Evaluating the Efficiency of Machine Learning Algorithms in Edge Computing Environments for IoT Devices

1. Introduction 1.1 Background and Motivation 1.2 Research Objectives 1.3 Structure of the Work 2. Theoretical Foundations 2.1 Machine Learning Algorithms Basics 2.2 Edge Computing Principles 2.3 IoT Devices and Requirements 3. Literature Review 3.1 Previous Studies on ML Efficiency 3.2 Edge Computing in IoT Applications 3.3 Comparative Analysis of Related Work 4. Methodology 4.1 Research Design and Approach 4.2 Data Collection Methods 4.3 Evaluation Metrics Used 5. Case Study: ML in Edge Environments 5.1 Selection of Algorithms 5.2 Deployment of Algorithms on Edge 5.3 Performance Measurement Techniques 6. Results and Discussion 6.1 Analysis of Data Collected 6.2 Comparison of Algorithm Efficiency 6.3 Interpretation of Findings 7. Challenges and Limitations 7.1 Technical Constraints in Edge Computing 7.2 Limitations of Selected ML Algorithms 7.3 Future Research Directions 8. Conclusion and Recommendations 8.1 Summary of Key Findings 8.2 Practical Implications for IoT Devices 8.3 Recommendations for Practitioners