Techniques for Managing Data Imbalance and Detecting Anomalies in IoT Data
J Veena Rathna Augesteelia · 2024
The proliferation of IOT systems has introduced new challenges in data management, notably concerning data imbalance and anomaly detection. This chapter provides a comprehensive examination of techniques for addressing data imbalance in IoT environments and enhancing anomaly detection capabilities. Data imbalance arises from the disproportionate representation of classes within IoT datasets, leading to skewed model performance and operational inefficiencies. The dynamic nature of IoT data, characterized by temporal and spatial variations, further complicates these challenges. This chapter explores various strategies for mitigating data imbalance, including resampling techniques, algorithmic adjustments, and hybrid approaches that combine multiple methods for more effective results. Additionally, it delves into advanced anomaly detection techniques, emphasizing the integration of statistical methods and machine learning approaches to improve the identification of rare but critical events. By addressing both data imbalance and anomaly detection, this chapter aims to advance the development of robust and adaptive IoT systems capable of maintaining high performance in complex and evolving environments.