Exploring Energy-Efficient Machine Learning Algorithms for Low-Power IoT Edge Devices

1. Introduction 1.1 Background and Motivation 1.2 Problem Statement 1.3 Objectives of the Study 1.4 Structure of the Thesis 2. Internet of Things (IoT) Overview 2.1 Definition and Applications 2.2 IoT Architecture and Components 2.3 Challenges in IoT Deployment 3. Machine Learning and IoT 3.1 Role of Machine Learning in IoT 3.2 Common Algorithms Used 3.3 Challenges in Machine Learning for IoT 4. Energy-Efficiency Considerations 4.1 Importance of Energy Efficiency 4.2 Metrics for Evaluating Efficiency 4.3 Current Energy-Saving Techniques 5. Low-Power Edge Devices 5.1 Overview of Edge Devices 5.2 Power Constraints in Edge Devices 5.3 Architectural Considerations 6. Energy-Efficient Machine Learning Algorithms 6.1 Algorithmic Optimizations 6.2 Model Compression Techniques 6.3 Adaptive Learning Models 6.4 Case Studies 7. Evaluation and Testing 7.1 Evaluation Methodologies 7.2 Performance Metrics 7.3 Experimental Setup and Results 8. Conclusion and Future Work 8.1 Summary of Findings 8.2 Limitations of Current Work 8.3 Directions for Future Research