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Submit a ProposalReshaping Intelligent Business and Industry
 | Edited by Surjeet Dalal, Neeraj Dahiya, Vivek Jaglan Deepika Koundal and Dac-Nhuong Le
Copyright: 2024 | Status: Published ISBN: 9781119904878 | Hardcover | 638 pages Price: $225 USD  |
One Line Description
The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) is reshaping the way industries, businesses, and economies function; the 34 chapters in this collection show how the full potential of these technologies is being enabled to create intelligent machines that simulate smart behavior and support decision-making with little or no human interference, thereby providing startling organizational efficiencies.
Audience
This book will appeal to all business and organization leaders, entrepreneurs, policymakers, and economists, as well as scientists, engineers, and students working in artificial intelligence, software engineering, and information technology.
This book will appeal to all business and organization leaders, entrepreneurs, policymakers, and economists, as well as scientists, engineers, and students working in artificial intelligence, software engineering, and information technology.
Description
Readers will discover that in Reshaping Intelligent Business and Industry:
•The book unpacks the two superpowers of innovation, AI and IoT, and explains how they connect to better communicate and exchange information about online activities;
•How the center and the network’s edge generate predictive analytics or anomaly alerts;
•The meaning of AI at the edge and IoT networks.
•How bandwidth is reduced and privacy and security are enhanced;
•How AI applications increase operating efficiency, spawn new products and services, and enhance risk management;
•How AI and IoT create ‘intelligent’ devices and how new AI technology enables IoT to reach its full potential;
•Analyzes AIOT platforms and the handling of personal information for shared frameworks that remain sensitive to customers’ privacy while effectively utilizing data.
Back to Top
Readers will discover that in Reshaping Intelligent Business and Industry:
•The book unpacks the two superpowers of innovation, AI and IoT, and explains how they connect to better communicate and exchange information about online activities;
•How the center and the network’s edge generate predictive analytics or anomaly alerts;
•The meaning of AI at the edge and IoT networks.
•How bandwidth is reduced and privacy and security are enhanced;
•How AI applications increase operating efficiency, spawn new products and services, and enhance risk management;
•How AI and IoT create ‘intelligent’ devices and how new AI technology enables IoT to reach its full potential;
•Analyzes AIOT platforms and the handling of personal information for shared frameworks that remain sensitive to customers’ privacy while effectively utilizing data.
Back to Top
Author / Editor Details
Surjeet Dalal, PhD, is an associate professor in the Department of Computer Science & Engineering at SRM University, Haryana, India. His current research areas are artificial intelligence, cloud computing, and IoT. He has published two cloud computing books and published 20+ papers in Scopus-indexed journals.
Surjeet Dalal, PhD, is an associate professor in the Department of Computer Science & Engineering at SRM University, Haryana, India. His current research areas are artificial intelligence, cloud computing, and IoT. He has published two cloud computing books and published 20+ papers in Scopus-indexed journals.
Neeraj Dahiya, PhD, is an assistant professor in the Department of Computer Science & Engineering, SRM University, Haryana, India. His research areas include artificial intelligence, machine learning, speech processing, etc. Along with publishing research papers, Dahiya has four patents in artificial intelligence and machine learning.
Deepika Koundal, PhD, is an assistant professor at the University of Petroleum and Energy Studies, Dehradun, India. Her areas of interest are artificial intelligence, biomedical imaging and signals, image processing, etc. She has published two books and 40+ research papers in SCI and Scopus-indexed journals.
Dac-Nhuong Le, PhD, obtained his doctorate in computer science from Vietnam National University, Vietnam in 2015. He is deputy head of the Faculty of Information Technology, Haiphong University, Vietnam. His area of research includes evaluation computing and approximate algorithms, network communication, security and vulnerability, network performance analysis and simulation, cloud computing, IoT, and image processing in biomedicine. He has over 50 publications and edited/authored many computer science books with the Wiley-Scrivener imprint.
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Table of Contents
List of Figures
List of Tables
Foreword
Preface
Acknowledgments
Acronyms
PART I: ARTIFICIAL INTELLIGENCE APPLICATIONS
1. Artificial Intelligence Overview: Architecture, Applications and Challenges
Geet Kiran Kaur, Nandita Malik, Sharad Chauhan, Mankiran Kaur
1.1 Introduction
1.1.1 History of Artificial Intelligence
1.1.2 Components of Artificial Intelligence
1.1.3 Levels of Artificial Intelligence
1.2 Artificial Intelligence Agents
1.3 Artificial Intelligence Algorithms
1.4 Applications of Artificial Intelligence
1.4.1 Agriculture
1.4.2 Healthcare
1.4.3 Education
1.4.4 Banking
1.4.5 Opportunities and Challenges
1.5 Conclusion
References
2. Video Analytics Using Deep Learning Models
Sanjeev Kumar Bhatt, S. Srinivasan
2.1 Introduction
2.1.1 Artificial Intelligence
2.1.2 Deep Learning Overview and Evolution
2.1.3 Edge Computing
2.1.4 Cloud Computing
2.2 Video Analytics
2.2.1 Video Surveillance
2.2.2 Real-Time Video Mining and Video Monitoring
2.2.3 Video Analytics Functional Model
2.3 Object Detection and Object Tracking
2.3.1 Object Detection
2.3.2 Object Tracking
2.3.3 Role of Deep Learning in Object Tracking
2.3.4 Functional Model
2.4 Industrial Application
2.4.1 Healthcare
2.4.2 Smart City
2.4.3 Connected Home
2.4.4 Security
2.4.5 Sports
2.5 Conclusion
References
3. Optimizing Search Engine for Enhancing Computing and Communication in Real-Time Systems
Meeta Singh, Poonam Nandal, Deepa Bura
3.1 Introduction
3.1.1 Existing System
3.1.2 Problem Definition
3.1.3 Feasibility Study
3.1.4 Experimental Work
3.2 Literature Review
3.3 Requirement Specification
3.3.1 Search Engine Optimization
3.3.2 Web Analytics
3.3.3 Web Architecture Diagram
3.4 Testing and Validation
3.4.1 SEO Testing
3.4.2 Google Analytics Testing
3.4.3 Structured Data Testing
3.4.4 SEO Implementation
3.4.5 Google Analytics Implementation
3.4.6 Google Data Studio Implementation
3.4.7 Tableau Implementation
3.5 Result
3.6 Conclusion and Future Work
References
4. The Need for XAI: Challenges and Its Applications
Swati, Menu Vijarania, Vivek Jaglan, Dac-Nhuong Le
4.1 Introduction
4.2 Literature Review
4.3 The Need for Exploring XAI
4.3.1 Explain to Justify
4.3.2 Explain to Control
4.3.3 Explain to Improve
4.3.4 Explain to Discover
4.3.5 Challenges in XAI
4.4 Scope of Explanation
4.4.1 Local Explanation
4.4.2 Global Explanations
4.5 Differences in Research Methodology
4.5.1 Perturbation Based
4.5.2 Backpropagation- or Gradient-Based
4.5.3 XAI Applications
4.6 Conclusion
References
5. Why Law Firms Need to Embrace Artificial Intelligence to Transform the Indian Legal Industry
B. Dharneesh, S. Thenisha, S. S. Srithick, A. Abirami
5.1 Introduction
5.1.1 Research Methodology
5.1.2 Research Objectives
5.2 What Is Artificial Intelligence?
5.2.1 Modernization and Delivery of Legal Services
5.2.2 AI Technology for Use in Law Firms
5.3 The Law and Policy Relating to AI in India
5.3.1 Threats that AI Could Pose in India
5.3.2 National Data Protection Law
5.3.3 Discrimination Law
5.3.4 Competition Law
5.3.5 Consumer Protection Law
5.4 The Morality Debate: The Ethicality of AI in Law
5.4.1 Equal Treatment and Non-Discrimination
5.4.2 Transparency
5.4.3 Accountability in Decision-Making
5.5 Conclusion
References
6. A Comparative Study of Supervised and Unsupervised Machine Learning
Algorithms for Predictive Analytics
V. Belsini Gladshiya, Sharmila
6.1 Introduction
6.2 Predictive Analytics
6.3 Machine Learning
6.3.1 Supervised Machine Learning
6.3.2 Unsupervised Learning
6.3.3 Supervised vs. Unsupervised Machine Learning Algorithms
6.4 Applications of Supervised and Unsupervised Learning
6.5 Conclusion
References
7. Machine Learning Approach for Predicting the Price of Used Cars
Swati, Meenu Vijarania, Akshat Agarwal, Dac-Nhuong Le
7.1 Introduction
7.2 Related Work
7.3 Research Methodology
7.3.1 Dataset Collection
7.3.2 Data Preprocessing
7.3.3 Data Analysis
7.4 Model Description
7.5 Conclusion
References
PART II: INTERNET OF THINGS APPLICATIONS
8. Recent Industry-Defined and Domain-Specific IoT Architectures
Sharad Chauhan, Ritika Arora, Geetkiran Kaur
8.1 Introduction
8.2 Literature Review
8.3 Benefits and Major Components of IoT
8.3.1 Benefits of IoT
8.3.2 Major Components of IoT
8.4 IoT Implementation and Building Blocks
8.4.1 Various Requirements for IoT Implementation
8.4.2 IoT Building Blocks
8.5 IoT Architecture
8.5.1 Layered Architectures
8.5.2 Domain-Specific IoT Architecture
8.5.3 Industry-Defined Architectures
8.6 Conclusion
References
9. IoT Devices
Mukesh Choubisa
9.1 Introduction
9.1.1 What is IoT?
9.1.2 History of IoT
9.1.3 Realizing the Concept
9.1.4 IoT Takes Off
9.1.5 Future of Internet of Things
9.2 Application of IoT
9.2.1 Transportation
9.2.2 Environmental Monitoring
9.2.3 Infrastructure Management
9.2.4 Medical and Healthcare Management
9.2.5 Home Automation
9.2.6 Energy Management
9.2.7 Agriculture
9.2.8 Water Supply
9.3 IoT Devices
9.3.1 Introduction of IoT Devices
9.3.2 Devices
9.4 Conclusion
References
10. IoT Securities: Applications, Security Issues and Solutions Using Diverse
Technologies
Lovanya Bajaj, Nikhil Sharma, Prashant Giridhar Shambharkar
10.1 Introduction
10.2 Related Work
10.3 Overview of Internet of Things (IoT)
10.3.1 Application Areas in IoT Demanding Crucial Security
10.3.2 Security Attacks in Internet of Things
10.4 Security Issues Addressed Using Diverse Technologies
10.4.1 Security Issues Addressed Using Machine Learning
10.4.2 Security Issues Addressed Using Artificial Intelligence
10.4.3 Security Issues Addressed Using Blockchain Technology
10.5 Open Challenges and Future Research Directions
10.5.1 Resource Limitations
10.5.2 Heterogeneous Devices
10.5.3 Interoperability of Security Protocols
10.5.4 Single Points of Failure
10.5.5 Hardware/Firmware Vulnerabilities
10.5.6 Trusted Updates and Management
10.5.7 Blockchain Vulnerabilities
10.6 Conclusion
References
11. FAMoS: Smart Farm Automatic Monitoring System
J. Dakshana, P. Balasubramaniam, A. Abirami, S. TamilSelvan
11.1 Introduction
11.2 Related Work
11.3 Methodologies Proposed
11.4 Software Elements
11.4.1 Machine Learning Algorithms with Dataset Prediction
11.4.2 Design
11.4.3 Features and Technologies
11.5 Project Cost Estimation
11.5.1 Unique Selling Point
11.5.2 SWOT
11.5.3 STP
11.6 Conclusion
References
12. IoT-Based Module to Control Electronic Devices Through Wi-Fi and Bluetooth
Ritu Shrivastava, Amit Shrivastava, Kapil Chaturvedi
12.1 Introduction
12.2 Literature Review
12.3 Proposed Wi-Fi Communication Module
12.3.1 Remotely Access IoT-Based Smart Home Appliances via Wi-Fi
12.3.2 Remotely Access IoT-Based Smart Home Appliances via Bluetooth
12.4 IR (Infrared) Remote and Arduino Nano
12.5 Conclusion
References
13. An Insight into the IoT Building Blocks: Architecture, Framework, Principles,
Applications and Challenges
Priyanka, Anoop Kumar, Keziah Nagaraj
13.1 Introduction
13.2 Related Work
13.3 Traditional and New Architecture of IoT
13.3.1 Traditional Architecture of IoT
13.3.2 New Architecture of IoT
13.4 Design Principles and Decision Framework of IoT
13.4.1 Design Principles of IoT
13.4.2 Decision Framework
13.5 Applications and Challenges
13.5.1 Applications
13.5.2 Challenges
13.6 Conclusion
References
14. Interoperability: A Conceptual Framework
Soni Chaurasia, Kamal Kumar
14.1 Introduction
14.2 Inclusions in IoT Network
14.3 IoT Interoperability Protocols
14.3.1 IoT Data Protocols
14.3.2 IoT Network Protocols
14.4 Interoperability Conceptual Framework Proposed
14.4.1 IoT Deployment Architecture
14.4.2 Understanding Interoperability Model
14.4.3 Novel IoT Interoperability Scheme
14.5 Conclusion
References
15. Securing IoT Devices Against MITM and DoS Attacks: An Analysis
Vicky Tyagi, Amar Saraswat, Ashwani Kumar, Shalini Gambhir
15.1 Introduction
15.2 Architecture of IoT
15.3 Attacks on IoT
15.3.1 MITM Attacks in IoT
15.3.2 DoS Attacks in IoT
15.3.3 MITM Targets an IoT Network
15.3.4 DoS Targets an IoT Network
15.4 Some Possible Solutions to Avoid/Prevent Cyberattacks
15.5 Conclusions
References
PART III: ARTIFICIAL INTELLIGENCE OF THINGS: SMART CITY AND SOCIAL APPLICATIONS
16. AIoT-Based Smart Cities
Shelly Garg, Namitan
16.1 What Are Smart Cities?
16.2 Internet of Things
16.2.1 Applications of IoT
16.2.2 Why is IoT on the Rise Today?
16.3 Introduction to Artificial Intelligence
16.3.1 Types of Artificial Intelligence
16.3.2 Why is AI Harmful?
16.3.3 Why is AI Useful?
16.3.4 Pros and Cons of Artificial Intelligence
16.4 AIoT in Smart Cities
16.5 Conclusion
References
17. Integrating Artificial Intelligence and IoT for Smart Cities: Applications and Challenges
Varsha Bhatia, Vivek Jaglan
17.1 Introduction
17.2 Overview of Smart Cities
17.3 AIoT in Smart Cities
17.3.1 Smart Healthcare
17.3.2 Smart Education
17.3.3 Smart Energy
17.3.4 Smart Homes
17.3.5 Smart Agriculture
17.3.6 Smart Transport
17.3.7 Smart City Services
17.4 Open Issues and Challenges
17.5 Conclusion
References
18. A Comprehensive Review of the Convergence of Blockchain, AI and IoT for Improving Social Interactions
Priya Sachdeva
18.1 Introduction
18.1.1 Background
18.1.2 Literature Reviews
18.1.3 Research Gap
18.1.4 Research Question
18.1.5 Importance of the Study
18.1.6 Research Objectives
18.2 Research Methodology
18.2.1 Research Method and Design
18.2.2 Research Approach
18.2.3 Analysis of Study
18.2.4 Convergence of Blockchain, AI as well as IoT
18.2.5 Standardization of Data
18.2.6 Enables Data Privacy and Security
18.2.7 Better Scalability
18.2.8 Authentication Through Blockchain-Based Identity
18.2.9 Automatization Through Blockchain
18.2.10 Monetization of IoT Devices
18.3 The Risks and Challenges of Convergence in the Making of Smart Cities
18.3.1 Use of Convergence in the Making of Smart Cities
18.3.2 Convergence Helps in Managing Infrastructure of Smart Cities
18.3.3 Risks and Challenges
18.4 Results and Findings
18.5 Future Research Directions for the Convergence of Blockchain, AI and IoT
18.6 Conclusion
References
19. AIoT-Based Smart Bin for Real-Time Monitoring and Management of Solid Waste
Ritik Agarwal
19.1 Introduction
19.2 Literature Review
19.2.1 SWMS
19.2.2 GMS
19.2.3 IoT-Based SWM
19.2.4 SWM by K-Query Scheduling
19.2.5 SWC as a Service
19.3 Proposed Methodology
19.3.1 System Design
19.3.2 Transmission Pattern of the Proposed System
19.3.3 Processing of the Trash Bin
19.4 Conclusion
19.5 Challenges and Future Work
References
20. AIoT in the Education Sector
Zhumaniyaz Mamatnabiyev, Meirambek Zhaparov
20.1 Introduction
20.2 AIoT Applications in the Education Sector
20.3 IoT Development Stages
20.3.1 IoT Primitives and Topics
20.3.2 IoT Roadmap
20.4 Conclusion
References
21. Artificial Intelligence of Things (AIoT)-Enabled Personalized Banking:
Investigating Intention to Adopt
Ashok Singh Malhi, Raj K Kovid, Thipendra P Singh
21.1 Introduction
21.2 Literature Review and Hypotheses
21.2.1 Technology Acceptance Model (TAM)
21.3 Methodology
21.3.1 Sample and Data Collection Procedure
21.3.2 Variable Measures and Method of Analysis
21.4 Results and Discussion
21.4.1 Assessment of Measurement Model
21.4.2 Managerial Implications
21.4.3 Limitations and Scope for Further Research
21.5 Conclusion
References
PART IV: ARTIFICIAL INTELLIGENCE OF THINGS: APPLICATIONS IN HEALTHCARE
22. AI- and IoT-Enabled Healthcare Applications: A Review
N. Krishna Chaitanya, Mangesh M. Ghonge, G. Vimala Kumari, S.Leela Lakshmi
22.1 Introduction
22.2 Literature Review
22.3 IoT in Healthcare
22.3.1 IoT Applications in Healthcare
22.3.2 Blood Glucose Monitoring
22.3.3 Blood Pressure Monitoring
22.3.4 Heart Rate Monitoring
22.3.5 Temperature Monitoring
22.3.6 Depression Monitoring
22.3.7 Asthma Inhalers
22.3.8 Smart Contact Lenses
22.3.9 Robotic Surgery
22.4 Role of Artificial Intelligence in Healthcare
22.5 Challenges of IoT in Healthcare
22.6 Conclusion
References
23. An Extensive Survey of AIoT in Healthcare: Applications, Challenges and Future Opportunities
Gunjan, Ishwari Singh Rajput, Aditya Gupta, Soni Chaurasia
23.1 Introduction
23.2 Background
23.2.1 Artificial Intelligence (AI)
23.2.2 Internet of Things (IoT)
23.2.3 Integration of AI and IoT
23.2.4 Architecture of AIoT
23.2.5 Application of AIoT
23.3 AIoT in Healthcare
23.4 AIoT Challenges and Opportunities
23.4.1 Challenges
23.4.2 Opportunities
23.5 Conclusion
References
24. AI- and IoT-Based Face Recognition Model for Identification of Human Diseases
Pushan Kumar Dutta, Susanta Mitra
24.1 Introduction
24.2 Problem Identification
24.2.1 Morphology of the Craniofacial Complex
24.2.2 Solution
24.3 Proposed Methodology
24.3.1 Software Development
24.3.2 Dataset Creation and Its Extraction
24.3.3 Training the System or Model
24.3.4 Recognizing Faces in the Video Stream
24.4 Findings and Discussion
24.5 Conclusion
References
25. IoT in the Medical Field
S. Sasikala, K. Sharmila
25.1 Introduction
25.1.1 Classification of IoT
25.1.2 Nine Healthcare Monitoring Devices
25.2 Security Matters for IoT in Healthcare
25.3 Cholesterol Control Levels
25.3.1 Symptoms of High Cholesterol
25.3.2 Diagnosis of High Cholesterol Levels
25.3.3 Risks for High Cholesterol
25.3.4 Complications from High Cholesterol
25.3.5 Treatment and Prevention
25.3.6 Screening for Stroke and Cardiovascular Disease Risk
25.4 IoR Device for Cholesterol Test: CURO L7
25.5 Challenges, Limitations, and Future Scope
25.6 Conclusion
References
26. Using Deep Learning to Characterize Persistent Physiological Parameters in Patient Monitoring Systems
Dhyanendra Jain, Anjani Gupta, Amit Kumar Pandey, Prashant Vats
26.1 Introduction
26.2 Using Deep Learning and AI for Surveillance
26.3 Other Ways that Do Not Rely on Machine Learning Knowledge
26.4 The Present Position of Learning Algorithms and Patient Monitoring
26.4.1 Detection of Infections and Symptomatic Worsening
26.4.2 Lowering False Alarm Rate
26.4.3 Identifying Dementia
26.4.4 Detecting Respiratory Perceptions Using Waveforms Monitoring
26.4.5 Managing of Anesthesia in the Intensive Care Unit
26.4.6 Identifying Unreported Atrial Fibrillation and Another Arrhythmia
26.5 Possible Uses for Machine Learning Surveillance
26.5.1 Assisting with Monitoring Inferential Process
26.5.2 Synth Sounds, Motifs, Motion, and Sophistication
26.5.3 Incorporation of Clinical Information for a Patient
26.6 Concerns and Future Objectives
26.7 Conclusions
References
PART V: ARTIFICIAL INTELLIGENCE OF THINGS: APPLICATIONS IN AGRICULTURE AND INDUSTRIES
27. Smart Agriculture System Using Artificial Intelligence and Internet of Things
Meenakshi Yadav, Preety, Esha Saxena, Akhilesh Das
27.1 Introduction
27.2 Artificial Intelligence and IoT in Agriculture
27.2.1 Role of AI in Agriculture
27.2.2 Role of IoT in Agriculture
27.3 Components of AI and IoT for Agriculture
27.4 Application of IoT and AI in Agricultural Automation
27.5 Challenges and Opportunities
27.6 Conclusion and Future Trends
References
28. Application of AI and IoT in Agriculture
Rashmi Singh
28.1 Agricultural Process
28.2 The Use of Artificial Intelligence in Agricultural Applications
28.3 Predictive Analytics and Precision Agriculture
28.3.1 Meam Squared Error
28.3.2 Crop Yield Prediction Using Machine Learning for Accurate Soil Nutrient Measurement via Sensor Network
28.3.3 Agriculture Analysis in Data Mining High-Tech Farming of the Future
28.3.4 Crop Yield Prediction Using Hadoop’s Agro Algorithm
28.3.5 Crop Yield Prediction
28.3.6 Data from Soil Testing for Cultivation Advisory Report
28.4 Agricultural Robotics (Agrobots)
28.5 AI-Enabled System for Pest Control and Disease Diagnosis
28.6 Adopting AI: A Challenge for Farmers
28.7 Conclusion
References
29. Traffic Management System Using AIoT
Pallavi Choudekar, Rashmi Singh
29.1 Introduction
29.1.1 Artificial Intelligence
29.1.2 AI and Transport
29.1.3 IoT in Transportation Industry
29.2 Smart Road Traffic Management System (SRTMS)
29.3 Smart Traffic Information System (TIS)
29.3.1 How TIS Works
29.3.2 Collection of Data
29.3.3 Reconstitution and Translation of Data
29.3.4 Dissemination of Information
29.3.5 Safety Management and Emergency Systems
29.3.6 Emergency Management and AI
29.3.7 Traffic Management Plan for Urban Areas
29.3.8 Real-Time Traffic Analysis
29.3.9 Predictive Traffic Control
29.3.10 Diverse Lanes for Different Vehicles
29.4 Smart Parking Management System
29.4.1 Smart Parking System
29.4.2 How Does Intelligent Parking Work?
29.5 Smart Pavement Management System
29.5.1 Basics of Smart Pavement Management System
29.5.2 PMS Framework Implementation
29.6 Conclusion
References
30. Autonomous Vehicles: A Convergence Application of AI and IoT
Neha Gehlot, Amritpal Kuar
30.1 Introduction
30.1.1 How Self-Driving Cars (SDCs) Work
30.1.2 SDC Technologies
30.1.3 Levels of Automation
30.2 Technical Challenges in Self-Driving Cars
30.2.1 Latency
30.2.2 Verification and Testing
30.2.3 Reliability and Safeness of SDCs
30.2.4 Software Quality
30.2.5 Computing Resources
30.2.6 Security and Hacking Threats
30.2.7 Data Confidentiality
30.2.8 Accuracy of Environmental Sensing
30.2.9 Managing the Sensor Data
30.2.10 Uncertainty of SDC Decisions
30.2.11 Actuation Process/Task
30.3 Other Very Critical Areas
30.3.1 Unpredictable Road Conditions
30.3.2 Imperfect Traffic Conditions
30.3.3 Wave Interference
30.3.4 Human-Centric
30.3.5 Software System Bugs and Intrusion Attacks
30.3.6 Need for Huge Classifications
30.3.7 Problems Due to Co-existence of Human-Driven and Self-Driving Cars
30.3.8 Flocking Problem
30.3.9 Sharing Cost Challenge
30.4 Blind Spots in Autonomous Vehicles
30.5 Potential Cyberattacks on Automated Vehicles
30.5.1 High Level Threats
30.5.2 Medium Level Threats
30.5.3 Verdict on Cyber Security of SDCs
30.6 Conclusion
References
31. Convergence of Artificial Intelligence and Internet of Things for Software-Defined Radios
Shilpa Mehta, Xue Jun Li, Surjeet Dalal
31.1 Introduction
31.2 Review of SDR Receiver Architectures
31.2.1 Superheterodyne Receivers
31.2.2 Dual Conversion Superheterodyne Receivers
31.2.3 Image Rejection Receivers
31.2.4 Direct Conversion Receivers
31.2.5 Low-IF Receivers
31.3 Integration of SDR and IoT
31.3.1 IoT
31.3.2 IoT Technologies: Literature Review
31.4 Artificial Intelligence
31.4.1 Historical Background
31.4.2 Optimization
31.4.3 Multi-Objective Optimization
31.5 Proposed AI-Based Receiver Architecture
31.5.1 Low Noise Amplifier
31.5.2 Mixer
31.5.3 IF Amplifiers
31.5.4 Polyphase Filter
31.6 AI Optimization
31.7 Results and Discussion
31.8 Conclusion
References
32. Artificial Intelligence of Things (AIoT) for Intelligent Data Design
Parul Gandhi, Raj Kumar
32.1 Introduction
32.1.1 How Does AIoT Work?
32.1.2 AIoT Architecture
32.1.3 AIoT Applications
32.2 AI, IoT, and Big Data Analytics
32.3 AIoT-Based Data Analytics and Decision-Making
32.3.1 Public Data
32.3.2 Business Data
32.3.3 Government Data
32.4 Challenges
32.5 Role of AIoT in the Context of COVID-19
32.6 Future Direction and Conclusion
References
33. A Study of Implementation of Blockchain Technology in Land Registration:
A SWOT Analysis
Ankita Goyal, Upendra Singh
33.1 Introduction
33.2 Literature Review
33.3 Research Methodology
33.4 Discussion, Analysis, Limitation and Future Work
33.4.1 Discussion
33.4.2 Analysis
33.4.3 Limitation and Future Work
33.5 Conclusion
References
34. Smart Mask Disinfection System (SMDS)
N. Swetha Sridevi, N. C. Preethika, T. Subiksha
34.1 Introduction
34.1.1 Topic Introduction
34.1.2 Motivation
34.1.3 Task Undertaken
34.1.4 Objective
34.1.5 Scope of the Work
34.1.6 Intended Approach
34.2 Related Work
34.3 Smart Mask Disinfection System
34.3.1 Problem Statement
34.3.2 Materials and Methods
34.3.3 Design
34.3.4 Experimental Setup
34.3.5 Modeling
34.3.6 Methodology
34.3.7 Operational Algorithm
34.4 Results and Discussion
34.5 Conclusion
References
Editors
Index
Back to Top
List of Figures
List of Tables
Foreword
Preface
Acknowledgments
Acronyms
PART I: ARTIFICIAL INTELLIGENCE APPLICATIONS
1. Artificial Intelligence Overview: Architecture, Applications and Challenges
Geet Kiran Kaur, Nandita Malik, Sharad Chauhan, Mankiran Kaur
1.1 Introduction
1.1.1 History of Artificial Intelligence
1.1.2 Components of Artificial Intelligence
1.1.3 Levels of Artificial Intelligence
1.2 Artificial Intelligence Agents
1.3 Artificial Intelligence Algorithms
1.4 Applications of Artificial Intelligence
1.4.1 Agriculture
1.4.2 Healthcare
1.4.3 Education
1.4.4 Banking
1.4.5 Opportunities and Challenges
1.5 Conclusion
References
2. Video Analytics Using Deep Learning Models
Sanjeev Kumar Bhatt, S. Srinivasan
2.1 Introduction
2.1.1 Artificial Intelligence
2.1.2 Deep Learning Overview and Evolution
2.1.3 Edge Computing
2.1.4 Cloud Computing
2.2 Video Analytics
2.2.1 Video Surveillance
2.2.2 Real-Time Video Mining and Video Monitoring
2.2.3 Video Analytics Functional Model
2.3 Object Detection and Object Tracking
2.3.1 Object Detection
2.3.2 Object Tracking
2.3.3 Role of Deep Learning in Object Tracking
2.3.4 Functional Model
2.4 Industrial Application
2.4.1 Healthcare
2.4.2 Smart City
2.4.3 Connected Home
2.4.4 Security
2.4.5 Sports
2.5 Conclusion
References
3. Optimizing Search Engine for Enhancing Computing and Communication in Real-Time Systems
Meeta Singh, Poonam Nandal, Deepa Bura
3.1 Introduction
3.1.1 Existing System
3.1.2 Problem Definition
3.1.3 Feasibility Study
3.1.4 Experimental Work
3.2 Literature Review
3.3 Requirement Specification
3.3.1 Search Engine Optimization
3.3.2 Web Analytics
3.3.3 Web Architecture Diagram
3.4 Testing and Validation
3.4.1 SEO Testing
3.4.2 Google Analytics Testing
3.4.3 Structured Data Testing
3.4.4 SEO Implementation
3.4.5 Google Analytics Implementation
3.4.6 Google Data Studio Implementation
3.4.7 Tableau Implementation
3.5 Result
3.6 Conclusion and Future Work
References
4. The Need for XAI: Challenges and Its Applications
Swati, Menu Vijarania, Vivek Jaglan, Dac-Nhuong Le
4.1 Introduction
4.2 Literature Review
4.3 The Need for Exploring XAI
4.3.1 Explain to Justify
4.3.2 Explain to Control
4.3.3 Explain to Improve
4.3.4 Explain to Discover
4.3.5 Challenges in XAI
4.4 Scope of Explanation
4.4.1 Local Explanation
4.4.2 Global Explanations
4.5 Differences in Research Methodology
4.5.1 Perturbation Based
4.5.2 Backpropagation- or Gradient-Based
4.5.3 XAI Applications
4.6 Conclusion
References
5. Why Law Firms Need to Embrace Artificial Intelligence to Transform the Indian Legal Industry
B. Dharneesh, S. Thenisha, S. S. Srithick, A. Abirami
5.1 Introduction
5.1.1 Research Methodology
5.1.2 Research Objectives
5.2 What Is Artificial Intelligence?
5.2.1 Modernization and Delivery of Legal Services
5.2.2 AI Technology for Use in Law Firms
5.3 The Law and Policy Relating to AI in India
5.3.1 Threats that AI Could Pose in India
5.3.2 National Data Protection Law
5.3.3 Discrimination Law
5.3.4 Competition Law
5.3.5 Consumer Protection Law
5.4 The Morality Debate: The Ethicality of AI in Law
5.4.1 Equal Treatment and Non-Discrimination
5.4.2 Transparency
5.4.3 Accountability in Decision-Making
5.5 Conclusion
References
6. A Comparative Study of Supervised and Unsupervised Machine Learning
Algorithms for Predictive Analytics
V. Belsini Gladshiya, Sharmila
6.1 Introduction
6.2 Predictive Analytics
6.3 Machine Learning
6.3.1 Supervised Machine Learning
6.3.2 Unsupervised Learning
6.3.3 Supervised vs. Unsupervised Machine Learning Algorithms
6.4 Applications of Supervised and Unsupervised Learning
6.5 Conclusion
References
7. Machine Learning Approach for Predicting the Price of Used Cars
Swati, Meenu Vijarania, Akshat Agarwal, Dac-Nhuong Le
7.1 Introduction
7.2 Related Work
7.3 Research Methodology
7.3.1 Dataset Collection
7.3.2 Data Preprocessing
7.3.3 Data Analysis
7.4 Model Description
7.5 Conclusion
References
PART II: INTERNET OF THINGS APPLICATIONS
8. Recent Industry-Defined and Domain-Specific IoT Architectures
Sharad Chauhan, Ritika Arora, Geetkiran Kaur
8.1 Introduction
8.2 Literature Review
8.3 Benefits and Major Components of IoT
8.3.1 Benefits of IoT
8.3.2 Major Components of IoT
8.4 IoT Implementation and Building Blocks
8.4.1 Various Requirements for IoT Implementation
8.4.2 IoT Building Blocks
8.5 IoT Architecture
8.5.1 Layered Architectures
8.5.2 Domain-Specific IoT Architecture
8.5.3 Industry-Defined Architectures
8.6 Conclusion
References
9. IoT Devices
Mukesh Choubisa
9.1 Introduction
9.1.1 What is IoT?
9.1.2 History of IoT
9.1.3 Realizing the Concept
9.1.4 IoT Takes Off
9.1.5 Future of Internet of Things
9.2 Application of IoT
9.2.1 Transportation
9.2.2 Environmental Monitoring
9.2.3 Infrastructure Management
9.2.4 Medical and Healthcare Management
9.2.5 Home Automation
9.2.6 Energy Management
9.2.7 Agriculture
9.2.8 Water Supply
9.3 IoT Devices
9.3.1 Introduction of IoT Devices
9.3.2 Devices
9.4 Conclusion
References
10. IoT Securities: Applications, Security Issues and Solutions Using Diverse
Technologies
Lovanya Bajaj, Nikhil Sharma, Prashant Giridhar Shambharkar
10.1 Introduction
10.2 Related Work
10.3 Overview of Internet of Things (IoT)
10.3.1 Application Areas in IoT Demanding Crucial Security
10.3.2 Security Attacks in Internet of Things
10.4 Security Issues Addressed Using Diverse Technologies
10.4.1 Security Issues Addressed Using Machine Learning
10.4.2 Security Issues Addressed Using Artificial Intelligence
10.4.3 Security Issues Addressed Using Blockchain Technology
10.5 Open Challenges and Future Research Directions
10.5.1 Resource Limitations
10.5.2 Heterogeneous Devices
10.5.3 Interoperability of Security Protocols
10.5.4 Single Points of Failure
10.5.5 Hardware/Firmware Vulnerabilities
10.5.6 Trusted Updates and Management
10.5.7 Blockchain Vulnerabilities
10.6 Conclusion
References
11. FAMoS: Smart Farm Automatic Monitoring System
J. Dakshana, P. Balasubramaniam, A. Abirami, S. TamilSelvan
11.1 Introduction
11.2 Related Work
11.3 Methodologies Proposed
11.4 Software Elements
11.4.1 Machine Learning Algorithms with Dataset Prediction
11.4.2 Design
11.4.3 Features and Technologies
11.5 Project Cost Estimation
11.5.1 Unique Selling Point
11.5.2 SWOT
11.5.3 STP
11.6 Conclusion
References
12. IoT-Based Module to Control Electronic Devices Through Wi-Fi and Bluetooth
Ritu Shrivastava, Amit Shrivastava, Kapil Chaturvedi
12.1 Introduction
12.2 Literature Review
12.3 Proposed Wi-Fi Communication Module
12.3.1 Remotely Access IoT-Based Smart Home Appliances via Wi-Fi
12.3.2 Remotely Access IoT-Based Smart Home Appliances via Bluetooth
12.4 IR (Infrared) Remote and Arduino Nano
12.5 Conclusion
References
13. An Insight into the IoT Building Blocks: Architecture, Framework, Principles,
Applications and Challenges
Priyanka, Anoop Kumar, Keziah Nagaraj
13.1 Introduction
13.2 Related Work
13.3 Traditional and New Architecture of IoT
13.3.1 Traditional Architecture of IoT
13.3.2 New Architecture of IoT
13.4 Design Principles and Decision Framework of IoT
13.4.1 Design Principles of IoT
13.4.2 Decision Framework
13.5 Applications and Challenges
13.5.1 Applications
13.5.2 Challenges
13.6 Conclusion
References
14. Interoperability: A Conceptual Framework
Soni Chaurasia, Kamal Kumar
14.1 Introduction
14.2 Inclusions in IoT Network
14.3 IoT Interoperability Protocols
14.3.1 IoT Data Protocols
14.3.2 IoT Network Protocols
14.4 Interoperability Conceptual Framework Proposed
14.4.1 IoT Deployment Architecture
14.4.2 Understanding Interoperability Model
14.4.3 Novel IoT Interoperability Scheme
14.5 Conclusion
References
15. Securing IoT Devices Against MITM and DoS Attacks: An Analysis
Vicky Tyagi, Amar Saraswat, Ashwani Kumar, Shalini Gambhir
15.1 Introduction
15.2 Architecture of IoT
15.3 Attacks on IoT
15.3.1 MITM Attacks in IoT
15.3.2 DoS Attacks in IoT
15.3.3 MITM Targets an IoT Network
15.3.4 DoS Targets an IoT Network
15.4 Some Possible Solutions to Avoid/Prevent Cyberattacks
15.5 Conclusions
References
PART III: ARTIFICIAL INTELLIGENCE OF THINGS: SMART CITY AND SOCIAL APPLICATIONS
16. AIoT-Based Smart Cities
Shelly Garg, Namitan
16.1 What Are Smart Cities?
16.2 Internet of Things
16.2.1 Applications of IoT
16.2.2 Why is IoT on the Rise Today?
16.3 Introduction to Artificial Intelligence
16.3.1 Types of Artificial Intelligence
16.3.2 Why is AI Harmful?
16.3.3 Why is AI Useful?
16.3.4 Pros and Cons of Artificial Intelligence
16.4 AIoT in Smart Cities
16.5 Conclusion
References
17. Integrating Artificial Intelligence and IoT for Smart Cities: Applications and Challenges
Varsha Bhatia, Vivek Jaglan
17.1 Introduction
17.2 Overview of Smart Cities
17.3 AIoT in Smart Cities
17.3.1 Smart Healthcare
17.3.2 Smart Education
17.3.3 Smart Energy
17.3.4 Smart Homes
17.3.5 Smart Agriculture
17.3.6 Smart Transport
17.3.7 Smart City Services
17.4 Open Issues and Challenges
17.5 Conclusion
References
18. A Comprehensive Review of the Convergence of Blockchain, AI and IoT for Improving Social Interactions
Priya Sachdeva
18.1 Introduction
18.1.1 Background
18.1.2 Literature Reviews
18.1.3 Research Gap
18.1.4 Research Question
18.1.5 Importance of the Study
18.1.6 Research Objectives
18.2 Research Methodology
18.2.1 Research Method and Design
18.2.2 Research Approach
18.2.3 Analysis of Study
18.2.4 Convergence of Blockchain, AI as well as IoT
18.2.5 Standardization of Data
18.2.6 Enables Data Privacy and Security
18.2.7 Better Scalability
18.2.8 Authentication Through Blockchain-Based Identity
18.2.9 Automatization Through Blockchain
18.2.10 Monetization of IoT Devices
18.3 The Risks and Challenges of Convergence in the Making of Smart Cities
18.3.1 Use of Convergence in the Making of Smart Cities
18.3.2 Convergence Helps in Managing Infrastructure of Smart Cities
18.3.3 Risks and Challenges
18.4 Results and Findings
18.5 Future Research Directions for the Convergence of Blockchain, AI and IoT
18.6 Conclusion
References
19. AIoT-Based Smart Bin for Real-Time Monitoring and Management of Solid Waste
Ritik Agarwal
19.1 Introduction
19.2 Literature Review
19.2.1 SWMS
19.2.2 GMS
19.2.3 IoT-Based SWM
19.2.4 SWM by K-Query Scheduling
19.2.5 SWC as a Service
19.3 Proposed Methodology
19.3.1 System Design
19.3.2 Transmission Pattern of the Proposed System
19.3.3 Processing of the Trash Bin
19.4 Conclusion
19.5 Challenges and Future Work
References
20. AIoT in the Education Sector
Zhumaniyaz Mamatnabiyev, Meirambek Zhaparov
20.1 Introduction
20.2 AIoT Applications in the Education Sector
20.3 IoT Development Stages
20.3.1 IoT Primitives and Topics
20.3.2 IoT Roadmap
20.4 Conclusion
References
21. Artificial Intelligence of Things (AIoT)-Enabled Personalized Banking:
Investigating Intention to Adopt
Ashok Singh Malhi, Raj K Kovid, Thipendra P Singh
21.1 Introduction
21.2 Literature Review and Hypotheses
21.2.1 Technology Acceptance Model (TAM)
21.3 Methodology
21.3.1 Sample and Data Collection Procedure
21.3.2 Variable Measures and Method of Analysis
21.4 Results and Discussion
21.4.1 Assessment of Measurement Model
21.4.2 Managerial Implications
21.4.3 Limitations and Scope for Further Research
21.5 Conclusion
References
PART IV: ARTIFICIAL INTELLIGENCE OF THINGS: APPLICATIONS IN HEALTHCARE
22. AI- and IoT-Enabled Healthcare Applications: A Review
N. Krishna Chaitanya, Mangesh M. Ghonge, G. Vimala Kumari, S.Leela Lakshmi
22.1 Introduction
22.2 Literature Review
22.3 IoT in Healthcare
22.3.1 IoT Applications in Healthcare
22.3.2 Blood Glucose Monitoring
22.3.3 Blood Pressure Monitoring
22.3.4 Heart Rate Monitoring
22.3.5 Temperature Monitoring
22.3.6 Depression Monitoring
22.3.7 Asthma Inhalers
22.3.8 Smart Contact Lenses
22.3.9 Robotic Surgery
22.4 Role of Artificial Intelligence in Healthcare
22.5 Challenges of IoT in Healthcare
22.6 Conclusion
References
23. An Extensive Survey of AIoT in Healthcare: Applications, Challenges and Future Opportunities
Gunjan, Ishwari Singh Rajput, Aditya Gupta, Soni Chaurasia
23.1 Introduction
23.2 Background
23.2.1 Artificial Intelligence (AI)
23.2.2 Internet of Things (IoT)
23.2.3 Integration of AI and IoT
23.2.4 Architecture of AIoT
23.2.5 Application of AIoT
23.3 AIoT in Healthcare
23.4 AIoT Challenges and Opportunities
23.4.1 Challenges
23.4.2 Opportunities
23.5 Conclusion
References
24. AI- and IoT-Based Face Recognition Model for Identification of Human Diseases
Pushan Kumar Dutta, Susanta Mitra
24.1 Introduction
24.2 Problem Identification
24.2.1 Morphology of the Craniofacial Complex
24.2.2 Solution
24.3 Proposed Methodology
24.3.1 Software Development
24.3.2 Dataset Creation and Its Extraction
24.3.3 Training the System or Model
24.3.4 Recognizing Faces in the Video Stream
24.4 Findings and Discussion
24.5 Conclusion
References
25. IoT in the Medical Field
S. Sasikala, K. Sharmila
25.1 Introduction
25.1.1 Classification of IoT
25.1.2 Nine Healthcare Monitoring Devices
25.2 Security Matters for IoT in Healthcare
25.3 Cholesterol Control Levels
25.3.1 Symptoms of High Cholesterol
25.3.2 Diagnosis of High Cholesterol Levels
25.3.3 Risks for High Cholesterol
25.3.4 Complications from High Cholesterol
25.3.5 Treatment and Prevention
25.3.6 Screening for Stroke and Cardiovascular Disease Risk
25.4 IoR Device for Cholesterol Test: CURO L7
25.5 Challenges, Limitations, and Future Scope
25.6 Conclusion
References
26. Using Deep Learning to Characterize Persistent Physiological Parameters in Patient Monitoring Systems
Dhyanendra Jain, Anjani Gupta, Amit Kumar Pandey, Prashant Vats
26.1 Introduction
26.2 Using Deep Learning and AI for Surveillance
26.3 Other Ways that Do Not Rely on Machine Learning Knowledge
26.4 The Present Position of Learning Algorithms and Patient Monitoring
26.4.1 Detection of Infections and Symptomatic Worsening
26.4.2 Lowering False Alarm Rate
26.4.3 Identifying Dementia
26.4.4 Detecting Respiratory Perceptions Using Waveforms Monitoring
26.4.5 Managing of Anesthesia in the Intensive Care Unit
26.4.6 Identifying Unreported Atrial Fibrillation and Another Arrhythmia
26.5 Possible Uses for Machine Learning Surveillance
26.5.1 Assisting with Monitoring Inferential Process
26.5.2 Synth Sounds, Motifs, Motion, and Sophistication
26.5.3 Incorporation of Clinical Information for a Patient
26.6 Concerns and Future Objectives
26.7 Conclusions
References
PART V: ARTIFICIAL INTELLIGENCE OF THINGS: APPLICATIONS IN AGRICULTURE AND INDUSTRIES
27. Smart Agriculture System Using Artificial Intelligence and Internet of Things
Meenakshi Yadav, Preety, Esha Saxena, Akhilesh Das
27.1 Introduction
27.2 Artificial Intelligence and IoT in Agriculture
27.2.1 Role of AI in Agriculture
27.2.2 Role of IoT in Agriculture
27.3 Components of AI and IoT for Agriculture
27.4 Application of IoT and AI in Agricultural Automation
27.5 Challenges and Opportunities
27.6 Conclusion and Future Trends
References
28. Application of AI and IoT in Agriculture
Rashmi Singh
28.1 Agricultural Process
28.2 The Use of Artificial Intelligence in Agricultural Applications
28.3 Predictive Analytics and Precision Agriculture
28.3.1 Meam Squared Error
28.3.2 Crop Yield Prediction Using Machine Learning for Accurate Soil Nutrient Measurement via Sensor Network
28.3.3 Agriculture Analysis in Data Mining High-Tech Farming of the Future
28.3.4 Crop Yield Prediction Using Hadoop’s Agro Algorithm
28.3.5 Crop Yield Prediction
28.3.6 Data from Soil Testing for Cultivation Advisory Report
28.4 Agricultural Robotics (Agrobots)
28.5 AI-Enabled System for Pest Control and Disease Diagnosis
28.6 Adopting AI: A Challenge for Farmers
28.7 Conclusion
References
29. Traffic Management System Using AIoT
Pallavi Choudekar, Rashmi Singh
29.1 Introduction
29.1.1 Artificial Intelligence
29.1.2 AI and Transport
29.1.3 IoT in Transportation Industry
29.2 Smart Road Traffic Management System (SRTMS)
29.3 Smart Traffic Information System (TIS)
29.3.1 How TIS Works
29.3.2 Collection of Data
29.3.3 Reconstitution and Translation of Data
29.3.4 Dissemination of Information
29.3.5 Safety Management and Emergency Systems
29.3.6 Emergency Management and AI
29.3.7 Traffic Management Plan for Urban Areas
29.3.8 Real-Time Traffic Analysis
29.3.9 Predictive Traffic Control
29.3.10 Diverse Lanes for Different Vehicles
29.4 Smart Parking Management System
29.4.1 Smart Parking System
29.4.2 How Does Intelligent Parking Work?
29.5 Smart Pavement Management System
29.5.1 Basics of Smart Pavement Management System
29.5.2 PMS Framework Implementation
29.6 Conclusion
References
30. Autonomous Vehicles: A Convergence Application of AI and IoT
Neha Gehlot, Amritpal Kuar
30.1 Introduction
30.1.1 How Self-Driving Cars (SDCs) Work
30.1.2 SDC Technologies
30.1.3 Levels of Automation
30.2 Technical Challenges in Self-Driving Cars
30.2.1 Latency
30.2.2 Verification and Testing
30.2.3 Reliability and Safeness of SDCs
30.2.4 Software Quality
30.2.5 Computing Resources
30.2.6 Security and Hacking Threats
30.2.7 Data Confidentiality
30.2.8 Accuracy of Environmental Sensing
30.2.9 Managing the Sensor Data
30.2.10 Uncertainty of SDC Decisions
30.2.11 Actuation Process/Task
30.3 Other Very Critical Areas
30.3.1 Unpredictable Road Conditions
30.3.2 Imperfect Traffic Conditions
30.3.3 Wave Interference
30.3.4 Human-Centric
30.3.5 Software System Bugs and Intrusion Attacks
30.3.6 Need for Huge Classifications
30.3.7 Problems Due to Co-existence of Human-Driven and Self-Driving Cars
30.3.8 Flocking Problem
30.3.9 Sharing Cost Challenge
30.4 Blind Spots in Autonomous Vehicles
30.5 Potential Cyberattacks on Automated Vehicles
30.5.1 High Level Threats
30.5.2 Medium Level Threats
30.5.3 Verdict on Cyber Security of SDCs
30.6 Conclusion
References
31. Convergence of Artificial Intelligence and Internet of Things for Software-Defined Radios
Shilpa Mehta, Xue Jun Li, Surjeet Dalal
31.1 Introduction
31.2 Review of SDR Receiver Architectures
31.2.1 Superheterodyne Receivers
31.2.2 Dual Conversion Superheterodyne Receivers
31.2.3 Image Rejection Receivers
31.2.4 Direct Conversion Receivers
31.2.5 Low-IF Receivers
31.3 Integration of SDR and IoT
31.3.1 IoT
31.3.2 IoT Technologies: Literature Review
31.4 Artificial Intelligence
31.4.1 Historical Background
31.4.2 Optimization
31.4.3 Multi-Objective Optimization
31.5 Proposed AI-Based Receiver Architecture
31.5.1 Low Noise Amplifier
31.5.2 Mixer
31.5.3 IF Amplifiers
31.5.4 Polyphase Filter
31.6 AI Optimization
31.7 Results and Discussion
31.8 Conclusion
References
32. Artificial Intelligence of Things (AIoT) for Intelligent Data Design
Parul Gandhi, Raj Kumar
32.1 Introduction
32.1.1 How Does AIoT Work?
32.1.2 AIoT Architecture
32.1.3 AIoT Applications
32.2 AI, IoT, and Big Data Analytics
32.3 AIoT-Based Data Analytics and Decision-Making
32.3.1 Public Data
32.3.2 Business Data
32.3.3 Government Data
32.4 Challenges
32.5 Role of AIoT in the Context of COVID-19
32.6 Future Direction and Conclusion
References
33. A Study of Implementation of Blockchain Technology in Land Registration:
A SWOT Analysis
Ankita Goyal, Upendra Singh
33.1 Introduction
33.2 Literature Review
33.3 Research Methodology
33.4 Discussion, Analysis, Limitation and Future Work
33.4.1 Discussion
33.4.2 Analysis
33.4.3 Limitation and Future Work
33.5 Conclusion
References
34. Smart Mask Disinfection System (SMDS)
N. Swetha Sridevi, N. C. Preethika, T. Subiksha
34.1 Introduction
34.1.1 Topic Introduction
34.1.2 Motivation
34.1.3 Task Undertaken
34.1.4 Objective
34.1.5 Scope of the Work
34.1.6 Intended Approach
34.2 Related Work
34.3 Smart Mask Disinfection System
34.3.1 Problem Statement
34.3.2 Materials and Methods
34.3.3 Design
34.3.4 Experimental Setup
34.3.5 Modeling
34.3.6 Methodology
34.3.7 Operational Algorithm
34.4 Results and Discussion
34.5 Conclusion
References
Editors
Index
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