Table of Contents:

1. Introduction
  1.1 Background
  1.2 Evolution of Edge Computing
  1.3 Significance of Resource Allocation in Edge Computing
  1.4 Research Motivation
  1.5 Research Objectives
  1.6 Research Scope
  1.7 Organization of the Thesis

2. Literature Review
  2.1 Overview of Edge Computing
  2.2 Resource Allocation Challenges in Edge Computing
  2.3 Reinforcement Learning in Resource Management
  2.4 Edge Computing Architectures and Technologies
  2.5 Current Approaches to Resource Allocation
  2.6 Optimization Techniques in Edge Computing
  2.7 Related Work in Resource Allocation for Edge Computing

3. Methodology
  3.1 Data Collection and Analysis of Edge Computing Workloads
  3.2 Reinforcement Learning Algorithms for Resource Allocation
  3.3 Design of Reward Mechanisms for Resource Optimization
  3.4 Simulation and Experimentation Environment Setup
  3.5 Model Training and Evaluation
  3.6 Performance Metrics for Resource Allocation
  3.7 Ethical Considerations in Resource Management

4. Implementation and Results
  4.1 Development of Resource Allocation Framework
  4.2 Integration of Reinforcement Learning Models
  4.3 Experiment Design and Execution
  4.4 Analysis of Resource Allocation Optimization
  4.5 Performance Comparison with Traditional Methods
  4.6 Visualization of Resource Utilization Improvements
  4.7 Discussion of Results and Findings

5. Conclusion and Future Work
  5.1 Summary of Research Contributions
  5.2 Implications of the Study
  5.3 Limitations of the Research
  5.4 Future Research Directions in Edge Computing
  5.5 Practical Applications and Industry Relevance
  5.6 Recommendations for Resource Allocation in Edge Computing
  5.7 Conclusion and Final Remarks