Exploring Effective Outlier Detection in IoT: A Systematic Survey of Techniques and Applications

This paper presents a survey of outlier detection techniques used in the analysis of IoT data. We conducted a comprehensive review of existing research studies and identified the most popular techniques for detecting outliers in IoT data, including statistical techniques, machine learning algorithms, and hybrid methods. We also discuss the challenges of IoT, such as data quality, scalability, and security issues, and highlight the importance of outlier detection in various IoT applications, such as healthcare, smart cities, and industrial automation. The results of our survey can be used as a useful resource for researchers and practitioners who are interested in exploring the state-of-the-art techniques in outlier detection for IoT data analysis as it's a very simple guide for any beginner.