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Introduction

Serverless computing is a cloud computing model in which the cloud provider manages the infrastructure needed to execute code, allowing developers to focus on writing and deploying functions without worrying about servers. Edge intelligence, on the other hand, refers to the processing and analysis of data closer to the source of data generation, such as sensors and IoT devices. Combining serverless computing with edge intelligence offers the potential to harness the power of cloud computing for real-time data processing and decision-making at the edge.

This thesis explores the use of serverless computing for edge intelligence applications, with a focus on the benefits, challenges, and best practices for implementing this technology. By leveraging the scalability and flexibility of serverless computing, organizations can overcome the limitations of traditional cloud computing models and build intelligent edge solutions that are agile, cost-effective, and scalable.

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 Overview of Serverless Computing
2.2 Edge Intelligence in IoT
2.3 Integration of Serverless Computing and Edge Intelligence
2.4 Advantages and Disadvantages of Serverless Computing for Edge Intelligence
2.5 Use Cases of Serverless Computing for Edge Intelligence
2.6 Best Practices for Implementing Serverless Computing at the Edge
2.7 Security and Privacy Concerns in Serverless Computing for Edge Intelligence
2.8 Performance Evaluation of Serverless Computing at the Edge
2.9 Comparison with Traditional Cloud Computing Models
2.10 Future Trends in Serverless Computing for Edge Intelligence

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Sampling Strategy
3.5 Research Instrument
3.6 Ethical Considerations
3.7 Pilot Study
3.8 Data Validity and Reliability

Chapter 4: Discussion of Findings
4.1 Analysis of Data
4.2 Comparison of Findings with Existing Literature
4.3 Implications for Practice
4.4 Recommendations for Future Research
4.5 Limitations of the Study
4.6 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusions
5.3 Recommendations
5.4 Contributions to Knowledge
5.5 Future Research Directions

Thesis Overview on Serverless Computing for Edge Intelligence

Serverless computing has emerged as a promising technology for edge intelligence applications, offering a scalable and cost-effective solution for processing real-time data at the edge. By abstracting away the infrastructure management and scaling capabilities, serverless computing allows developers to focus on building intelligent edge solutions without the constraints of traditional cloud computing models.

This thesis investigates the integration of serverless computing and edge intelligence, exploring the benefits, challenges, and best practices for implementing this technology. Through a comprehensive literature review, research methodology, discussion of findings, and conclusion, this thesis aims to provide insights into how organizations can leverage serverless computing for edge intelligence applications to drive innovation and competitive advantage in the digital age.

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