Tiny Machine Learning

Rajdeep Chakraborty, PhD is a Professor in the Department of Computer Science and Engineering, Institute of Engineering and Technology, SAGE University, Indore, Madhya Pradesh, India with more than two decades of teaching experience and extensive involvement in research. He has made notable contributions through various publications, including patents, books, journal articles, and conference papers. His research interests include cryptography, network security, IoT, and blockchain. Rana Majumdar, PhD is an Associate Professor at the Sister Nivedita University, Kolkata, West Bengal, India. He is the author of numerous research publications at the national and international levels, three books, one copyright, and 12 patents. His research focuses on machine learning, computer vision, software reliability engineering, digital image and video processing, and machine learning. S. Balamurugan, PhD is the Director of Intelligent Research Consultancy Services, Coimbatore, Tamil Nadu, India. He has published more than 90 books, 300 articles in national and international journals and conferences, and 300 patents. He is a research consultant for many companies, startups, and micro-, small, and medium enterprises. Sheng-Lung Peng, PhD is a Professor in the Department of Creative Technologies and Product Design and the Dean of the College of Innovative Design and Management at the National Taipei University of Business, Taiwan. In addition to his roles at NTUB, he holds honorary and adjunct professorships at several institutions and serves as the President of the Association of Taiwan Computer Programming Contest and the Association of Algorithms and Computation Theory. His research focuses on designing algorithms in artificial intelligence, bioinformatics, combinatorics, data mining, and networking, with more than 100 research papers published in these areas.

• Preface xxvPart I: Fundamentals 11 The Basics of TinyML: An Introductory Exploration 3Darothi Sarkar and Monalisa Dey1.1 Introduction to TinyML 41.2 Technological Underpinnings of TinyML 61.3 Real-World Applications of TinyML 111.4 Challenges and Limitations of TinyML 142 Advances in TinyML: A Systematic Review of Architectures, Algorithms, and Innovations 21Sumanta Chatterjee, Aritra Banerjee, Tania Biswas and Somya Ranjan Bhoi2.1 Introduction 222.2 Background 242.3 Tiny Machine Learning 262.4 TinyML Operations 282.5 Application 322.6 Challenges and Proposed Solutions 402.7 Impacts of TinyML 432.8 Sustainable Development Through TinyML 452.9 Conclusion 473 Edge Intelligence and Trust: The Synergy of TinyML, IoT, and Blockchain in Modern Applications 51Abhishek Bhattacharya, Soumi Dutta, Anupam Ghosh, Arijit Dutta, Prabuddha Chatterjee and Sangeeta Banik3.1 Introduction 523.2 Literature Review 593.3 Applications 653.4 Discussion 753.5 Conclusion 774 Use Cases of TinyML 87S. Sharmila Devi4.1 Introduction 884.2 Use Cases of TinyML 904.3 Conclusion 994.4 Future Scope 100Part II: Applications 1055 Advancing Smart Devices and IoT: Research Insights and Directions 107Ajay Verma, Nahida Majeed Wani and Girraj Kumar Verma5.1 Introduction 1085.2 How Smart Devices Work 1095.3 The Need for Smart Devices in the Real World 1115.4 Properties of Smart Devices 1125.5 Connection to the Internet of Things (IoT) 1155.6 Security and Privacy: Keeping the IoT Landscape Safe 1165.7 Trends and Research Opportunities in the Future 1225.8 Conclusion 1236 TinyML for Smart Devices and IoT: Enabling Efficient and Intelligent Applications 127Neeta A. Ukirade6.1 Introduction 1286.2 Tools and Frameworks for TinyML Development 1306.3 Key Techniques in TinyML for IoT 1336.4 Applications of TinyML in Smart IoT Devices 1356.5 Challenges and Limitations 1386.6 Future Directions 1416.7 Conclusion 1437 Predictive Maintenance Using Tiny Machine Learning: A Revolutionary Approach to Proactive Equipment Maintenance 149G. JayaLakshmi, Ch. JayaLakshmi and M. Ramesh7.1 Introduction 1507.2 Predictive Maintenance: The Need for Proactivity 1517.3 TinyML: Scope, Advantages, and Applications 1547.4 TinyML in Predictive Maintenance: Key Components and Implementation Framework 1577.5 Analyzing Real-World Applications and Case Studies of TinyML-Based Predictive Maintenance Systems 1597.6 Conclusion 1597.7 Future Scope 1608 TinyML and IoT in Agriculture: Boosting Real-Time Efficiency, Autonomy, and Resilience in Smart Farming 163Shanthalakshmi M., Deepika N., Avvudaiyappan R.M. and Prince Raj J.8.1 Introduction 1648.2 Smart Fertilizer Distribution Using Soil and Crop Data 1678.3 Weed Detection Using TinyML and IoT 1708.4 Animal Intrusion Detection in Crops 1728.5 Disease Prevention and Detection 1768.6 Enhancing Smart Irrigation with TinyML for Climate Prediction and Optimization Existing Systems 1798.7 Conclusion 1848.8 Future Scope 1859 TinyML and IoT for Predictive Maintenance and Real-Time Decision Support in Automotive Air Conditioning 191G. Bhavani and C. Jeyalakshmi9.1 Introduction 1929.2 Architecture Overview 1939.3 Technologies Enabling TinyML 1989.4 Advantages of a Properly Functioning AC System 2019.5 Advantages of TinyML in Real-Time Data Monitoring 2019.6 Challenges 2029.7 Conclusion 2049.8 Future Scope 20510 Automated Harm Detection: Enhancing Women's Safety in Real Time 207Shoban S., Rohith V., Shanthalakshmi M. and Deepika N.10.1 Introduction 20810.2 Prior Knowledge 21010.3 Related Works 21210.4 Proposed Methodology 21510.5 Challenges 23210.6 Future Scope 23311 Butterfly Optimization with Random Forest for COVID-19 Prediction Using Lung Image 237Sivanantham Kalimuthu, Ramkumar N., Arun Prakash N., Boorneush M. and Dhusiyanth M.11.1 Introduction 23811.2 Literature Survey 24111.3 Proposed Research Methodology 24311.4 Implementation Results 24711.5 Conclusion 25411.6 Future Scope 255Part III: Security 25912 AI-Powered Resilience and Privacy Preservation in Cloud-IoT Environments for Smart Devices Using Fog Computing Methodologies 261Biplab Gope and Soumen Santra12.1 Introduction 26212.2 AI-Powered Resilience Mechanisms 26412.3 Privacy Preservation Techniques 26512.4 Fog Computing as an Enabler 26512.5 Key Concepts 26512.6 Results 26612.7 Current Practices and Challenges 26712.8 Challenges 26812.9 Proposed Solutions 26812.10 Applications and Use Cases 26912.11 Technological Frameworks 27012.12 Conclusion 27012.13 Future Scope 27313 Data Privacy and Transmission Security 279Saptarshi Kumar Sarkar, Anupama Sen and Piyal Roy13.1 Introduction 27913.2 Foundations of Data Privacy 28313.3 Transmission Security 28813.4 Emerging Threats to Data Privacy and Transmission Security 294Contents xvii13.5 Impact of Emerging Technologies 30013.6 Challenges in Ensuring Data Privacy and Secure Transmission 30613.7 Practical Approaches to Enhancing Data Privacy and Transmission Security 31213.8 Conclusion 31614 Security and Privacy Concerns for Blockchain-Enabled Federated Learning 321Partha Ghosh, Ananya Biswas, Suradhuni Ghosh, Rima Bhowmik and Ankita Barua14.1 Introduction 32214.2 Importance of Security and Privacy 32414.3 Architecture of Federated Learning 32614.4 Difference between Centralized Learning, Distributed Learning, and Federated Learning 32714.5 Sources of Vulnerabilities in Federated Learning 32914.6 Security Threats in Federated Learning 33214.7 Defense Mechanism in Federated Learning System 33714.8 Federated Learning Schemes 34114.9 Federated Learning: An Approach to Healthcare in IIoE that Protects Privacy 34214.10 Homomorphic Encryption (HE) Method in IIoE-Focused Federated Learning 343Contents xix14.11 Blockchain-Powered Federated Learning 34514.12 Decentralized Data Sharing in Healthcare 34714.13 Public Key Infrastructure (PKI) for the System 35114.14 Protecting Privacy with Cross-Chained Fl Techniques 35214.15 Use of Blockchain-Enabled FL to Preserve Privacy 35314.16 Challenges and Solutions 35414.17 Open Research Challenges 35814.18 Conclusion and Future Direction 36015 Adversarial Attacks and Defenses in Security 367Sudeshna Dey, Siddhartha Chatterjee, Sumita Gupta and Sima Das15.1 Introduction 36815.2 Fundamentals of Federated Learning 37015.3 Security and Privacy Threats in FL 37315.4 Attacks in Federated Learning 37415.5 Problems and Committing Directions 38215.6 Conclusion 38516 Ethical and Technical Foundations of Privacy-Preserving Federated Learning 389Muhammad Rifthy Kalideen16.1 Introduction 39016.2 Foundations of Federated Learning 39216.3 Ethical Foundations of Privacy in Federated Learning 39616.4 Technical Foundations of Privacy-Preserving Federated Learning 40216.5 Interplay Between Ethical and Technical Foundations 40716.6 Case Studies and Real-World Applications 41016.7 Future Directions and Emerging Trends 41216.8 Conclusion 415References 41617 Integrating Security Measures in CLAHE-Enhanced YOLOV8 Model for Underwater Object Detection 423Niyati Sahoo, Sanjukta Mohanty and Arup Abhinna Acharya17.1 Introduction 42417.2 Background 42617.3 Related Works 43517.4 Proposed Approach 43817.5 Experiment and Results 45017.6 Frequently Occurring Threats and Mitigation Policy 45217.7 Conclusion 45417.8 Future Scope 45418 TinyML Deployment for Resource-Constrained Devices in IoT Applications with Attribute-Based Encryption Scheme 457R. Lavanya and V. Thanigaivelan18.1 Introduction 45818.2 Resource-Constrained Devices 46118.3 Background for Attribute-Based Encryption 46618.4 Related Work in ABE and Other Security Schemes 46818.5 Local Interpretable Model-Agnostic Explanations 47018.6 Conclusion 47219 Deep Learning-Based Adversarial Attack Detection for Cloud-IoT Systems 475Amit Kumar, Sachin Ahuja and Ganesh Gupta19.1 Introduction 47619.2 Background and Motivation 47819.3 Deep Learning for Intrusion Detection in Cloud-IoT Systems 48019.4 Case Study: Adversarial Attack Detection in Smart Grid Systems 48319.5 Challenges and Future Directions 48519.6 Conclusion 486Bibliography 487Index 489