Lightweight Machine Learning Techniques for Real-Time Anomaly Detection in IoT Sensor Networks

The explosive growth of Internet of Things (IoT) deployments has led to the proliferation of sensor networks that generate vast volumes of real-time data. Ensuring the integrity and reliability of this data is critical, particularly in domains such as industrial automation, smart cities, and environmental monitoring. However, traditional anomaly detection methods often struggle to keep pace with the resource-constrained and latency-sensitive nature of IoT environments. This study explores a range of lightweight machine learning (ML) techniques tailored for efficient, real-time anomaly detection in IoT sensor networks. By evaluating algorithms such as decision trees, one-class SVMs, k-nearest neighbors, and incremental learning models, we identify approaches that strike a balance between computational efficiency and detection accuracy. We also introduce a streamlined evaluation framework that simulates real-world conditions, highlighting the trade-offs between energy consumption, processing overhead, and response time. Our findings suggest that hybrid and adaptive models, particularly those that integrate online learning with local edge processing, offer a promising path forward. This research contributes practical insights for deploying scalable, trustworthy anomaly detection mechanisms in diverse IoT applications, where resource efficiency and rapid response are paramount.