Preface xxv Part I: Fundamentals 1 1 The Basics of TinyML: An Introductory Exploration 3 Darothi Sarkar and Monalisa Dey 1.1 Introduction to TinyML 4 1.2 Technological Underpinnings of TinyML 6 1.3 Real-World Applications of TinyML 11 1.4 Challenges and Limitations of TinyML 14 2 Advances in TinyML: A Systematic Review of Architectures, Algorithms, and Innovations 21 Sumanta Chatterjee, Aritra Banerjee, Tania Biswas and Somya Ranjan Bhoi 2.1 Introduction 22 2.2 Background 24 2.3 Tiny Machine Learning 26 2.
4 TinyML Operations 28 2.5 Application 32 2.6 Challenges and Proposed Solutions 40 2.7 Impacts of TinyML 43 2.8 Sustainable Development Through TinyML 45 2.9 Conclusion 47 3 Edge Intelligence and Trust: The Synergy of TinyML, IoT, and Blockchain in Modern Applications 51 Abhishek Bhattacharya, Soumi Dutta, Anupam Ghosh, Arijit Dutta, Prabuddha Chatterjee and Sangeeta Banik 3.1 Introduction 52 3.2 Literature Review 59 3.
3 Applications 65 3.4 Discussion 75 3.5 Conclusion 77 4 Use Cases of TinyML 87 S. Sharmila Devi 4.1 Introduction 88 4.2 Use Cases of TinyML 90 4.3 Conclusion 99 4.4 Future Scope 100 Part II: Applications 105 5 Advancing Smart Devices and IoT: Research Insights and Directions 107 Ajay Verma, Nahida Majeed Wani and Girraj Kumar Verma 5.
1 Introduction 108 5.2 How Smart Devices Work 109 5.3 The Need for Smart Devices in the Real World 111 5.4 Properties of Smart Devices 112 5.5 Connection to the Internet of Things (IoT) 115 5.6 Security and Privacy: Keeping the IoT Landscape Safe 116 5.7 Trends and Research Opportunities in the Future 122 5.8 Conclusion 123 6 TinyML for Smart Devices and IoT: Enabling Efficient and Intelligent Applications 127 Neeta A.
Ukirade 6.1 Introduction 128 6.2 Tools and Frameworks for TinyML Development 130 6.3 Key Techniques in TinyML for IoT 133 6.4 Applications of TinyML in Smart IoT Devices 135 6.5 Challenges and Limitations 138 6.6 Future Directions 141 6.7 Conclusion 143 7 Predictive Maintenance Using Tiny Machine Learning: A Revolutionary Approach to Proactive Equipment Maintenance 149 G.
JayaLakshmi, Ch. JayaLakshmi and M. Ramesh 7.1 Introduction 150 7.2 Predictive Maintenance: The Need for Proactivity 151 7.3 TinyML: Scope, Advantages, and Applications 154 7.4 TinyML in Predictive Maintenance: Key Components and Implementation Framework 157 7.5 Analyzing Real-World Applications and Case Studies of TinyML-Based Predictive Maintenance Systems 159 7.
6 Conclusion 159 7.7 Future Scope 160 8 TinyML and IoT in Agriculture: Boosting Real-Time Efficiency, Autonomy, and Resilience in Smart Farming 163 Shanthalakshmi M., Deepika N., Avvudaiyappan R.M. and Prince Raj J. 8.1 Introduction 164 8.
2 Smart Fertilizer Distribution Using Soil and Crop Data 167 8.3 Weed Detection Using TinyML and IoT 170 8.4 Animal Intrusion Detection in Crops 172 8.5 Disease Prevention and Detection 176 8.6 Enhancing Smart Irrigation with TinyML for Climate Prediction and Optimization Existing Systems 179 8.7 Conclusion 184 8.8 Future Scope 185 9 TinyML and IoT for Predictive Maintenance and Real-Time Decision Support in Automotive Air Conditioning 191 G. Bhavani and C.
Jeyalakshmi 9.1 Introduction 192 9.2 Architecture Overview 193 9.3 Technologies Enabling TinyML 198 9.4 Advantages of a Properly Functioning AC System 201 9.5 Advantages of TinyML in Real-Time Data Monitoring 201 9.6 Challenges 202 9.7 Conclusion 204 9.
8 Future Scope 205 10 Automated Harm Detection: Enhancing Women''s Safety in Real Time 207 Shoban S., Rohith V., Shanthalakshmi M. and Deepika N. 10.1 Introduction 208 10.2 Prior Knowledge 210 10.3 Related Works 212 10.
4 Proposed Methodology 215 10.5 Challenges 232 10.6 Future Scope 233 11 Butterfly Optimization with Random Forest for COVID-19 Prediction Using Lung Image 237 Sivanantham Kalimuthu, Ramkumar N., Arun Prakash N., Boorneush M. and Dhusiyanth M. 11.1 Introduction 238 11.
2 Literature Survey 241 11.3 Proposed Research Methodology 243 11.4 Implementation Results 247 11.5 Conclusion 254 11.6 Future Scope 255 Part III: Security 259 12 AI-Powered Resilience and Privacy Preservation in Cloud-IoT Environments for Smart Devices Using Fog Computing Methodologies 261 Biplab Gope and Soumen Santra 12.1 Introduction 262 12.2 AI-Powered Resilience Mechanisms 264 12.3 Privacy Preservation Techniques 265 12.
4 Fog Computing as an Enabler 265 12.5 Key Concepts 265 12.6 Results 266 12.7 Current Practices and Challenges 267 12.8 Challenges 268 12.9 Proposed Solutions 268 12.10 Applications and Use Cases 269 12.11 Technological Frameworks 270 12.
12 Conclusion 270 12.13 Future Scope 273 13 Data Privacy and Transmission Security 279 Saptarshi Kumar Sarkar, Anupama Sen and Piyal Roy 13.1 Introduction 279 13.2 Foundations of Data Privacy 283 13.3 Transmission Security 288 13.4 Emerging Threats to Data Privacy and Transmission Security 294Contents xvii 13.5 Impact of Emerging Technologies 300 13.6 Challenges in Ensuring Data Privacy and Secure Transmission 306 13.
7 Practical Approaches to Enhancing Data Privacy and Transmission Security 312 13.8 Conclusion 316 14 Security and Privacy Concerns for Blockchain-Enabled Federated Learning 321 Partha Ghosh, Ananya Biswas, Suradhuni Ghosh, Rima Bhowmik and Ankita Barua 14.1 Introduction 322 14.2 Importance of Security and Privacy 324 14.3 Architecture of Federated Learning 326 14.4 Difference between Centralized Learning, Distributed Learning, and Federated Learning 327 14.5 Sources of Vulnerabilities in Federated Learning 329 14.6 Security Threats in Federated Learning 332 14.
7 Defense Mechanism in Federated Learning System 337 14.8 Federated Learning Schemes 341 14.9 Federated Learning: An Approach to Healthcare in IIoE that Protects Privacy 342 14.10 Homomorphic Encryption (HE) Method in IIoE-Focused Federated Learning 343Contents xix 14.11 Blockchain-Powered Federated Learning 345 14.12 Decentralized Data Sharing in Healthcare 347 14.13 Public Key Infrastructure (PKI) for the System 351 14.14 Protecting Privacy with Cross-Chained Fl Techniques 352 14.
15 Use of Blockchain-Enabled FL to Preserve Privacy 353 14.16 Challenges and Solutions 354 14.17 Open Research Challenges 358 14.18 Conclusion and Future Direction 360 15 Adversarial Attacks and Defenses in Security 367 Sudeshna Dey, Siddhartha Chatterjee, Sumita Gupta and Sima Das 15.1 Introduction 368 15.2 Fundamentals of Federated Learning 370 15.3 Security and Privacy Threats in FL 373 15.4 Attacks in Federated Learning 374 15.
5 Problems and Committing Directions 382 15.6 Conclusion 385 16 Ethical and Technical Foundations of Privacy-Preserving Federated Learning 389 Muhammad Rifthy Kalideen 16.1 Introduction 390 16.2 Foundations of Federated Learning 392 16.3 Ethical Foundations of Privacy in Federated Learning 396 16.4 Technical Foundations of Privacy-Preserving Federated Learning 402 16.5 Interplay Between Ethical and Technical Foundations 407 16.6 Case Studies and Real-World Applications 410 16.
7 Future Directions and Emerging Trends 412 16.8 Conclusion 415 References 416 17 Integrating Security Measures in CLAHE-Enhanced YOLOV8 Model for Underwater Object Detection 423 Niyati Sahoo, Sanjukta Mohanty and Arup Abhinna Acharya 17.1 Introduction 424 17.2 Background 426 17.3 Related Works 435 17.4 Proposed Approach 438 17.5 Experiment and Results 450 17.6 Frequently Occurring Threats and Mitigation Policy 452 17.
7 Conclusion 454 17.8 Future Scope 454 18 TinyML Deployment for Resource-Constrained Devices in IoT Applications with Attribute-Based Encryption Scheme 457 R. Lavanya and V. Thanigaivelan 18.1 Introduction 458 18.2 Resource-Constrained Devices 461 18.3 Background for Attribute-Based Encryption 466 18.4 Related Work in ABE and Other Security Schemes 468 18.
5 Local Interpretable Model-Agnostic Explanations 470 18.6 Conclusion 472 19 Deep Learning-Based Adversarial Attack Detection for Cloud-IoT Systems 475 Amit Kumar, Sachin Ahuja and Ganesh Gupta 19.1 Introduction 476 19.2 Background and Motivation 478 19.3 Deep Learning for Intrusion Detection in Cloud-IoT Systems 480 19.4 Case Study: Adversarial Attack Detection in Smart Grid Systems 483 19.5 Challenges and Future Directions 485 19.6 Conclusion 486 Bibliography 487 Index 489.