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Introduction

Edge computing has emerged as a promising paradigm for real-time data processing in recent years. With the increasing demand for low latency and high performance in applications such as Internet of Things (IoT), autonomous vehicles, and smart cities, traditional cloud computing solutions are facing limitations in meeting these requirements. Edge computing, which pushes computing resources closer to the data source, has the potential to address these challenges by enabling real-time data processing at the edge of the network.

This thesis investigates the application of edge computing for real-time data processing. The research aims to explore the benefits and challenges of deploying edge computing solutions, develop a system for real-time data processing using edge computing, and evaluate its performance in different scenarios. By studying edge computing for real-time data processing, this research contributes to the growing body of knowledge in the field of distributed computing and provides insights into the potential of edge computing to enhance the efficiency and scalability of real-time data processing applications.

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 Edge Computing
2.2 Real-time Data Processing
2.3 Edge Computing Architectures
2.4 Edge Computing Applications
2.5 Challenges in Edge Computing
2.6 Edge Computing Technologies
2.7 Edge Computing Security
2.8 Edge Computing Performance
2.9 Edge Computing Scalability
2.10 Edge Computing Use Cases

Chapter 3: System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Processing
3.3 Edge Node Selection
3.4 Communication Protocols
3.5 Data Storage
3.6 Data Analytics
3.7 Real-time Processing Algorithms
3.8 Performance Evaluation

Chapter 4: System Implementation
4.1 Hardware and Software Requirements
4.2 Edge Node Configuration
4.3 Data Collection Setup
4.4 Data Processing Implementation
4.5 Communication Setup
4.6 Data Storage Implementation
4.7 Data Analytics Implementation
4.8 Real-time Processing Implementation

Chapter 5: Conclusion
5.1 Summary of Findings
5.2 Contribution to Knowledge
5.3 Future Research Directions
5.4 Conclusion

Thesis Overview on Edge Computing for Real-time Data Processing

Edge computing has gained significant attention in recent years as a technology that can revolutionize real-time data processing. By bringing computing resources closer to the data source, edge computing enables faster processing and reduced latency, making it a suitable solution for applications that require quick response times. This thesis aims to explore the potential of edge computing for real-time data processing and develop a system that demonstrates its capabilities in various scenarios.

The literature review in Chapter 2 provides an overview of edge computing, real-time data processing, edge computing architectures, applications, challenges, technologies, security, performance, scalability, and use cases. By synthesizing existing research in these areas, this chapter lays the foundation for the subsequent chapters on system design, methodology, implementation, and evaluation.

In Chapter 3, the system design and methodology are presented, detailing the architecture of the system, data collection and processing methods, edge node selection criteria, communication protocols, data storage, data analytics, real-time processing algorithms, and performance evaluation metrics. This chapter outlines the steps taken to design and develop a system that leverages edge computing for real-time data processing.

Chapter 4 focuses on the implementation of the system, including hardware and software requirements, edge node configuration, data collection setup, data processing implementation, communication setup, data storage implementation, data analytics implementation, and real-time processing implementation. By detailing the practical aspects of implementing an edge computing system, this chapter provides insights into the challenges and considerations involved in deploying edge computing solutions.

Finally, Chapter 5 concludes the thesis by summarizing the findings, highlighting the contribution to knowledge, suggesting future research directions, and concluding with key insights gained from the research. This thesis aims to contribute to the growing body of knowledge in edge computing for real-time data processing and provide a comprehensive overview of the potential of edge computing to enhance the efficiency and scalability of real-time data processing applications.

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