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Introduction

The Internet of Things (IoT) has revolutionized the way we interact with our surroundings, allowing for seamless communication between devices and systems. With the proliferation of IoT devices, there has been an exponential increase in the amount of data generated by sensors. This data holds valuable insights that can be leveraged for various applications, ranging from smart home automation to industrial monitoring. However, this data also presents challenges, such as the detection of anomalies that may indicate a malfunction or security breach.

Anomaly detection in IoT sensor data is a critical research area that aims to identify unusual patterns or outliers in the data that may signify a potential threat or problem. This thesis will explore various techniques and methodologies for anomaly detection in IoT sensor data, with a focus on improving the reliability and efficiency of anomaly detection systems.

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 Introduction to Anomaly Detection
2.2 IoT Sensor Data Processing
2.3 Anomaly Detection Techniques
2.4 Machine Learning Algorithms for Anomaly Detection
2.5 Deep Learning Approaches to Anomaly Detection
2.6 Anomaly Detection in Industrial IoT
2.7 Anomaly Detection in Healthcare IoT
2.8 Anomaly Detection in Smart Home IoT
2.9 Challenges in Anomaly Detection in IoT
2.10 Future Directions in Anomaly Detection Research

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Selection
3.5 Model Selection
3.6 Model Training
3.7 Model Evaluation
3.8 Performance Metrics
3.9 Experimental Setup

Chapter 4: Discussion of Findings
4.1 Analysis of Anomaly Detection Techniques
4.2 Comparison of Machine Learning and Deep Learning Approaches
4.3 Case Studies in Anomaly Detection
4.4 Impact of Anomaly Detection in IoT Applications
4.5 Addressing Challenges in Anomaly Detection
4.6 Future Research Directions
4.7 Recommendations for Practitioners
4.8 Summary of Key Findings

Chapter 5: Conclusion and Summary
5.1 Summary of the Thesis
5.2 Contributions of the Study
5.3 Implications for Future Research
5.4 Concluding Remarks

Thesis Overview:

Anomaly detection in IoT sensor data is a crucial aspect of ensuring the reliability and security of IoT systems. This thesis aims to provide a comprehensive analysis of different techniques and methodologies for anomaly detection in IoT sensor data. The literature review will delve into the various approaches to anomaly detection, including machine learning algorithms and deep learning techniques. The research methodology section will outline the experimental design and evaluation metrics used to assess the effectiveness of different anomaly detection models. The discussion of findings chapter will present the results of the experiments and provide insights into the performance of various anomaly detection techniques. Finally, the conclusion and summary chapter will summarize the key findings of the study and offer recommendations for future research in this field.

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