Explainable Machine Learning Models and Architectures
1st Edition
Published on 6. September 2023
Book
Hardback
272 pages
978-1-394-18584-9 (ISBN)
Description
EXPLAINABLE MACHINE LEARNING MODELS AND ARCHITECTURES This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, and the efficient hardware of machine learning applications.
Machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories that further lead to more IC area. Trade-offs between power consumption, delay and IC area are always a concern of designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient machine-learning models. Many real-time applications like the processing of biomedical data in healthcare, smart transportation, satellite image analysis, and IoT-enabled systems have a lot of scope for improvements in terms of accuracy, speed, computational powers, and overall power consumption.
This book deals with the efficient machine and deep learning models that support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and field programmable gate arrays (FPGAs), or any hybrid system. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.
Machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories that further lead to more IC area. Trade-offs between power consumption, delay and IC area are always a concern of designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient machine-learning models. Many real-time applications like the processing of biomedical data in healthcare, smart transportation, satellite image analysis, and IoT-enabled systems have a lot of scope for improvements in terms of accuracy, speed, computational powers, and overall power consumption.
This book deals with the efficient machine and deep learning models that support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and field programmable gate arrays (FPGAs), or any hybrid system. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.
More details
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Weight
531 gr
ISBN-13
978-1-394-18584-9 (9781394185849)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Suman Lata Tripathi | Mufti Mahmud
Explainable Machine Learning Models and Architectures
E-Book
08/2023
1st Edition
Wiley
€177.99
Available for download
Suman Lata Tripathi | Mufti Mahmud
Explainable Machine Learning Models and Architectures
E-Book
08/2023
1st Edition
Wiley
€168.99
Available for download
Persons
Suman Lata Tripathi, PhD, is a professor at Lovely Professional University with more than 21 years of experience in academics. She has published more than 103 research papers in refereed journals and conferences. She has organized several workshops, summer internships, and expert lectures for students, and she has worked as a session chair, conference steering committee member, editorial board member, and reviewer for IEEE journals and conferences. She has published three books and currently has multiple volumes scheduled for publication from Wiley-Scrivener.
Mufti Mahmud, PhD, is an associate professor of cognitive computing at the Department of Computer Science of Nottingham Trent University, UK. He is the Coordinator of the Computer Science and Informatics Unit of Assessment of Research Excellence Framework at NTU and the deputy group leader of the Interactive Systems Research Group and the Cognitive Computing & Brain Informatics research group. He is also an active member of the Computing and Informatics Research Centre and the Medical Technologies Innovation Facility. He is a member of numerous societies and research committees.
Mufti Mahmud, PhD, is an associate professor of cognitive computing at the Department of Computer Science of Nottingham Trent University, UK. He is the Coordinator of the Computer Science and Informatics Unit of Assessment of Research Excellence Framework at NTU and the deputy group leader of the Interactive Systems Research Group and the Cognitive Computing & Brain Informatics research group. He is also an active member of the Computing and Informatics Research Centre and the Medical Technologies Innovation Facility. He is a member of numerous societies and research committees.
Content
Preface xiii
Acknowledgements xv
1 A Comprehensive Review of Various Machine Learning Techniques 1
Pooja Pathak and Parul Choudhary
1.1 Introduction 1
1.1.1 Random Forest 2
1.1.2 Decision Tree 3
1.1.3 Support Vector Machine 4
1.1.4 Naive Bayes 5
1.1.5 K-Means Clustering 6
1.1.6 Principal Component Analysis 6
1.1.7 Linear Regression 6
1.1.8 Logistic Regression 7
1.1.9 Semi-Supervised Learning 8
1.1.10 Transductive SVM 9
1.1.11 Generative Models 9
1.1.12 Self-Training 9
1.1.13 Relearning 9
1.2 Conclusions 9
2 Artificial Intelligence and Image Recognition Algorithms 11
Siddharth, Anuranjana and Sanmukh Kaur
2.1 Introduction 12
2.2 Traditional Image Recognition Algorithms 13
2.2.1 Harris Corner Detector (1988) 13
2.2.2 SIFT (2004) 15
2.2.3 ASIFT 16
2.2.4 SURF (2006) 17
2.3 Neural Network-Based Algorithms 21
2.4 Convolutional Neural Network Architecture 22
2.5 Various CNN Architectures 23
2.5.1 LeNet-5 (1998) 23
2.5.2 AlexNet (2012) 24
2.5.3 VGGNet (2014) 24
2.5.4 GoogleNet (2015) 24
3 Efficient Architectures and Trade-Offs for FPGA-Based Real-Time Systems 31
L.M.I. Leo Joseph, J. Ajayan, Sandip Bhattacharya and Sreedhar Kollem
3.1 Overview of FPGA-Based Real-Time System 31
3.1.1 Key Elements of Real-Time System 32
3.1.2 Real-Time System and its Computation 32
3.1.3 FPGA Functionality and Applications 33
3.1.4 FPGA Applications 33
3.1.5 FPGA Architecture 34
3.1.6 Reconfigurable Architectures 35
3.2 Hybrid FPGA Configurations and its Algorithms 38
3.2.1 Hybrid FPGA 38
3.2.2 Hybrid FPGA Architecture 39
3.2.3 Hybrid FPGA Configuration 40
3.3 Hybrid FPGA Algorithms 42
3.3.1 Relevance of Hardware-Accelerated Architecture to FPGA Software Implementation 44
3.4 CNN Hardware Accelerator Architecture Overview 46
3.5 Summary 47
4 A Low-Power Audio Processing Using Machine Learning Module on FPGA and Applications 49
Suman Lata Tripathi, Dasari Lakshmi Prasanna and Mufti Mahmud
4.1 Introduction 49
4.2 Existing Machine Learning Modules and Audio Classifiers 50
4.3 Audio Processing Module Using Machine Learning 56
4.4 Application of Proposed FPGA-Based ML Models 57
4.5 Implementation of a Microphone on FPGA 59
4.6 Conclusion 60
4.7 Future Scope 60
5 Synthesis and Time Analysis of FPGA-Based DIT-FFT Module for Efficient VLSI Signal Processing Applications 65
Siba Kumar Panda, Konasagar Achyut and Dhruba Charan Panda
5.1 Introduction 66
5.2 Implementation of DIT-FFT Algorithm 67
5.2.1 A Quick Overview of DIT-FFT 67
5.2.2 Algorithmic Representation with Example 69
5.2.3 Simulated Output Waveform 69
5.3 Synthesis of Designed Circuit 71
5.4 Static Timing Analysis of Designed Circuit 73
5.5 Result and Discussion 77
5.6 Conclusion 77
6 Artificial Intelligence-Based Active Virtual Voice Assistant 81
Swathi Gowroju, G. Mounika, D. Bhavana, Shaik Abdul Latheef and A. Abhilash
6.1 Introduction 82
6.2 Literature Survey 83
6.3 System Functions 87
6.4 Model Training 88
6.5 Discussion 90
6.5.1 Furnishing Movie Recommendations 91
6.5.2 KNN Algorithm Book Recommendation 92
6.6 Results 93
6.7 Conclusion 102
7 Image Forgery Detection: An Approach with Machine Learning 105
Madhusmita Mishra, Silvia Tittotto and Santos Kumar Das
7.1 Introduction 105
7.2 Historical Background 107
7.3 CNN Architecture 109
7.4 Analysis of Error Level of Image 113
7.5 Proposed Model of Image Forgery Detection, Results and Discussion 115
7.6 Conclusion 118
7.7 Future Research Directions 119
8 Applications of Artificial Neural Networks in Optical Performance Monitoring 123
Isra Imtiyaz, Anuranjana, Sanmukh Kaur and Anubhav Gautam
8.1 Introduction 123
8.2 Algorithms Employed for Performance Monitoring 129
8.2.1 Artificial Neural Networks 129
8.2.2 Deep Neural Networks 130
8.2.3 Convolutional Neural Networks 131
8.2.3.1 Convolutional Layer 131
8.2.3.2 Non-Linear Layer 132
8.2.3.3 Pooling Layer 132
8.2.3.4 Fully Connected Layer 132
8.2.4 Support Vector Regression (SVR) 133
8.2.5 Support Vector Machine (SVM) 133
8.2.6 Kernel Ridge Regression (KRR) 133
8.2.7 Long Short-Term Memory (LSTM) 133
8.3 Artificial Intelligence (AI) Methods, Performance Monitoring and Applications in Optical Networks 134
8.3.1 Performance Monitoring 134
8.3.2 Applications of AI in Optical Networking 135
8.4 Optical Impairments and Fault Management 135
8.4.1 Noise 135
8.4.2 Distortion 135
8.4.3 Timing 136
8.4.4 Component Faults 136
8.4.5 Transmission Impairments 137
8.4.6 Fault Management in Optical Network 137
8.5 Conclusion 138
9 Website Development with Django Web Framework 141
Sanmukh Kaur, Anuranjana and Yashasvi Roy
9.1 Introduction 141
9.2 Salient Features of Django 142
9.2.1 Complete 142
9.2.2 Versatile 142
9.2.3 Secure 142
9.2.4 Scalable 143
9.2.5 Maintainable 143
9.2.6 Portable 143
9.3 UI Design 143
9.3.1 HTML 143
9.3.2 CSS 144
9.3.3 Bootstrap 144
9.4 Methodology 144
9.5 UI Design 144
9.6 Backend Development 148
9.6.1 Login Page 148
9.6.2 Registration Page 149
9.6.3 User Tracking 149
9.7 Ouputs 150
9.8 Conclusion 152
10 Revenue Forecasting Using Machine Learning Models 155
Yashasvi Roy and Sanmukh Kaur
10.1 Introduction 155
10.2 Types of Forecasting 156
10.2.1 Qualitative Forecasting 156
10.2.1.1 Industries That Use Qualitative Forecasting 157
10.2.1.2 Qualitative Forecasting Methods 158
10.2.2 Quantitative Forecasting 158
10.2.2.1 Quantitative Forecasting Methods 159
10.2.3 Artificial Intelligence Forecasting 160
10.2.3.1 Artificial Neural Network (ANN) 160
10.2.3.2 Support Vector Machine (SVM) 161
10.3 Types of ML Models Used in Finance 162
10.3.1 Linear Regression 162
10.3.1.1 Simple Linear Regression 162
10.3.1.2 Multiple Linear Regression 162
10.3.2 Ridge Regression 163
10.3.3 Decision Tree 164
10.3.3.1 Prediction of Continuous Variables 164
10.3.3.2 Prediction of Categorical Variables 165
10.3.4 Random Forest Regressor 165
10.3.5 Gradient Boosting Regression 166
10.3.5.1 Advantages of Gradient Boosting 167
10.4 Model Performance 167
10.4.1 R-Squared Method 167
10.4.2 Mean Squared Error (MSE) 167
10.4.3 Root Mean Square Error (RMSE) 168
10.5 Conclusion 168
11 Application of Machine Learning Optimization Techniques in Wind Resource Assessment 171
Udhayakumar K. and Krishnamoorthy R.
11.1 Introduction 172
11.2 Wind Data Analysis Methods 173
11.2.1 Wind Characteristics Parameters 173
11.2.2 Wind Speed Distribution Methods 173
11.2.3 Weibull Method 174
11.2.4 Goodness of Fit 175
11.3 Wind Site and Measurement Details 175
11.3.1 Seasonal Wind Periods 176
11.3.2 Machine Learning and Optimization Techniques 176
11.3.2.1 Moth Flame Optimization (MFO) Method 176
11.4 Results and Discussions 180
11.4.1 Wind Characteristics 182
11.4.1.1 Kayathar Station (Onshore) 182
11.4.1.2 Gulf of Khambhat (Gujarat Offshore) Station 187
11.4.1.3 Jafrabad (Gujarat-Nearshore) 192
11.4.2 Wind Distribution Fitting 195
11.4.2.1 Kayathar Station (Onshore) 196
11.4.2.2 Bimodal Behaviour 196
11.4.2.3 Gulf of Khambhat (Offshore) Wind Distribution 202
11.4.2.4 Jafrabad Station (Nearshore) Distribution Fitting 203
11.4.3 Optimization Methods for Parameter Estimation 212
11.4.3.1 Optimization Parameters Comparison 212
11.4.4 Wind Power Density Analysis (WPD) 214
11.4.4.1 Comparison of Wind Power Density 215
11.5 Research Summary 221
11.6 Conclusions 222
12 IoT to Scale-Up Smart Infrastructure in Indian Cities: A New Paradigm 227
Indu Bala, Simarpreet Kaur, Lavpreet Kaur and Pavan Thimmavajjala
12.1 Introduction 228
12.2 Technological Progress: A Brief History 229
12.3 What is the Internet of Things (IoT)? 230
12.4 Economic Effects of Internet of Things 230
12.5 Infrastructure and Smart Infrastructure: The Difference 232
12.5.1 What is Smart Infrastructure? 233
12.5.2 What are the Principles of Smart Infrastructure? 234
12.5.3 Components of IoT-Based Smart City Project 235
12.6 Architecture for Smart Cities 236
12.6.1 Networking Technologies 237
12.6.2 Network Topologies 237
12.6.3 Network Architectures 238
12.6.3.1 Home Area Networks (HANs) 238
12.6.3.2 Field/Neighborhood Area Networks (FANs/NANs) 238
12.6.3.3 Wide Area Networks (WANs) 238
12.6.3.4 Network Protocols 238
12.7 IoT Technology in India's Smart Cities: The Current Scenario 239
12.8 Challenges in IoT-Based Smart City Projects 243
12.8.1 Technological Challenges 243
12.8.1.1 Privacy and Security 243
12.8.1.2 Smart Sensors and Infrastructure Essentials 243
12.8.1.3 Networking in IoT Systems 244
12.8.1.4 Big Data Analytics 244
12.8.2 Financial - Economic Challenges 244
12.9 Role of Explainable AI 245
12.10 Conclusion and Future Scope 246
References 246
Index 251
Acknowledgements xv
1 A Comprehensive Review of Various Machine Learning Techniques 1
Pooja Pathak and Parul Choudhary
1.1 Introduction 1
1.1.1 Random Forest 2
1.1.2 Decision Tree 3
1.1.3 Support Vector Machine 4
1.1.4 Naive Bayes 5
1.1.5 K-Means Clustering 6
1.1.6 Principal Component Analysis 6
1.1.7 Linear Regression 6
1.1.8 Logistic Regression 7
1.1.9 Semi-Supervised Learning 8
1.1.10 Transductive SVM 9
1.1.11 Generative Models 9
1.1.12 Self-Training 9
1.1.13 Relearning 9
1.2 Conclusions 9
2 Artificial Intelligence and Image Recognition Algorithms 11
Siddharth, Anuranjana and Sanmukh Kaur
2.1 Introduction 12
2.2 Traditional Image Recognition Algorithms 13
2.2.1 Harris Corner Detector (1988) 13
2.2.2 SIFT (2004) 15
2.2.3 ASIFT 16
2.2.4 SURF (2006) 17
2.3 Neural Network-Based Algorithms 21
2.4 Convolutional Neural Network Architecture 22
2.5 Various CNN Architectures 23
2.5.1 LeNet-5 (1998) 23
2.5.2 AlexNet (2012) 24
2.5.3 VGGNet (2014) 24
2.5.4 GoogleNet (2015) 24
3 Efficient Architectures and Trade-Offs for FPGA-Based Real-Time Systems 31
L.M.I. Leo Joseph, J. Ajayan, Sandip Bhattacharya and Sreedhar Kollem
3.1 Overview of FPGA-Based Real-Time System 31
3.1.1 Key Elements of Real-Time System 32
3.1.2 Real-Time System and its Computation 32
3.1.3 FPGA Functionality and Applications 33
3.1.4 FPGA Applications 33
3.1.5 FPGA Architecture 34
3.1.6 Reconfigurable Architectures 35
3.2 Hybrid FPGA Configurations and its Algorithms 38
3.2.1 Hybrid FPGA 38
3.2.2 Hybrid FPGA Architecture 39
3.2.3 Hybrid FPGA Configuration 40
3.3 Hybrid FPGA Algorithms 42
3.3.1 Relevance of Hardware-Accelerated Architecture to FPGA Software Implementation 44
3.4 CNN Hardware Accelerator Architecture Overview 46
3.5 Summary 47
4 A Low-Power Audio Processing Using Machine Learning Module on FPGA and Applications 49
Suman Lata Tripathi, Dasari Lakshmi Prasanna and Mufti Mahmud
4.1 Introduction 49
4.2 Existing Machine Learning Modules and Audio Classifiers 50
4.3 Audio Processing Module Using Machine Learning 56
4.4 Application of Proposed FPGA-Based ML Models 57
4.5 Implementation of a Microphone on FPGA 59
4.6 Conclusion 60
4.7 Future Scope 60
5 Synthesis and Time Analysis of FPGA-Based DIT-FFT Module for Efficient VLSI Signal Processing Applications 65
Siba Kumar Panda, Konasagar Achyut and Dhruba Charan Panda
5.1 Introduction 66
5.2 Implementation of DIT-FFT Algorithm 67
5.2.1 A Quick Overview of DIT-FFT 67
5.2.2 Algorithmic Representation with Example 69
5.2.3 Simulated Output Waveform 69
5.3 Synthesis of Designed Circuit 71
5.4 Static Timing Analysis of Designed Circuit 73
5.5 Result and Discussion 77
5.6 Conclusion 77
6 Artificial Intelligence-Based Active Virtual Voice Assistant 81
Swathi Gowroju, G. Mounika, D. Bhavana, Shaik Abdul Latheef and A. Abhilash
6.1 Introduction 82
6.2 Literature Survey 83
6.3 System Functions 87
6.4 Model Training 88
6.5 Discussion 90
6.5.1 Furnishing Movie Recommendations 91
6.5.2 KNN Algorithm Book Recommendation 92
6.6 Results 93
6.7 Conclusion 102
7 Image Forgery Detection: An Approach with Machine Learning 105
Madhusmita Mishra, Silvia Tittotto and Santos Kumar Das
7.1 Introduction 105
7.2 Historical Background 107
7.3 CNN Architecture 109
7.4 Analysis of Error Level of Image 113
7.5 Proposed Model of Image Forgery Detection, Results and Discussion 115
7.6 Conclusion 118
7.7 Future Research Directions 119
8 Applications of Artificial Neural Networks in Optical Performance Monitoring 123
Isra Imtiyaz, Anuranjana, Sanmukh Kaur and Anubhav Gautam
8.1 Introduction 123
8.2 Algorithms Employed for Performance Monitoring 129
8.2.1 Artificial Neural Networks 129
8.2.2 Deep Neural Networks 130
8.2.3 Convolutional Neural Networks 131
8.2.3.1 Convolutional Layer 131
8.2.3.2 Non-Linear Layer 132
8.2.3.3 Pooling Layer 132
8.2.3.4 Fully Connected Layer 132
8.2.4 Support Vector Regression (SVR) 133
8.2.5 Support Vector Machine (SVM) 133
8.2.6 Kernel Ridge Regression (KRR) 133
8.2.7 Long Short-Term Memory (LSTM) 133
8.3 Artificial Intelligence (AI) Methods, Performance Monitoring and Applications in Optical Networks 134
8.3.1 Performance Monitoring 134
8.3.2 Applications of AI in Optical Networking 135
8.4 Optical Impairments and Fault Management 135
8.4.1 Noise 135
8.4.2 Distortion 135
8.4.3 Timing 136
8.4.4 Component Faults 136
8.4.5 Transmission Impairments 137
8.4.6 Fault Management in Optical Network 137
8.5 Conclusion 138
9 Website Development with Django Web Framework 141
Sanmukh Kaur, Anuranjana and Yashasvi Roy
9.1 Introduction 141
9.2 Salient Features of Django 142
9.2.1 Complete 142
9.2.2 Versatile 142
9.2.3 Secure 142
9.2.4 Scalable 143
9.2.5 Maintainable 143
9.2.6 Portable 143
9.3 UI Design 143
9.3.1 HTML 143
9.3.2 CSS 144
9.3.3 Bootstrap 144
9.4 Methodology 144
9.5 UI Design 144
9.6 Backend Development 148
9.6.1 Login Page 148
9.6.2 Registration Page 149
9.6.3 User Tracking 149
9.7 Ouputs 150
9.8 Conclusion 152
10 Revenue Forecasting Using Machine Learning Models 155
Yashasvi Roy and Sanmukh Kaur
10.1 Introduction 155
10.2 Types of Forecasting 156
10.2.1 Qualitative Forecasting 156
10.2.1.1 Industries That Use Qualitative Forecasting 157
10.2.1.2 Qualitative Forecasting Methods 158
10.2.2 Quantitative Forecasting 158
10.2.2.1 Quantitative Forecasting Methods 159
10.2.3 Artificial Intelligence Forecasting 160
10.2.3.1 Artificial Neural Network (ANN) 160
10.2.3.2 Support Vector Machine (SVM) 161
10.3 Types of ML Models Used in Finance 162
10.3.1 Linear Regression 162
10.3.1.1 Simple Linear Regression 162
10.3.1.2 Multiple Linear Regression 162
10.3.2 Ridge Regression 163
10.3.3 Decision Tree 164
10.3.3.1 Prediction of Continuous Variables 164
10.3.3.2 Prediction of Categorical Variables 165
10.3.4 Random Forest Regressor 165
10.3.5 Gradient Boosting Regression 166
10.3.5.1 Advantages of Gradient Boosting 167
10.4 Model Performance 167
10.4.1 R-Squared Method 167
10.4.2 Mean Squared Error (MSE) 167
10.4.3 Root Mean Square Error (RMSE) 168
10.5 Conclusion 168
11 Application of Machine Learning Optimization Techniques in Wind Resource Assessment 171
Udhayakumar K. and Krishnamoorthy R.
11.1 Introduction 172
11.2 Wind Data Analysis Methods 173
11.2.1 Wind Characteristics Parameters 173
11.2.2 Wind Speed Distribution Methods 173
11.2.3 Weibull Method 174
11.2.4 Goodness of Fit 175
11.3 Wind Site and Measurement Details 175
11.3.1 Seasonal Wind Periods 176
11.3.2 Machine Learning and Optimization Techniques 176
11.3.2.1 Moth Flame Optimization (MFO) Method 176
11.4 Results and Discussions 180
11.4.1 Wind Characteristics 182
11.4.1.1 Kayathar Station (Onshore) 182
11.4.1.2 Gulf of Khambhat (Gujarat Offshore) Station 187
11.4.1.3 Jafrabad (Gujarat-Nearshore) 192
11.4.2 Wind Distribution Fitting 195
11.4.2.1 Kayathar Station (Onshore) 196
11.4.2.2 Bimodal Behaviour 196
11.4.2.3 Gulf of Khambhat (Offshore) Wind Distribution 202
11.4.2.4 Jafrabad Station (Nearshore) Distribution Fitting 203
11.4.3 Optimization Methods for Parameter Estimation 212
11.4.3.1 Optimization Parameters Comparison 212
11.4.4 Wind Power Density Analysis (WPD) 214
11.4.4.1 Comparison of Wind Power Density 215
11.5 Research Summary 221
11.6 Conclusions 222
12 IoT to Scale-Up Smart Infrastructure in Indian Cities: A New Paradigm 227
Indu Bala, Simarpreet Kaur, Lavpreet Kaur and Pavan Thimmavajjala
12.1 Introduction 228
12.2 Technological Progress: A Brief History 229
12.3 What is the Internet of Things (IoT)? 230
12.4 Economic Effects of Internet of Things 230
12.5 Infrastructure and Smart Infrastructure: The Difference 232
12.5.1 What is Smart Infrastructure? 233
12.5.2 What are the Principles of Smart Infrastructure? 234
12.5.3 Components of IoT-Based Smart City Project 235
12.6 Architecture for Smart Cities 236
12.6.1 Networking Technologies 237
12.6.2 Network Topologies 237
12.6.3 Network Architectures 238
12.6.3.1 Home Area Networks (HANs) 238
12.6.3.2 Field/Neighborhood Area Networks (FANs/NANs) 238
12.6.3.3 Wide Area Networks (WANs) 238
12.6.3.4 Network Protocols 238
12.7 IoT Technology in India's Smart Cities: The Current Scenario 239
12.8 Challenges in IoT-Based Smart City Projects 243
12.8.1 Technological Challenges 243
12.8.1.1 Privacy and Security 243
12.8.1.2 Smart Sensors and Infrastructure Essentials 243
12.8.1.3 Networking in IoT Systems 244
12.8.1.4 Big Data Analytics 244
12.8.2 Financial - Economic Challenges 244
12.9 Role of Explainable AI 245
12.10 Conclusion and Future Scope 246
References 246
Index 251