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Introduction

In recent years, the retail industry has undergone a significant transformation with the advent of technologies such as Internet of Things (IoT), artificial intelligence, big data analytics, and cloud computing. One of the emerging technologies that is revolutionizing the retail industry is edge computing. Edge computing brings computation and data storage closer to the source of data generation, providing real-time processing and analysis capabilities. This enables retailers to optimize their operations, enhance customer experiences, and gain a competitive advantage in the market.

This thesis explores the application of edge computing in smart retail, where it can be used to improve various aspects of the retail ecosystem such as inventory management, customer engagement, and supply chain optimization. The integration of edge computing with IoT devices, sensors, and cameras in retail stores can enable real-time data analysis and decision-making, leading to enhanced operational efficiency and personalized customer experiences.

Chapter 1: Introduction

1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review

2.1 Evolution of Edge Computing
2.2 Edge Computing Architectures
2.3 Edge Computing Applications in Retail
2.4 Benefits of Edge Computing in Smart Retail
2.5 Challenges and Barriers of Implementing Edge Computing in Retail
2.6 Edge Computing Technologies in Retail
2.7 Case Studies of Edge Computing Implementation in Smart Retail
2.8 Integration of Edge Computing with Other Technologies in Retail
2.9 Future Trends in Edge Computing for Smart Retail
2.10 Summary of Literature Review

Chapter 3: System Design and Methodology

3.1 Research Framework
3.2 Research Methodology
3.3 Data Collection Methods
3.4 Data Analysis Techniques
3.5 System Architecture Design
3.6 Development of Edge Computing Solutions for Smart Retail
3.7 Performance Evaluation Metrics
3.8 Validation and Testing Procedures

Chapter 4: System Implementation

4.1 Hardware and Software Requirements
4.2 Edge Computing Infrastructure Setup
4.3 Implementation of Edge Computing Algorithms
4.4 Data Integration and Processing
4.5 System Integration with Existing Retail Technologies
4.6 Deployment and Monitoring
4.7 Performance Optimization
4.8 Scalability and Security Considerations

Chapter 5: Conclusion and Summary

5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Implications for Practice
5.4 Recommendations for Future Research
5.5 Conclusion

Thesis Overview on Edge Computing for Smart Retail

Edge computing has emerged as a promising technology that can significantly transform the retail industry by enabling real-time data processing and analysis at the edge of the network. This thesis explores the application of edge computing in smart retail, where it can enhance various aspects of the retail ecosystem such as inventory management, customer engagement, and supply chain optimization. The integration of edge computing with IoT devices, sensors, and cameras in retail stores can enable retailers to make informed decisions quickly and provide personalized customer experiences.

Chapter 1 provides an introduction to the research topic, outlining the background, problem statement, objectives, scope, limitations, significance, structure of the thesis, and definitions of terms. Chapter 2 presents a comprehensive literature review on edge computing, focusing on its evolution, architectures, applications, benefits, challenges, technologies, case studies, integration with other technologies, and future trends in smart retail.

Chapter 3 delves into the system design and methodology, discussing the research framework, methodology, data collection methods, analysis techniques, system architecture design, development of edge computing solutions, performance evaluation metrics, validation, and testing procedures. Chapter 4 explores the system implementation process, including hardware and software requirements, infrastructure setup, algorithm implementation, data integration, system integration, deployment, monitoring, performance optimization, scalability, and security considerations.

Chapter 5 concludes the thesis with a summary of findings, contributions to the field, implications for practice, recommendations for future research, and a conclusion on the overall study. This thesis aims to provide valuable insights into the application of edge computing in smart retail and contribute to the body of knowledge in this rapidly evolving field.

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