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Submit a ProposalDevelopment of 6G Networks and Technology
 | Edited by Suman Lata Tripathi, Mufti Mahmood, C. Narmadha, and S. Albert Alexander Copyright: 2025 | Expected Pub Date:2024// ISBN: 9781394230655 | Hardcover | 472 pages Price: $225 USD  |
One Line Description
Development of 6G Networks and Technology provides an in-depth exploration of the potential impact of 6G networks on various industries, including healthcare, agriculture, transport, and national security, making it an essential resource for researchers, scholars, and students working in the field of wireless networks and high-speed data processing systems.
Audience
Undergraduate, postgraduate, and research students and faculty and researchers working on industry problems working with wireless networks and high-speed data processing systems
Undergraduate, postgraduate, and research students and faculty and researchers working on industry problems working with wireless networks and high-speed data processing systems
Description
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.
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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.
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Author / Editor Details
Suman Lata Tripathi, PhD is a professor at Lovely Professional University, Phagwara, India with more than seventeen years of experience in academics. She has published more than 74 research papers in refereed science journals and conferences, 14 books, and a book series in addition to 13 Indian patents and four copyrights. She has organized several workshops, summer internships, and expert lectures for students and has worked as a session chair, conference steering committee member, editorial board member, and peer reviewer in national and international journals and conferences.
Suman Lata Tripathi, PhD is a professor at Lovely Professional University, Phagwara, India with more than seventeen years of experience in academics. She has published more than 74 research papers in refereed science journals and conferences, 14 books, and a book series in addition to 13 Indian patents and four copyrights. She has organized several workshops, summer internships, and expert lectures for students and has worked as a session chair, conference steering committee member, editorial board member, and peer reviewer in national and international journals and conferences.
Mufti Mahmud, PhD is an associate professor of Cognitive Computing in the Department of Computer Science at Nottingham Trent University, United Kingdom. Additionally, he serves as the coordinator of the Computer Science and Informatics Unit of Assessment in the Research Excellence Framework at the university and the deputy group leader of and the Cognitive Computing and Brain Informatics research group. He is also an active member of the Computing and Informatics Research Centre and Medical Technologies Innovation Facility. He is a fellow of the Higher Education Academy, a senior member of the Institute of Electrical and Electronics Engineering, the Association of Computing Machinery, and a professional member of the British Computer Society.
C. Narmadha, PhD is an associate professor and head of the Department of Electronics and Communication Engineering at the Periyar Maniammai Institute of Science and Technology and University, India. She has more than 19 years of academic experience and has published more than 15 research articles. Additionally, she has organized several workshops, summer internships, expert lectures, and research and innovation activities for students. She is a professional associated as a member of the Indian Society for Technical Education, Computer Society of India, and Broadcast Engineering Society. Her areas of expertise include wireless sensor networks, computer and communication systems, and signal processing.
S. Albert Alexander, PhD is the recipient of the prestigious Raman Research Fellowship from the University Grants Commission from the Government of India. He has 15 years of academic and research experience, has published 57 technical papers in national and international journals, and presented 53 papers at national and international conferences. Additionally, he has completed four Government of India-funded projects and is currently working on an additional four research projects. He is a member and in prestigious positions in various national and international forums and has been invited as a speaker for over 250 programs across India and the United States.
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Table of Contents
Preface
Acknowledgements
1. Introduction to AI Techniques for 6G Application
Manoj Singh Adhikari, Raju Patel, Manoj Sindhwani and Shippu Sachdeva
1.1 Introduction
1.2 Different Generation of Communication: From 1G to 6G
1.2.1 First Generation (1G)
1.2.2 Second Generation (2G)
1.2.3 Third Generation (3G)
1.2.4 Fourth Generation (4G)
1.2.5 Fifth Generation (5G)
1.2.6 Sixth Generation (6G)
1.3 Key Features and Requirements of 6G Networks
1.3.1 Faster Data Speeds
1.3.2 Ultra-Low Latency
1.3.3 Massive Capacity
1.3.4 Energy Efficiency
1.3.5 Seamless Connectivity
1.3.6 Advanced Spectrum Management
1.3.7 Enhanced Security and Privacy
1.3.8 Artificial Intelligence Integration
1.3.9 Heterogeneous Network Architecture
1.4 Role of Artificial Intelligence in 6G
1.4.1 Intelligent Radio Resource Management
1.4.2 Beamforming and MIMO
1.4.3 Intelligent Network Slicing
1.4.4 Intelligent Edge Computing
1.4.5 Intelligent Internet of Things
1.4.6 Enhanced Privacy
1.4.7 Intelligent Network Organization
1.4.8 Intelligent User Experience and Services
1.5 Machine Learning for 6G Networks
1.5.1 Intelligent Resource Management
1.5.2 Dynamic Spectrum Access
1.5.3 Intelligent Beamforming
1.5.4 Network Anomaly Detection
1.5.5 Intelligent Edge Computing
1.5.6 Intelligent Internet of Things
1.5.7 Intelligent Network Slicing
1.5.8 Intelligent Network Planning and Optimization
1.5.9 Predictive Maintenance
1.6 Deep Learning for 6G Applications
1.6.1 Enhanced Communication Systems
1.6.2 Intelligent Beamforming and Antenna Systems
1.6.3 Image and Video Processing
1.6.4 Intelligent Internet of Things
1.6.5 Autonomous Systems
1.6.6 Natural Language Processing and Speech Recognition
1.6.7 Augmented Reality and Virtual Reality
1.6.8 Network Security
1.7 Edge Computing and AI in 6G
1.7.1 Distributed Intelligence
1.7.2 Low-Latency Applications
1.7.3 Intelligent Edge Devices
1.7.4 Edge-AI-Assisted Network Management
1.7.5 Federated Learning
1.7.6 AI-Driven Security
1.7.7 Edge-AI for Content Delivery
1.7.8 Context-Aware Applications
1.8 AI-Enhanced Network Security in 6G
1.8.1 Threat Detection and Prevention
1.8.2 Anomaly Detection
1.8.3 Intrusion Detection and Prevention Systems (IDPS)
1.8.4 User Authentication
1.8.5 AI-Enabled Threat Intelligence
1.8.6 Automated Security Incident Response
1.8.7 AI-Enhanced Security Analytics
1.8.8 Privacy-Preserving Techniques
1.9 Quantum Computing and AI Fusion in 6G
1.9.1 Enhanced AI Algorithms
1.9.2 Optimization and Search Problems
1.9.3 Security and Encryption
1.9.4 Quantum-Assisted Machine Learning
1.9.5 Quantum Sensor Networks
1.9.6 Quantum-Assisted Simulation
1.9.7 Quantum Machine Learning
1.9.8 Quantum-Assisted Optimization
1.10 AI for Smart City Applications in 6G
1.10.1 Intelligent Traffic Management
1.10.2 Energy Management and Sustainability
1.10.3 Smart Infrastructure Monitoring
1.10.4 Waste Management
1.10.5 Smart Public Security and Safety
1.10.6 AI-Enabled Citizen Services
1.10.7 Urban Planning and Design
1.10.8 Data Analytics and Insights
1.11 Challenges and Future Directions
1.11.1 Technical Complexity
1.11.1.1 Future Directions
1.11.2 Privacy and Security
1.11.2.1 Future Directions
1.11.3 Ethical Considerations
1.11.3.1 Future Directions
1.11.4 Infrastructure and Energy Efficiency
1.11.4.1 Future Directions
1.11.5 Collaboration and Standardization
1.11.5.1 Future Directions
1.11.6 Socioeconomic Impact
1.11.6.1 Future Directions
1.11.7 Environmental Sustainability
1.11.7.1 Future Directions
1.12 Conclusion
References
2. AI Techniques for 6G Applications
Jyoti R. Munavalli, Rashmi R. Deshpande and Jayashree M. Oli
2.1 6G Communication
2.2 Artificial Intelligence (AI) Computing in 6G
2.3 Role of AI in 6G
2.4 AI Techniques for 6G
2.4.1 Supervised Learning
2.4.2 Unsupervised Learning
2.4.3 Reinforcement Learning
2.4.4 Federated Learning
2.4.5 Deep Learning
2.5 Use Cases/Applications
2.5.1 Holographic Applications
2.5.2 Ubiquitous Computing
2.5.3 Deep Sensing/Tactile Internet
2.5.4 Dynamic Spectrum Allocation
2.6 Conclusion
References
3. An Evaluation of Pervasive Computing Using Narrowband Technology: Exploring the Implications for 5G and Future Generations
Sriharipriya K. C., Athira Soman Nair, Kannanpuzha Chelsea Antony, Megha Nair B. and Amala Ipe
3.1 Introduction
3.2 Features
3.2.1 Power Consumption
3.2.2 Improved Coverage and Sensitivity with Low Latency
3.2.3 Transmission Mode
3.2.4 Resource of Spectrum
3.2.5 Mode of Working
3.2.6 Structure of Frame
3.2.7 Network of NB-IoT
3.2.8 Semi-Static Link Adaptation
3.2.9 Retransmission of Data
3.3 Basic Principles and Core Technologies of Narrowband
3.3.1 Theory of Analysis of Connection
3.3.2 Theory of Latency Survey
3.3.3 The Mechanism for Coverage Enhancement
3.3.4 Technology with Ultra-Low Power
3.3.5 Relationship of Coupling Between Signaling and Data
3.4 Correlation of Other Communication Technology with NB-IoT
3.4.1 With eMTC Technology
3.4.1.1 Coverage
3.4.1.2 Power Consumption
3.4.1.3 Connection Count
3.4.1.4 Voice Assistance
3.4.1.5 Mobility Management
3.4.1.6 Network Deployment’s Effect on the Current Network
3.4.1.7 Operative Mode
3.4.1.8 Combined Results
3.4.2 With More Wireless Network Methods
3.5 Applications
3.6 Security Needs
3.6.1 Perception Layer
3.6.2 Transmission Layers
3.6.3 Application Layer
3.7 Conclusion
References
4. Cumulant-Based Performance Analysis of 5G and 6G Communication Networks
Madhusmita Mishra, Sarat Kumar Patra and Ashok Kumar Turuk
4.1 Introduction
4.2 Performance Analysis of the Modified BSLM Technique Using PAPR Characteristics and Various Phase Sequences
4.2.1 Overview of SLM-Based PAPR Reduction and Modification
4.2.2 PAPR Reduction Analysis Using CCDF
4.2.3 Analysis of PAPR Reduction Using Various Phase Sequences
4.3 Mutual Independency Basing on Joint Cumulants
4.4 Computational Complexity
4.5 Conclusion
References
5. Leveraging 6G Networks for Disaster Monitoring and Management in Remote Sensing
G. Vinuja and N. Bharatha Devi
5.1 Introduction
5.2 Literature Review
5.2.1 Overview of 6G Networks and Their Potential Benefits in Disaster Management
5.3 Real-Time Disaster Monitoring and Management Using Remote Technologies
5.3.1 Enhanced Connectivity
5.3.2 Remote Sensing and Monitoring
5.3.3 Data Analytics and AI
5.3.4 Virtual Reality (VR) and Augmented Reality (AR)
5.3.5 Telemedicine and Remote Healthcare
5.3.6 Public Awareness and Communication
5.3.7 Smart Infrastructure and IoT Integration
5.3.8 Quicker Response Times
5.3.9 Enhanced Risk Assessment
5.3.10 Resource Allocation Optimization
5.3.11 Enhanced Coordination and Collaboration
5.3.12 Targeted Recovery and Reconstruction
5.3.13 Enhanced Preparedness and Planning
5.4 Methodology
5.4.1 Description of Research Design
5.4.2 Data Collection Methods
5.4.3 Analysis Techniques
5.5 Results
5.5.1 Summary of Data Collected
5.5.2 Analysis of Data
5.5.3 Discussion of Findings
5.6 Discussion
5.6.1 Interpretation of Results
5.6.2 Implications for the Future of Disaster Management
5.7 Conclusion
References
6. Applications of 6G-Based Remote Sensing Network in Environmental Monitoring
G. Vinuja and N. Bharatha Devi
6.1 Introduction
6.2 Literature Review
6.3 Experimental Methods and Materials
6.3.1 Fast Data Transfer and Processing
6.3.2 Improved Accuracy and Precision in Monitoring
6.3.3 Enhanced Data Security and Privacy
6.4 Results and Discussion
6.4.1 Innovative Remote Sensing Devices
6.4.2 Real-Time Monitoring Using Smart Sensors
6.4.3 Integration of 6G Technology and Artificial Intelligence
6.5 Applications of 6G-Based Remote Sensing Network in Environmental Monitoring
6.5.1 Soil and Water Quality Monitoring
6.5.2 Climate and Weather Monitoring
6.5.3 Air Pollution Monitoring
6.6 Challenges and Limitations of Implementing 6G Technology in Environmental Monitoring
6.6.1 High Cost of Installation and Maintenance
6.6.2 Lack of Trained Professionals in 6G Technology
6.6.3 Ethical and Legal Concerns Surrounding Data Privacy
6.7 Conclusion
References
7. Transforming Remote Sensing with Sixth-Generation Wireless Technology
Bishnu Kant Shukla, Amit Tripathi, Ayushi Bhati, Vaishnavi Bansal, Pushpendra Kumar Sharma and Shivam Verma
7.1 Introduction
7.2 Understanding Remote Sensing
7.2.1 Scattering and Absorption of EMR in Atmosphere
7.2.2 Interaction of EMR with Target
7.2.3 Spectral Signatures of Different Targets
7.3 Sensor Technologies in Remote Sensing
7.3.1 Passive and Active Sensors
7.3.2 Hyperspectral and Multispectral Sensors
7.3.3 Thermal Imaging
7.3.4 Geostationary and Geosynchronous Satellites
7.4 Resolution in Remote Sensing
7.4.1 Spatial Resolution
7.4.2 Spectral Resolution
7.4.3 Temporal Resolution
7.4.4 Radiometric Resolution
7.5 Remote Sensing Techniques and Processing
7.5.1 False Color Composite, True Color Composite
7.5.2 Stereoscopy
7.5.3 Along-Track Scanners, Across-Track Scanners
7.5.4 Instantaneous Field of View (IFOV)
7.5.5 Digital Image Processing
7.6 Microwave Remote Sensing
7.6.1 Radar
7.6.2 Radar Shadow Effects, Layover Effects
7.7 The Advent of 6G Technology
7.7.1 Understanding 6G Technology
7.7.2 Potential Impact of 6G on Remote Sensing
7.8 Transforming Remote Sensing with 6G
7.8.1 Improved Data Transfer and Processing
7.8.2 Energy Efficiency in Remote Sensing Systems
7.8.3 Increased Device Connectivity
7.9 Case Studies: Application of 6G in Remote Sensing
7.9.1 Agriculture: Crop Type Mapping, Crop Monitoring, and Damage Assessment
7.9.2 Forestry: Species Identification and Typing, Burn Mapping
7.9.3 Geology
7.10 Conclusion
References
8. Deep Learning Models for Image Annotation Application in a 6G Network Environment
Sandhya Avasthi, Suman Lata Tripathi, Tanushree Sanwal and Mufti Mahmud
8.1 Introduction
8.1.1 Image Detection and Annoation Applications
8.1.2 How Do 6G Networks Enhance Image Annotation Performance?
8.2 6G Network Overview
8.2.1 5G Limitations
8.2.2 Deep Learning with 6G
8.3 Deep Learning Models for Image Annotation
8.3.1 Convolution Neural Network (CNN)
8.3.2 Recurrent Neural Network
8.3.3 Long Short-Term Memory (LSTM)
8.4 Automatic Image Annotation Framework in Real Time
8.4.1 Deep Learning-Based Image Annotation Process Pipeline
8.4.2 Preprocessing
8.4.3 Feature Extraction
8.4.4 Segmentation
8.4.5 Object Detection
8.4.6 Annotation or Labeling of Objects
8.5 Challenges in Implementing Image Annotation Application
8.6 6G and Transformation World Wide
8.7 Challenges in 6G
8.8 Conclusion
References
9. Integration of Artificial Intelligence in 6G Networks for Processing Blood Cancer Data
R. Senthil Ganesh, S. A. Sivakumar and B. Maruthi Shankar
9.1 Insights into 6G Networks: Revolutionizing Healthcare Data Processing
9.2 Methodology for Blood Cancer Data Processing
9.3 Enhancing Diagnostics, Treatment Planning, and Patient Monitoring Using 6G Networks
9.4 Various AI Techniques for Analyzing Blood Cancer Data
9.5 AI Integration in 6G Networks for Blood Cancer Data Processing
9.6 Results and Discussions
9.7 Conclusion
References
10. Enhancing Connectivity and Data-Driven Decision-Making for Smart Agriculture by Embracing 6G Technology
Y.V.R. Naga Pawan and Kolla Bhanu Prakash
10.1 Fundamental Concepts of Smart Agriculture
10.1.1 Smart Agriculture
10.2 Applications of 6G in SA
10.3 Empowerment of 6G in SA
10.4 Enhanced Monitoring and Predictive Analytics in SA
10.4.1 Predictive Analytics
10.5 Advantages of 6G in SA
10.6 Challenges in the Implementation of 6G in SA
References
11. Security and Cost Optimization in Laser-Based Fencing Solutions
Sanmukh Kaur and Anurupa Lubana
11.1 Introduction
11.2 Potential Security Challenges
11.2.1 Beam Spoofing
11.2.2 Beam Bending
11.3 Objectives of the Chapter
11.3.1 To Defend the Laser Fencing Against Potential Attacks
11.3.2 To Optimize the Cost of Manufacturing and Operating
11.4 Secure Communication Protocol
11.4.1 Node Setup
11.4.2 Protocol
11.4.2.1 Packet Structure
11.4.2.2 Fence State
11.4.2.3 Seed and Encryption
11.4.2.4 Timestamp Counter
11.4.2.5 Error Checking
11.5 Algorithm
11.6 Conclusion
References
12. Security and Privacy in 6G-Based Human–Computer Interfaces: Challenges and Opportunities
Kamaraj Arunachalam and Senthil Kumar Jagatheesaperumal
12.1 Introduction
12.2 Evolution of 6G Networks and HCIs
12.2.1 Connected Robotics and Autonomous Systems
12.2.2 Wireless Brain–Computer Interactions (BCIs)
12.2.3 Haptic Communication and Smart Healthcare
12.2.4 Automation and Industrial Ecosystem
12.2.5 Internet of Everything (IoE)
12.3 Risks and Vulnerabilities in 6G-Based HCIs
12.4 Solutions and Strategies for Ensuring Security and Privacy
12.4.1 Authentication Techniques in 6G HCIs
12.4.2 Encryption Algorithms and Protocols
12.4.3 Cybersecurity Measures for HCIs
12.4.4 Privacy-Enhancing Technologies
12.5 Future Trends and Opportunities for Enhancing Security and Privacy
12.5.1 Advancements in User Identification and Authentication
12.5.2 Secure Data Transmission and Storage
12.5.3 Incorporating Privacy by Design
12.5.4 Collaboration and Standardization Efforts
12.6 Conclusion
References
13. Security and Privacy in 6G Applications: Optimization and Realization of Stochastic-Based Rapid Random Number Generation
S. Nithya Devi, S. Senthil Kumar, V. K. Reshma and S. Shanmugaraju
13.1 Introduction
13.2 Literature Review
13.3 Problem with Sensor Data
13.4 Study Process
13.4.1 Conventional Digital Clock Manager Scheme
13.4.2 Stochastic Circuits
13.4.3 Rapid Generating of Random Numbers Using a Stochastic Model
13.4.4 Received Signal Strength Indicator (RSSI)
13.4.5 Setting Up the Experiment and Collecting Data
13.4.6 QCA Multiplexers and D-Latch
13.5 Results and Analysis
13.6 Conclusion
References
14. Roles and Challenges of 6G for the Human–Computer Interface
Priyabrata Dash, Akankshya Patnaik, Sarat Kumar Sahoo and Franco Fernando Yanine
14.1 Introduction
14.2 Sixth Generation
14.3 Roles of 6G for the Human–Computer Interface
14.4 Challenges of 6G for the Human–Computer Interface
14.5 Uses of 6G in Different Sectors
14.6 Impact of 6G in Organizations
14.7 Conclusion
References
15. Leveraging 6G Technology for Advancements in Smart Agriculture: Opportunities and Challenges
B. Sathyasri, R.S. Valarmathi and G. Aloy Anuja Mary
15.1 Introduction
15.2 Literature Review
15.3 Methodology
15.3.1 Benefits of 6G in Smart Agriculture
15.3.2 Increased Precision and Accuracy in Farming Practices
15.3.3 Real-Time Monitoring and Data Collection
15.3.4 Improved Communication and Collaboration Among Farmers
15.3.5 Efficient Allocation of Resources
15.3.6 Enhanced Crop Yields and Quality
15.4 Challenges to Implementing 6G in Smart Agriculture
15.4.1 High Cost of Technology
15.4.2 Limited Network Coverage in Rural Areas
15.4.5 Concerns over Data Security and Privacy
15.4.6 Need for Technical Expertise to Operate and Maintain Technology
15.5 Potential Applications of 6G in Smart Agriculture
15.5.1 Crop Monitoring and Management
15.5.2 Livestock Monitoring and Disease Control
15.5.3 Smart Irrigation Systems
15.5.4 Automated Machinery and Equipment
15.5.5 Supply Chain Management
15.6 Expected Outcomes
15.7 Example of a Farm or Company That Has Successfully Adopted 6G Technology
15.8 Benefits Experienced and Impact on Agricultural Productivity
15.8.1 Lessons Learned and Recommendations for Others
15.9 Conclusion
References
16. Exploring 6G Research: Advancements, Applications, and Challenges
S. Senthil Kumar, S. Balaji, S. Nithya Devi and V. Priyadharsini
16.1 Introduction
16.2 Our Contributions and Comparable Work
16.2.1 Previous Studies
16.2.2 Contributions
16.3 Credibility
16.3.1 Reliability
16.3.2 Security and Safety
16.3.3 Dependability in 6G Networks
16.4 Reliability, ML, and 6G
16.4.1 Background in Brief
16.4.2 Dependability of Federated Learning
16.4.2.1 Reliability
16.4.2.2 Availability
16.4.2.3 Safety
16.5 Dependability for Mission-Critical Applications
16.5.1 Dependability Analysis of 6G MCAs
16.5.2 Availability
16.6 Future Research Directions
16.7 Conclusions
References
17. E-Travel ID-Based Bus Fare Collection System Using 6G Networks
S. A. Sivakumar, Pavithra K., Pavatharani P., Naviyarasu G. and Sajetha M.
17.1 Insights into 6G Networks
17.2 Impact of 6G on Transportation Sector
17.3 Existing Approach and Problem Identification
17.4 E-Travel ID-Based Bus Fare Collection System Using 6G Networks
17.5 Results and Discussion
17.6 Conclusion
References
18. Alert Generation Tool for Messaging Systems
Akshaya K. and Sanmukh Kaur
18.1 Introduction
18.2 Importance of Alerts in the Messaging System
18.2.1 System Health Monitoring
18.2.2 Proactive Issue Resolution
18.2.3 Performance Optimization
18.2.4 Capacity Planning
18.2.5 Security and Compliance
18.3 Monitoring CPU Usage in Real Time
18.3.1 Importance of CPU Usage Monitoring
18.3.1.1 Identifying Performance Bottlenecks
18.3.1.2 Diagnosing Performance Issues
18.3.1.3 Optimizing Resource Allocation
18.3.1.4 Proactive Issue Detection
18.3.1.5 Capacity Planning and Scaling
18.3.1.6 Resource Efficiency and Cost Optimization
18.3.2 Methodology
18.3.2.1 Importing the Necessary Libraries
18.3.2.2 User Input for Process ID
18.3.2.3 Defining the “warning()” Function
18.3.2.4 Defining the “monitor()” Function
18.3.2.5 Scheduling the Monitoring Tasks
18.3.2.6 Running the Monitoring Loop
18.3.2.7 Python Code
18.3.3 Output
18.3.4 Benefits of Real-Time CPU Usage Monitoring
18.4 URL Tracking
18.4.1 Methodology
18.4.1.1 Python Code
18.4.1.2 Output
18.4.1.3 Python Code
18.4.2 Output
18.5 Automated Delivery Performance Monitoring
18.5.1 Methodology
18.5.1.1 Code
18.5.2 Output
18.5.3 Applications
18.5.3.1 Marketing Campaigns
18.5.3.2 Transactional Notifications
18.5.3.3 Customer Support Systems
18.5.3.4 System Alerts
18.5.3.5 Performance Evaluation
18.6 High Volume of Testing Message Alert
18.6.1 Methodology
18.6.1.1 Import Necessary Libraries
18.6.1.2 Set Up Twilio and Email Credentials
18.6.1.3 Establish a Connection to MySQL Database
18.6.1.4 Create a Cursor Object and Execute a Query
18.6.1.5 Fetch Data and Create a Pandas DataFrame
18.6.1.6 Export Data to Excel
18.6.1.7 Count the Number of Testing Messages
18.6.1.8 Close the Cursor and Connection
18.6.1.9 Print Status Messages
18.6.1.10 Send SMS and Email Notifications
18.6.1.11 Python Code
18.6.2 Output
18.7 Conclusion
References
19. Design of an Underwater Robotic Fish Controlled through a Mobile Phone
Mohammed Nisam, N. Mouli Sharm, Vajid N. O., Sobhit Saxena and Suman Lata Tripathi
19.1 Introduction
19.1.1 Block diagram
19.1.2 Flowchart and Explanation:
19.2 Module Code Description
19.3 Description of Proposed Robotic Fish
19.4 Component and Material Selection
19.5 Conclusion
19.6 Suggestion for Future Work
References
About the Editors
Index
Back to Top
Preface
Acknowledgements
1. Introduction to AI Techniques for 6G Application
Manoj Singh Adhikari, Raju Patel, Manoj Sindhwani and Shippu Sachdeva
1.1 Introduction
1.2 Different Generation of Communication: From 1G to 6G
1.2.1 First Generation (1G)
1.2.2 Second Generation (2G)
1.2.3 Third Generation (3G)
1.2.4 Fourth Generation (4G)
1.2.5 Fifth Generation (5G)
1.2.6 Sixth Generation (6G)
1.3 Key Features and Requirements of 6G Networks
1.3.1 Faster Data Speeds
1.3.2 Ultra-Low Latency
1.3.3 Massive Capacity
1.3.4 Energy Efficiency
1.3.5 Seamless Connectivity
1.3.6 Advanced Spectrum Management
1.3.7 Enhanced Security and Privacy
1.3.8 Artificial Intelligence Integration
1.3.9 Heterogeneous Network Architecture
1.4 Role of Artificial Intelligence in 6G
1.4.1 Intelligent Radio Resource Management
1.4.2 Beamforming and MIMO
1.4.3 Intelligent Network Slicing
1.4.4 Intelligent Edge Computing
1.4.5 Intelligent Internet of Things
1.4.6 Enhanced Privacy
1.4.7 Intelligent Network Organization
1.4.8 Intelligent User Experience and Services
1.5 Machine Learning for 6G Networks
1.5.1 Intelligent Resource Management
1.5.2 Dynamic Spectrum Access
1.5.3 Intelligent Beamforming
1.5.4 Network Anomaly Detection
1.5.5 Intelligent Edge Computing
1.5.6 Intelligent Internet of Things
1.5.7 Intelligent Network Slicing
1.5.8 Intelligent Network Planning and Optimization
1.5.9 Predictive Maintenance
1.6 Deep Learning for 6G Applications
1.6.1 Enhanced Communication Systems
1.6.2 Intelligent Beamforming and Antenna Systems
1.6.3 Image and Video Processing
1.6.4 Intelligent Internet of Things
1.6.5 Autonomous Systems
1.6.6 Natural Language Processing and Speech Recognition
1.6.7 Augmented Reality and Virtual Reality
1.6.8 Network Security
1.7 Edge Computing and AI in 6G
1.7.1 Distributed Intelligence
1.7.2 Low-Latency Applications
1.7.3 Intelligent Edge Devices
1.7.4 Edge-AI-Assisted Network Management
1.7.5 Federated Learning
1.7.6 AI-Driven Security
1.7.7 Edge-AI for Content Delivery
1.7.8 Context-Aware Applications
1.8 AI-Enhanced Network Security in 6G
1.8.1 Threat Detection and Prevention
1.8.2 Anomaly Detection
1.8.3 Intrusion Detection and Prevention Systems (IDPS)
1.8.4 User Authentication
1.8.5 AI-Enabled Threat Intelligence
1.8.6 Automated Security Incident Response
1.8.7 AI-Enhanced Security Analytics
1.8.8 Privacy-Preserving Techniques
1.9 Quantum Computing and AI Fusion in 6G
1.9.1 Enhanced AI Algorithms
1.9.2 Optimization and Search Problems
1.9.3 Security and Encryption
1.9.4 Quantum-Assisted Machine Learning
1.9.5 Quantum Sensor Networks
1.9.6 Quantum-Assisted Simulation
1.9.7 Quantum Machine Learning
1.9.8 Quantum-Assisted Optimization
1.10 AI for Smart City Applications in 6G
1.10.1 Intelligent Traffic Management
1.10.2 Energy Management and Sustainability
1.10.3 Smart Infrastructure Monitoring
1.10.4 Waste Management
1.10.5 Smart Public Security and Safety
1.10.6 AI-Enabled Citizen Services
1.10.7 Urban Planning and Design
1.10.8 Data Analytics and Insights
1.11 Challenges and Future Directions
1.11.1 Technical Complexity
1.11.1.1 Future Directions
1.11.2 Privacy and Security
1.11.2.1 Future Directions
1.11.3 Ethical Considerations
1.11.3.1 Future Directions
1.11.4 Infrastructure and Energy Efficiency
1.11.4.1 Future Directions
1.11.5 Collaboration and Standardization
1.11.5.1 Future Directions
1.11.6 Socioeconomic Impact
1.11.6.1 Future Directions
1.11.7 Environmental Sustainability
1.11.7.1 Future Directions
1.12 Conclusion
References
2. AI Techniques for 6G Applications
Jyoti R. Munavalli, Rashmi R. Deshpande and Jayashree M. Oli
2.1 6G Communication
2.2 Artificial Intelligence (AI) Computing in 6G
2.3 Role of AI in 6G
2.4 AI Techniques for 6G
2.4.1 Supervised Learning
2.4.2 Unsupervised Learning
2.4.3 Reinforcement Learning
2.4.4 Federated Learning
2.4.5 Deep Learning
2.5 Use Cases/Applications
2.5.1 Holographic Applications
2.5.2 Ubiquitous Computing
2.5.3 Deep Sensing/Tactile Internet
2.5.4 Dynamic Spectrum Allocation
2.6 Conclusion
References
3. An Evaluation of Pervasive Computing Using Narrowband Technology: Exploring the Implications for 5G and Future Generations
Sriharipriya K. C., Athira Soman Nair, Kannanpuzha Chelsea Antony, Megha Nair B. and Amala Ipe
3.1 Introduction
3.2 Features
3.2.1 Power Consumption
3.2.2 Improved Coverage and Sensitivity with Low Latency
3.2.3 Transmission Mode
3.2.4 Resource of Spectrum
3.2.5 Mode of Working
3.2.6 Structure of Frame
3.2.7 Network of NB-IoT
3.2.8 Semi-Static Link Adaptation
3.2.9 Retransmission of Data
3.3 Basic Principles and Core Technologies of Narrowband
3.3.1 Theory of Analysis of Connection
3.3.2 Theory of Latency Survey
3.3.3 The Mechanism for Coverage Enhancement
3.3.4 Technology with Ultra-Low Power
3.3.5 Relationship of Coupling Between Signaling and Data
3.4 Correlation of Other Communication Technology with NB-IoT
3.4.1 With eMTC Technology
3.4.1.1 Coverage
3.4.1.2 Power Consumption
3.4.1.3 Connection Count
3.4.1.4 Voice Assistance
3.4.1.5 Mobility Management
3.4.1.6 Network Deployment’s Effect on the Current Network
3.4.1.7 Operative Mode
3.4.1.8 Combined Results
3.4.2 With More Wireless Network Methods
3.5 Applications
3.6 Security Needs
3.6.1 Perception Layer
3.6.2 Transmission Layers
3.6.3 Application Layer
3.7 Conclusion
References
4. Cumulant-Based Performance Analysis of 5G and 6G Communication Networks
Madhusmita Mishra, Sarat Kumar Patra and Ashok Kumar Turuk
4.1 Introduction
4.2 Performance Analysis of the Modified BSLM Technique Using PAPR Characteristics and Various Phase Sequences
4.2.1 Overview of SLM-Based PAPR Reduction and Modification
4.2.2 PAPR Reduction Analysis Using CCDF
4.2.3 Analysis of PAPR Reduction Using Various Phase Sequences
4.3 Mutual Independency Basing on Joint Cumulants
4.4 Computational Complexity
4.5 Conclusion
References
5. Leveraging 6G Networks for Disaster Monitoring and Management in Remote Sensing
G. Vinuja and N. Bharatha Devi
5.1 Introduction
5.2 Literature Review
5.2.1 Overview of 6G Networks and Their Potential Benefits in Disaster Management
5.3 Real-Time Disaster Monitoring and Management Using Remote Technologies
5.3.1 Enhanced Connectivity
5.3.2 Remote Sensing and Monitoring
5.3.3 Data Analytics and AI
5.3.4 Virtual Reality (VR) and Augmented Reality (AR)
5.3.5 Telemedicine and Remote Healthcare
5.3.6 Public Awareness and Communication
5.3.7 Smart Infrastructure and IoT Integration
5.3.8 Quicker Response Times
5.3.9 Enhanced Risk Assessment
5.3.10 Resource Allocation Optimization
5.3.11 Enhanced Coordination and Collaboration
5.3.12 Targeted Recovery and Reconstruction
5.3.13 Enhanced Preparedness and Planning
5.4 Methodology
5.4.1 Description of Research Design
5.4.2 Data Collection Methods
5.4.3 Analysis Techniques
5.5 Results
5.5.1 Summary of Data Collected
5.5.2 Analysis of Data
5.5.3 Discussion of Findings
5.6 Discussion
5.6.1 Interpretation of Results
5.6.2 Implications for the Future of Disaster Management
5.7 Conclusion
References
6. Applications of 6G-Based Remote Sensing Network in Environmental Monitoring
G. Vinuja and N. Bharatha Devi
6.1 Introduction
6.2 Literature Review
6.3 Experimental Methods and Materials
6.3.1 Fast Data Transfer and Processing
6.3.2 Improved Accuracy and Precision in Monitoring
6.3.3 Enhanced Data Security and Privacy
6.4 Results and Discussion
6.4.1 Innovative Remote Sensing Devices
6.4.2 Real-Time Monitoring Using Smart Sensors
6.4.3 Integration of 6G Technology and Artificial Intelligence
6.5 Applications of 6G-Based Remote Sensing Network in Environmental Monitoring
6.5.1 Soil and Water Quality Monitoring
6.5.2 Climate and Weather Monitoring
6.5.3 Air Pollution Monitoring
6.6 Challenges and Limitations of Implementing 6G Technology in Environmental Monitoring
6.6.1 High Cost of Installation and Maintenance
6.6.2 Lack of Trained Professionals in 6G Technology
6.6.3 Ethical and Legal Concerns Surrounding Data Privacy
6.7 Conclusion
References
7. Transforming Remote Sensing with Sixth-Generation Wireless Technology
Bishnu Kant Shukla, Amit Tripathi, Ayushi Bhati, Vaishnavi Bansal, Pushpendra Kumar Sharma and Shivam Verma
7.1 Introduction
7.2 Understanding Remote Sensing
7.2.1 Scattering and Absorption of EMR in Atmosphere
7.2.2 Interaction of EMR with Target
7.2.3 Spectral Signatures of Different Targets
7.3 Sensor Technologies in Remote Sensing
7.3.1 Passive and Active Sensors
7.3.2 Hyperspectral and Multispectral Sensors
7.3.3 Thermal Imaging
7.3.4 Geostationary and Geosynchronous Satellites
7.4 Resolution in Remote Sensing
7.4.1 Spatial Resolution
7.4.2 Spectral Resolution
7.4.3 Temporal Resolution
7.4.4 Radiometric Resolution
7.5 Remote Sensing Techniques and Processing
7.5.1 False Color Composite, True Color Composite
7.5.2 Stereoscopy
7.5.3 Along-Track Scanners, Across-Track Scanners
7.5.4 Instantaneous Field of View (IFOV)
7.5.5 Digital Image Processing
7.6 Microwave Remote Sensing
7.6.1 Radar
7.6.2 Radar Shadow Effects, Layover Effects
7.7 The Advent of 6G Technology
7.7.1 Understanding 6G Technology
7.7.2 Potential Impact of 6G on Remote Sensing
7.8 Transforming Remote Sensing with 6G
7.8.1 Improved Data Transfer and Processing
7.8.2 Energy Efficiency in Remote Sensing Systems
7.8.3 Increased Device Connectivity
7.9 Case Studies: Application of 6G in Remote Sensing
7.9.1 Agriculture: Crop Type Mapping, Crop Monitoring, and Damage Assessment
7.9.2 Forestry: Species Identification and Typing, Burn Mapping
7.9.3 Geology
7.10 Conclusion
References
8. Deep Learning Models for Image Annotation Application in a 6G Network Environment
Sandhya Avasthi, Suman Lata Tripathi, Tanushree Sanwal and Mufti Mahmud
8.1 Introduction
8.1.1 Image Detection and Annoation Applications
8.1.2 How Do 6G Networks Enhance Image Annotation Performance?
8.2 6G Network Overview
8.2.1 5G Limitations
8.2.2 Deep Learning with 6G
8.3 Deep Learning Models for Image Annotation
8.3.1 Convolution Neural Network (CNN)
8.3.2 Recurrent Neural Network
8.3.3 Long Short-Term Memory (LSTM)
8.4 Automatic Image Annotation Framework in Real Time
8.4.1 Deep Learning-Based Image Annotation Process Pipeline
8.4.2 Preprocessing
8.4.3 Feature Extraction
8.4.4 Segmentation
8.4.5 Object Detection
8.4.6 Annotation or Labeling of Objects
8.5 Challenges in Implementing Image Annotation Application
8.6 6G and Transformation World Wide
8.7 Challenges in 6G
8.8 Conclusion
References
9. Integration of Artificial Intelligence in 6G Networks for Processing Blood Cancer Data
R. Senthil Ganesh, S. A. Sivakumar and B. Maruthi Shankar
9.1 Insights into 6G Networks: Revolutionizing Healthcare Data Processing
9.2 Methodology for Blood Cancer Data Processing
9.3 Enhancing Diagnostics, Treatment Planning, and Patient Monitoring Using 6G Networks
9.4 Various AI Techniques for Analyzing Blood Cancer Data
9.5 AI Integration in 6G Networks for Blood Cancer Data Processing
9.6 Results and Discussions
9.7 Conclusion
References
10. Enhancing Connectivity and Data-Driven Decision-Making for Smart Agriculture by Embracing 6G Technology
Y.V.R. Naga Pawan and Kolla Bhanu Prakash
10.1 Fundamental Concepts of Smart Agriculture
10.1.1 Smart Agriculture
10.2 Applications of 6G in SA
10.3 Empowerment of 6G in SA
10.4 Enhanced Monitoring and Predictive Analytics in SA
10.4.1 Predictive Analytics
10.5 Advantages of 6G in SA
10.6 Challenges in the Implementation of 6G in SA
References
11. Security and Cost Optimization in Laser-Based Fencing Solutions
Sanmukh Kaur and Anurupa Lubana
11.1 Introduction
11.2 Potential Security Challenges
11.2.1 Beam Spoofing
11.2.2 Beam Bending
11.3 Objectives of the Chapter
11.3.1 To Defend the Laser Fencing Against Potential Attacks
11.3.2 To Optimize the Cost of Manufacturing and Operating
11.4 Secure Communication Protocol
11.4.1 Node Setup
11.4.2 Protocol
11.4.2.1 Packet Structure
11.4.2.2 Fence State
11.4.2.3 Seed and Encryption
11.4.2.4 Timestamp Counter
11.4.2.5 Error Checking
11.5 Algorithm
11.6 Conclusion
References
12. Security and Privacy in 6G-Based Human–Computer Interfaces: Challenges and Opportunities
Kamaraj Arunachalam and Senthil Kumar Jagatheesaperumal
12.1 Introduction
12.2 Evolution of 6G Networks and HCIs
12.2.1 Connected Robotics and Autonomous Systems
12.2.2 Wireless Brain–Computer Interactions (BCIs)
12.2.3 Haptic Communication and Smart Healthcare
12.2.4 Automation and Industrial Ecosystem
12.2.5 Internet of Everything (IoE)
12.3 Risks and Vulnerabilities in 6G-Based HCIs
12.4 Solutions and Strategies for Ensuring Security and Privacy
12.4.1 Authentication Techniques in 6G HCIs
12.4.2 Encryption Algorithms and Protocols
12.4.3 Cybersecurity Measures for HCIs
12.4.4 Privacy-Enhancing Technologies
12.5 Future Trends and Opportunities for Enhancing Security and Privacy
12.5.1 Advancements in User Identification and Authentication
12.5.2 Secure Data Transmission and Storage
12.5.3 Incorporating Privacy by Design
12.5.4 Collaboration and Standardization Efforts
12.6 Conclusion
References
13. Security and Privacy in 6G Applications: Optimization and Realization of Stochastic-Based Rapid Random Number Generation
S. Nithya Devi, S. Senthil Kumar, V. K. Reshma and S. Shanmugaraju
13.1 Introduction
13.2 Literature Review
13.3 Problem with Sensor Data
13.4 Study Process
13.4.1 Conventional Digital Clock Manager Scheme
13.4.2 Stochastic Circuits
13.4.3 Rapid Generating of Random Numbers Using a Stochastic Model
13.4.4 Received Signal Strength Indicator (RSSI)
13.4.5 Setting Up the Experiment and Collecting Data
13.4.6 QCA Multiplexers and D-Latch
13.5 Results and Analysis
13.6 Conclusion
References
14. Roles and Challenges of 6G for the Human–Computer Interface
Priyabrata Dash, Akankshya Patnaik, Sarat Kumar Sahoo and Franco Fernando Yanine
14.1 Introduction
14.2 Sixth Generation
14.3 Roles of 6G for the Human–Computer Interface
14.4 Challenges of 6G for the Human–Computer Interface
14.5 Uses of 6G in Different Sectors
14.6 Impact of 6G in Organizations
14.7 Conclusion
References
15. Leveraging 6G Technology for Advancements in Smart Agriculture: Opportunities and Challenges
B. Sathyasri, R.S. Valarmathi and G. Aloy Anuja Mary
15.1 Introduction
15.2 Literature Review
15.3 Methodology
15.3.1 Benefits of 6G in Smart Agriculture
15.3.2 Increased Precision and Accuracy in Farming Practices
15.3.3 Real-Time Monitoring and Data Collection
15.3.4 Improved Communication and Collaboration Among Farmers
15.3.5 Efficient Allocation of Resources
15.3.6 Enhanced Crop Yields and Quality
15.4 Challenges to Implementing 6G in Smart Agriculture
15.4.1 High Cost of Technology
15.4.2 Limited Network Coverage in Rural Areas
15.4.5 Concerns over Data Security and Privacy
15.4.6 Need for Technical Expertise to Operate and Maintain Technology
15.5 Potential Applications of 6G in Smart Agriculture
15.5.1 Crop Monitoring and Management
15.5.2 Livestock Monitoring and Disease Control
15.5.3 Smart Irrigation Systems
15.5.4 Automated Machinery and Equipment
15.5.5 Supply Chain Management
15.6 Expected Outcomes
15.7 Example of a Farm or Company That Has Successfully Adopted 6G Technology
15.8 Benefits Experienced and Impact on Agricultural Productivity
15.8.1 Lessons Learned and Recommendations for Others
15.9 Conclusion
References
16. Exploring 6G Research: Advancements, Applications, and Challenges
S. Senthil Kumar, S. Balaji, S. Nithya Devi and V. Priyadharsini
16.1 Introduction
16.2 Our Contributions and Comparable Work
16.2.1 Previous Studies
16.2.2 Contributions
16.3 Credibility
16.3.1 Reliability
16.3.2 Security and Safety
16.3.3 Dependability in 6G Networks
16.4 Reliability, ML, and 6G
16.4.1 Background in Brief
16.4.2 Dependability of Federated Learning
16.4.2.1 Reliability
16.4.2.2 Availability
16.4.2.3 Safety
16.5 Dependability for Mission-Critical Applications
16.5.1 Dependability Analysis of 6G MCAs
16.5.2 Availability
16.6 Future Research Directions
16.7 Conclusions
References
17. E-Travel ID-Based Bus Fare Collection System Using 6G Networks
S. A. Sivakumar, Pavithra K., Pavatharani P., Naviyarasu G. and Sajetha M.
17.1 Insights into 6G Networks
17.2 Impact of 6G on Transportation Sector
17.3 Existing Approach and Problem Identification
17.4 E-Travel ID-Based Bus Fare Collection System Using 6G Networks
17.5 Results and Discussion
17.6 Conclusion
References
18. Alert Generation Tool for Messaging Systems
Akshaya K. and Sanmukh Kaur
18.1 Introduction
18.2 Importance of Alerts in the Messaging System
18.2.1 System Health Monitoring
18.2.2 Proactive Issue Resolution
18.2.3 Performance Optimization
18.2.4 Capacity Planning
18.2.5 Security and Compliance
18.3 Monitoring CPU Usage in Real Time
18.3.1 Importance of CPU Usage Monitoring
18.3.1.1 Identifying Performance Bottlenecks
18.3.1.2 Diagnosing Performance Issues
18.3.1.3 Optimizing Resource Allocation
18.3.1.4 Proactive Issue Detection
18.3.1.5 Capacity Planning and Scaling
18.3.1.6 Resource Efficiency and Cost Optimization
18.3.2 Methodology
18.3.2.1 Importing the Necessary Libraries
18.3.2.2 User Input for Process ID
18.3.2.3 Defining the “warning()” Function
18.3.2.4 Defining the “monitor()” Function
18.3.2.5 Scheduling the Monitoring Tasks
18.3.2.6 Running the Monitoring Loop
18.3.2.7 Python Code
18.3.3 Output
18.3.4 Benefits of Real-Time CPU Usage Monitoring
18.4 URL Tracking
18.4.1 Methodology
18.4.1.1 Python Code
18.4.1.2 Output
18.4.1.3 Python Code
18.4.2 Output
18.5 Automated Delivery Performance Monitoring
18.5.1 Methodology
18.5.1.1 Code
18.5.2 Output
18.5.3 Applications
18.5.3.1 Marketing Campaigns
18.5.3.2 Transactional Notifications
18.5.3.3 Customer Support Systems
18.5.3.4 System Alerts
18.5.3.5 Performance Evaluation
18.6 High Volume of Testing Message Alert
18.6.1 Methodology
18.6.1.1 Import Necessary Libraries
18.6.1.2 Set Up Twilio and Email Credentials
18.6.1.3 Establish a Connection to MySQL Database
18.6.1.4 Create a Cursor Object and Execute a Query
18.6.1.5 Fetch Data and Create a Pandas DataFrame
18.6.1.6 Export Data to Excel
18.6.1.7 Count the Number of Testing Messages
18.6.1.8 Close the Cursor and Connection
18.6.1.9 Print Status Messages
18.6.1.10 Send SMS and Email Notifications
18.6.1.11 Python Code
18.6.2 Output
18.7 Conclusion
References
19. Design of an Underwater Robotic Fish Controlled through a Mobile Phone
Mohammed Nisam, N. Mouli Sharm, Vajid N. O., Sobhit Saxena and Suman Lata Tripathi
19.1 Introduction
19.1.1 Block diagram
19.1.2 Flowchart and Explanation:
19.2 Module Code Description
19.3 Description of Proposed Robotic Fish
19.4 Component and Material Selection
19.5 Conclusion
19.6 Suggestion for Future Work
References
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