G) 5
1.2.3 Third Generation (3G) 5
1.2.4 Fourth Generation (4G) 5
1.2.5 Fifth Generation (5G) 5
1.2.6 Sixth Generation (6G) 5
1.3 Key Features and Requirements of 6G Networks 6
1.3.1 Faster Data Speeds 6
1.3.2 Ultra-Low Latency 6
1.3.3 Massive Capacity 7
1.3.4 Energy Efficiency 7
1.3.5 Seamless Connectivity 7
1.3.6 Advanced Spectrum Management 7
1.3.7 Enhanced Security and Privacy 7
1.3.8 Artificial Intelligence Integration 7
1.3.9 Heterogeneous Network Architecture 8
1.4 Role of Artificial Intelligence in 6G 8
1.4.1 Intelligent Radio Resource Management 9
1.4.2 Beamforming and MIMO 9
1.4.3 Intelligent Network Slicing 9
1.4.4 Intelligent Edge Computing 9
1.4.5 Intelligent Internet of Things 9
1.4.6 Enhanced Privacy 10
1.4.7 Intelligent Network Organization 10
1.4.8 Intelligent User Experience and Services 10
1.5 Machine Learning for 6G Networks 10
1.5.1 Intelligent Resource Management 11
1.5.2 Dynamic Spectrum Access 11
1.5.3 Intelligent Beamforming 11
1.5.4 Network Anomaly Detection 11
1.5.5 Intelligent Edge Computing 11
1.5.6 Intelligent Internet of Things 12
1.5.7 Intelligent Network Slicing 12
1.5.8 Intelligent Network Planning and Optimization 12
1.5.9 Predictive Maintenance 12
1.6 Deep Learning for 6G Applications 12
1.6.1 Enhanced Communication Systems 13
1.6.2 Intelligent Beamforming and Antenna Systems 13
1.6.3 Image and Video Processing 13
1.6.4 Intelligent Internet of Things 13
1.6.5 Autonomous Systems 13
1.6.6 Natural Language Processing and Speech Recognition 14
1.6.7 Augmented Reality and Virtual Reality 14
1.6.8 Network Security 14
1.7 Edge Computing and AI in 6G 14
1.7.1 Distributed Intelligence 14
1.7.2 Low-Latency Applications 15
1.7.3 Intelligent Edge Devices 15
1.7.4 Edge-AI-Assisted Network Management 15
1.7.5 Federated Learning 15
1.7.6 AI-Driven Security 15
1.7.7 Edge-AI for Content Delivery 16
1.7.8 Context-Aware Applications 16
1.8 AI-Enhanced Network Security in 6G 16
1.8.1 Threat Detection and Prevention 16
1.8.2 Anomaly Detection 17
1.8.3 Intrusion Detection and Prevention Systems (IDPS) 17
1.8.4 User Authentication 17
1.8.5 AI-Enabled Threat Intelligence 17
1.8.6 Automated Security Incident Response 17
1.8.7 AI-Enhanced Security Analytics 18
1.8.8 Privacy-Preserving Techniques 18
1.9 Quantum Computing and AI Fusion in 6G 18
1.9.1 Enhanced AI Algorithms 18
1.9.2 Optimization and Search Problems 19
1.9.3 Security and Encryption 19
1.9.4 Quantum-Assisted Machine Learning 19
1.9.5 Quantum Sensor Networks 19
1.9.6 Quantum-Assisted Simulation 19
1.9.7 Quantum Machine Learning 20
1.9.8 Quantum-Assisted Optimization 20
1.10 AI for Smart City Applications in 6G 20
1.10.1 Intelligent Traffic Management 20
1.10.2 Energy Management and Sustainability 21
1.10.3 Smart Infrastructure Monitoring 21
1.10.4 Waste Management 21
1.10.5 Smart Public Security and Safety 21
1.10.6 AI-Enabled Citizen Services 21
1.10.7 Urban Planning and Design 22
1.10.8 Data Analytics and Insights 22
1.11 Challenges and Future Directions 22
1.11.1 Technical Complexity 22
1.11.1.1 Future Directions 23
1.11.2 Privacy and Security 23
1.11.2.1 Future Directions 23
1.11.3 Ethical Considerations 24
1.11.3.1 Future Directions 24
1.11.4 Infrastructure and Energy Efficiency 24
1.11.4.1 Future Directions 24
1.11.5 Collaboration and Standardization 24
1.11.5.1 Future Directions 25
1.11.6 Socioeconomic Impact 25
1.11.6.1 Future Directions 25
1.11.7 Environmental Sustainability 25
1.11.7.1 Future Directions 25
1.12 Conclusion 25
References 26
2 AI Techniques for 6G Applications 29
Jyoti R. Munavalli, Rashmi R. Deshpande and Jayashree M. Oli
2.1 6G Communication 30
2.2 Artificial Intelligence (AI) Computing in 6G 34
2.3 Role of AI in 6G 37
2.4 AI Techniques for 6G 38
2.4.1 Supervised Learning 39
2.4.2 Unsupervised Learning 41
2.4.3 Reinforcement Learning 42
2.4.4 Federated Learning 44
2.4.5 Deep Learning 46
2.5 Use Cases/Applications 47
2.5.1 Holographic Applications 47
2.5.2 Ubiquitous Computing 48
2.5.3 Deep Sensing/Tactile Internet 50
2.5.4 Dynamic Spectrum Allocation 51
2.6 Conclusion 53
References 53
3 An Evaluation of Pervasive Computing Using Narrowband Technology: Exploring the Implications for 5G and Future Generations 57
Sriharipriya K. C., Athira Soman Nair, Kannanpuzha Chelsea Antony, Megha Nair B. and Amala Ipe
3.1 Introduction 58
3.2 Features 59
3.2.1 Power Consumption 59
3.2.2 Improved Coverage and Sensitivity with Low Latency 61
3.2.3 Transmission Mode 61
3.2.4 Resource of Spectrum 62
3.2.5 Mode of Working 62
3.2.6 Structure of Frame 64
3.2.7 Network of NB-IoT 64
3.2.8 Semi-Static Link Adaptation 66
3.2.9 Retransmission of Data 66
3.3 Basic Principles and Core Technologies of Narrowband 67
3.3.1 Theory of Analysis of Connection 67
3.3.2 Theory of Latency Survey 68
3.3.3 The Mechanism for Coverage Enhancement 69
3.3.4 Technology with Ultra-Low Power 70
3.3.5 Relationship of Coupling Between Signaling and Data 71
3.4 Correlation of Other Communication Technology with NB-IoT 72
3.4.1 With eMTC Technology 72
3.4.1.1 Coverage 74
3.4.1.2 Power Consumption 75
3.4.1.3 Connection Count 75
3.4.1.4 Voice Assistance 76
3.4.1.5 Mobility Management 76
3.4.1.6 Network Deployment’s Effect on the Current Network 76
3.4.1.7 Operative Mode 77
3.4.1.8 Combined Results 77
3.4.2 With More Wireless Network Methods 77
3.5 Applications 80
3.6 Security Needs 83
3.6.1 Perception Layer 84
3.6.2 Transmission Layers 85
3.6.3 Application Layer 86
3.7 Conclusion 87
References 88
4 Cumulant-Based Performance Analysis of 5G and 6G Communication Networks 93
Madhusmita Mishra, Sarat Kumar Patra and Ashok Kumar Turuk
4.1 Introduction 94
4.2 Performance Analysis of the Modified BSLM Technique Using PAPR Characteristics and Various Phase Sequences 96
4.2.1 Overview of SLM-Based PAPR Reduction and Modification 96
4.2.2 PAPR Reduction Analysis Using CCDF 100
4.2.3 Analysis of PAPR Reduction Using Various Phase Sequences 101
4.3 Mutual Independency Basing on Joint Cumulants 108
4.4 Computational Complexity 110
4.5 Conclusion 110
References 111
5 Leveraging 6G Networks for Disaster Monitoring and Management in Remote Sensing 115
G. Vinuja and N. Bharatha Devi
5.1 Introduction 116
5.2 Literature Review 118
5.2.1 Overview of 6G Networks and Their Potential Benefits in Disaster Management 127
5.3 Real-Time Disaster Monitoring and Management Using Remote Technologies 128
5.3.1 Enhanced Connectivity 128
5.3.2 Remote Sensing and Monitoring 128
5.3.3 Data Analytics and AI 129
5.3.4 Virtual Reality (VR) and Augmented Reality (AR) 129
5.3.5 Telemedicine and Remote Healthcare 129
5.3.6 Public Awareness and Communication 129
5.3.7 Smart Infrastructure and IoT Integration 130
5.3.8 Quicker Response Times 130
5.3.9 Enhanced Risk Assessment 130
5.3.10 Resource Allocation Optimization 130
5.3.11 Enhanced Coordination and Collaboration 130
5.3.12 Targeted Recovery and Reconstruction 131
5.3.13 Enhanced Preparedness and Planning 131
5.4 Methodology 131
5.4.1 Description of Research Design 132
5.4.2 Data Collection Methods 133
5.4.3 Analysis Techniques 134
5.5 Results 134
5.5.1 Summary of Data Collected 135
5.5.2 Analysis of Data 136
5.5.3 Discussion of Findings 136
5.6 Discussion 139
5.6.1 Interpretation of Results 139
5.6.2 Implications for the Future of Disaster Management 140
5.7 Conclusion 140
References 141
6 Applications of 6G-Based Remote Sensing Network in Environmental Monitoring 145
G. Vinuja and N. Bharatha Devi
6.1 Introduction 145
6.2 Literature Review 149
6.3 Experimental Methods and Materials 153
6.3.1 Fast Data Transfer and Processing 153
6.3.2 Improved Accuracy and Precision in Monitoring 154
6.3.3 Enhanced Data Security and Privacy 155
6.4 Results and Discussion 156
6.4.1 Innovative Remote Sensing Devices 156
6.4.2 Real-Time Monitoring Using Smart Sensors 157
6.4.3 Integration of 6G Technology and Artificial Intelligence 159
6.5 Applications of 6G-Based Remote Sensing Network in Environmental Monitoring 159
6.5.1 Soil and Water Quality Monitoring 160
6.5.2 Climate and Weather Monitoring 160
6.5.3 Air Pollution Monitoring 161
6.6 Challenges and Limitations of Implementing 6G Technology in Environmental Monitoring 161
6.6.1 High Cost of Installation and Maintenance 162
6.6.2 Lack of Trained Professionals in 6G Technology 162
6.6.3 Ethical and Legal Concerns Surrounding Data Privacy 163
6.7 Conclusion 163
References 164
7 Transforming Remote Sensing with Sixth-Generation Wireless Technology 169
Bishnu Kant Shukla, Amit Tripathi, Ayushi Bhati, Vaishnavi Bansal, Pushpendra Kumar Sharma and Shivam Verma
7.1 Introduction 170
7.2 Understanding Remote Sensing 171
7.2.1 Scattering and Absorption of EMR in Atmosphere 171
7.2.2 Interaction of EMR with Target 172
7.2.3 Spectral Signatures of Different Targets 172
7.3 Sensor Technologies in Remote Sensing 173
7.3.1 Passive and Active Sensors 173
7.3.2 Hyperspectral and Multispectral Sensors 173
7.3.3 Thermal Imaging 174
7.3.4 Geostationary and Geosynchronous Satellites 175
7.4 Resolution in Remote Sensing 176
7.4.1 Spatial Resolution 176
7.4.2 Spectral Resolution 177
7.4.3 Temporal Resolution 178
7.4.4 Radiometric Resolution 178
7.5 Remote Sensing Techniques and Processing 179
7.5.1 False Color Composite, True Color Composite 179
7.5.2 Stereoscopy 179
7.5.3 Along-Track Scanners, Across-Track Scanners 180
7.5.4 Instantaneous Field of View (IFOV) 180
7.5.5 Digital Image Processing 181
7.6 Microwave Remote Sensing 182
7.6.1 Radar 183
7.6.2 Radar Shadow Effects, Layover Effects 183
7.7 The Advent of 6G Technology 184
7.7.1 Understanding 6G Technology 184
7.7.2 Potential Impact of 6G on Remote Sensing 185
7.8 Transforming Remote Sensing with 6G 186
7.8.1 Improved Data Transfer and Processing 186
7.8.2 Energy Efficiency in Remote Sensing Systems 187
7.8.3 Increased Device Connectivity 188
7.9 Case Studies: Application of 6G in Remote Sensing 190
7.9.1 Agriculture: Crop Type Mapping, Crop Monitoring, and Damage Assessment 190
7.9.2 Forestry: Species Identification and Typing, Burn Mapping 192
7.9.3 Geology 192
7.10 Conclusion 193
References 195
8 Deep Learning Models for Image Annotation Application in a 6G Network Environment 201
Sandhya Avasthi, Suman Lata Tripathi, Tanushree Sanwal and Mufti Mahmud
8.1 Introduction 202
8.1.1 Image Detection and Annoation Applications 203
8.1.2 How Do 6G Networks Enhance Image Annotation Performance? 204
8.2 6G Network Overview 205
8.2.1 5G Limitations 206
8.2.2 Deep Learning with 6G 207
8.3 Deep Learning Models for Image Annotation 207
8.3.1 Convolution Neural Network (CNN) 208
8.3.2 Recurrent Neural Network 209
8.3.3 Long Short-Term Memory (LSTM) 210
8.4 Automatic Image Annotation Framework in Real Time 211
8.4.1 Deep Learning-Based Image Annotation Process Pipeline 211
8.4.2 Preprocessing 211
8.4.3 Feature Extraction 212
8.4.4 Segmentation 213
8.4.5 Object Detection 214
8.4.6 Annotation or Labeling of Objects 214
8.5 Challenges in Implementing Image Annotation Application 214
8.6 6G and Transformation World Wide 215
8.7 Challenges in 6G 216
8.8 Conclusion 218
References 219
9 Integration of Artificial Intelligence in 6G Networks for Processing Blood Cancer Data 223
R. Senthil Ganesh, S. A. Sivakumar and B. Maruthi Shankar
9.1 Insights into 6G Networks: Revolutionizing Healthcare Data Processing 224
9.2 Methodology for Blood Cancer Data Processing 226
9.3 Enhancing Diagnostics, Treatment Planning, and Patient Monitoring Using 6G Networks 228
9.4 Various AI Techniques for Analyzing Blood Cancer Data 229
9.5 AI Integration in 6G Networks for Blood Cancer Data Processing 230
9.6 Results and Discussions 233
9.7 Conclusion 236
References 238
10 Enhancing Connectivity and Data-Driven Decision-Making for Smart Agriculture by Embracing 6G Technology 241
Y.V.R. Naga Pawan and Kolla Bhanu Prakash
10.1 Fundamental Concepts of Smart Agriculture 242
10.1.1 Smart Agriculture 242
10.2 Applications of 6G in SA 243
10.3 Empowerment of 6G in SA 249
10.4 Enhanced Monitoring and Predictive Analytics in SA 250
10.4.1 Predictive Analytics 252
10.5 Advantages of 6G in SA 253
10.6 Challenges in the Implementation of 6G in SA 257
References 260
11 Security and Cost Optimization in Laser-Based Fencing Solutions 265
Sanmukh Kaur and Anurupa Lubana
11.1 Introduction 265
11.2 Potential Security Challenges 266
11.2.1 Beam Spoofing 266
11.2.2 Beam Bending 268
11.3 Objectives of the Chapter 268
11.3.1 To Defend the Laser Fencing Against Potential Attacks 268
11.3.2 To Optimize the Cost of Manufacturing and Operating 268
11.4 Secure Communication Protocol 269
11.4.1 Node Setup 269
11.4.2 Protocol 270
11.4.2.1 Packet Structure 270
11.4.2.2 Fence State 270
11.4.2.3 Seed and Encryption 271
11.4.2.4 Timestamp Counter 271
11.4.2.5 Error Checking 271
11.5 Algorithm 272
11.6 Conclusion 275
References 276
12 Security and Privacy in 6G-Based Human-Computer Interfaces: Challenges and Opportunities 277
Kamaraj Arunachalam and Senthil Kumar Jagatheesaperumal
12.1 Introduction 278
12.2 Evolution of 6G Networks and HCIs 280
12.2.1 Connected Robotics and Autonomous Systems 281
12.2.2 Wireless Brain-Computer Interactions (BCIs) 281
12.2.3 Haptic Communication and Smart Healthcare 282
12.2.4 Automation and Industrial Ecosystem 282
12.2.5 Internet of Everything (IoE) 282
12.3 Risks and Vulnerabilities in 6G-Based HCIs 283
12.4 Solutions and Strategies for Ensuring Security and Privacy 286
12.4.1 Authentication Techniques in 6G HCIs 286
12.4.2 Encryption Algorithms and Protocols 287
12.4.3 Cybersecurity Measures for HCIs 288
12.4.4 Privacy-Enhancing Technologies 289
12.5 Future Trends and Opportunities for Enhancing Security and Privacy 291
12.5.1 Advancements in User Identification and Authentication 291
12.5.2 Secure Data Transmission and Storage 292
12.5.3 Incorporating Privacy by Design 293
12.5.4 Collaboration and Standardization Efforts 293
12.6 Conclusion 294
References 294
13 Security and Privacy in 6G Applications: Optimization and Realization of Stochastic-Based Rapid Random Number Generation 299
S. Nithya Devi, S. Senthil Kumar, V. K. Reshma and S. Shanmugaraju
13.1 Introduction 300
13.2 Literature Review 302
13.3 Problem with Sensor Data 304
13.4 Study Process 305
13.4.1 Conventional Digital Clock Manager Scheme 305
13.4.2 Stochastic Circuits 307
13.4.3 Rapid Generating of Random Numbers Using a Stochastic Model 307
13.4.4 Received Signal Strength Indicator (RSSI) 309
13.4.5 Setting Up the Experiment and Collecting Data 310
13.4.6 QCA Multiplexers and D-Latch 310
13.5 Results and Analysis 312
13.6 Conclusion 315
References 316
14 Roles and Challenges of 6G for the Human-Computer Interface 319
Priyabrata Dash, Akankshya Patnaik, Sarat Kumar Sahoo and Franco Fernando Yanine
14.1 Introduction 320
14.2 Sixth Generation 322
14.3 Roles of 6G for the Human-Computer Interface 326
14.4 Challenges of 6G for the Human-Computer Interface 328
14.5 Uses of 6G in Different Sectors 331
14.6 Impact of 6G in Organizations 333
14.7 Conclusion 334
References 335
15 Leveraging 6G Technology for Advancements in Smart Agriculture: Opportunities and Challenges 339
B. Sathyasri, R.S. Valarmathi and G. Aloy Anuja Mary
15.1 Introduction 340
15.2 Literature Review 345
15.3 Methodology 345
15.3.1 Benefits of 6G in Smart Agriculture 345
15.3.2 Increased Precision and Accuracy in Farming Practices 346
15.3.3 Real-Time Monitoring and Data Collection 346
15.3.4 Improved Communication and Collaboration Among Farmers 347
15.3.5 Efficient Allocation of Resources 347
15.3.6 Enhanced Crop Yields and Quality 347
15.4 Challenges to Implementing 6G in Smart Agriculture 348
15.4.1 High Cost of Technology 348
15.4.2 Limited Network Coverage in Rural Areas 349
15.4.3 Concerns over Data Security and Privacy 349
15.4.4 Need for Technical Expertise to Operate and Maintain Technology 350
15.5 Potential Applications of 6G in Smart Agriculture 350
15.5.1 Crop Monitoring and Management 351
15.5.2 Livestock Monitoring and Disease Control 351
15.5.3 Smart Irrigation Systems 352
15.5.4 Automated Machinery and Equipment 352
15.5.5 Supply Chain Management 353
15.6 Expected Outcomes 353
15.7 Example of a Farm or Company That Has Successfully Adopted 6G Technology 354
15.8 Benefits Experienced and Impact on Agricultural Productivity 355
15.8.1 Lessons Learned and Recommendations for Others 356
15.9 Conclusion 358
References 359
16 Exploring 6G Research: Advancements, Applications, and Challenges 363
S. Senthil Kumar, S. Balaji, S. Nithya Devi and V. Priyadharsini
16.1 Introduction 364
16.2 Our Contributions and Comparable Work 365
16.2.1 Previous Studies 366
16.2.2 Contributions 367
16.3 Credibility 367
16.3.1 Reliability 367
16.3.2 Security and Safety 368
16.3.3 Dependability in 6G Networks 368
16.4 Reliability, ML, and 6G 368
16.4.1 Background in Brief 369
16.4.2 Dependability of Federated Learning 369
16.4.2.1 Reliability 370
16.4.2.2 Availability 371
16.4.2.3 Safety 371
16.5 Dependability for Mission-Critical Applications 371
16.5.1 Dependability Analysis of 6G MCAs 372
16.5.2 Availability 372
16.6 Future Research Directions 372
16.7 Conclusions 374
References 374
17 E-Travel ID-Based Bus Fare Collection System Using 6G Networks 379
S. A. Sivakumar, Pavithra K., Pavatharani P., Naviyarasu G. and Sajetha M.
17.1 Insights into 6G Networks 380
17.2 Impact of 6G on Transportation Sector 381
17.3 Existing Approach and Problem Identification 383
17.4 E-Travel ID-Based Bus Fare Collection System Using 6G Networks 385
17.5 Results and Discussion 388
17.6 Conclusion 392
References 393
18 Alert Generation Tool for Messaging Systems 395
Akshaya K. and Sanmukh Kaur
18.1 Introduction 395
18.2 Importance of Alerts in the Messaging System 396
18.2.1 System Health Monitoring 396
18.2.2 Proactive Issue Resolution 397
18.2.3 Performance Optimization 397
18.2.4 Capacity Planning 397
18.2.5 Security and Compliance 397
18.3 Monitoring CPU Usage in Real Time 398
18.3.1 Importance of CPU Usage Monitoring 398
18.3.1.1 Identifying Performance Bottlenecks 398
18.3.1.2 Diagnosing Performance Issues 398
18.3.1.3 Optimizing Resource Allocation 398
18.3.1.4 Proactive Issue Detection 399
18.3.1.5 Capacity Planning and Scaling 399
18.3.1.6 Resource Efficiency and Cost Optimization 399
18.3.2 Methodology 399
18.3.2.1 Importing the Necessary Libraries 399
18.3.2.2 User Input for Process ID 399
18.3.2.3 Defining the “warning()” Function 400
18.3.2.4 Defining the “monitor()” Function 400
18.3.2.5 Scheduling the Monitoring Tasks 400
18.3.2.6 Running the Monitoring Loop 401
18.3.2.7 Python Code 401
18.3.3 Output 403
18.3.4 Benefits of Real-Time CPU Usage Monitoring 404
18.4 URL Tracking 404
18.4.1 Methodology 405
18.4.1.1 Python Code 406
18.4.1.2 Output 406
18.4.1.3 Python Code 407
18.4.2 Output 408
18.5 Automated Delivery Performance Monitoring 409
18.5.1 Methodology 410
18.5.1.1 Code 412
18.5.2 Output 413
18.5.3 Applications 415
18.5.3.1 Marketing Campaigns 415
18.5.3.2 Transactional Notifications 415
18.5.3.3 Customer Support Systems 415
18.5.3.4 System Alerts 415
18.5.3.5 Performance Evaluation 415
18.6 High Volume of Testing Message Alert 416
18.6.1 Methodology 416
18.6.1.1 Import Necessary Libraries 416
18.6.1.2 Set Up Twilio and Email Credentials 416
18.6.1.3 Establish a Connection to MySQL Database 416
18.6.1.4 Create a Cursor Object and Execute a Query 416
18.6.1.5 Fetch Data and Create a Pandas DataFrame 417
18.6.1.6 Export Data to Excel 417
18.6.1.7 Count the Number of Testing Messages 417
18.6.1.8 Close the Cursor and Connection 417
18.6.1.9 Print Status Messages 417
18.6.1.10 Send SMS and Email Notifications 417
18.6.1.11 Python Code 418
18.6.2 Output 419
18.7 Conclusion 421
References 421
19 Design of an Underwater Robotic Fish Controlled through a Mobile Phone 423
Mohammed Nisam, N. Mouli Sharm, Vajid N. O., Sobhit Saxena and Suman Lata Tripathi
19.1 Introduction 423
19.1.1 Block Diagram 425
19.1.2 Flowchart and Explanation 427
19.2 Module Code Description 427
19.3 Description of Proposed Robotic Fish 429
19.4 Component and Material Selection 430
19.5 Conclusion 435
19.6 Suggestion for Future Work 435
References 436
About the Editors 439
Index 441
