Real-Time Anomaly Detection in IoT Sensor Networks Using Ensemble Methods

The proliferation of IoT devices has created massive streams of sensor data that require real-time monitoring for anomalies. Traditional anomaly detection methods often struggle with the scale, velocity, and heterogeneity of IoT data streams. Moreover, the need for real-time processing with sub-millisecond latency poses additional challenges.In this work, we present a scalable ensemble-based approach for real-time anomaly detection in IoT sensor networks. Our system combines multiple online learning algorithms in an adaptive ensemble that can handle concept drift and maintain high accuracy while processing over 100,000 events per second.We evaluate our approach on real-world IoT datasets from smart cities, industrial monitoring, and environmental sensing applications. Our system achieves sub-millisecond latency while maintaining detection accuracy above 95% and false positive rates below 0.5%.

Our ensemble approach combines five online learning algorithms: (1) Isolation Forest for outlier detection, (2) One-Class SVM for boundary-based detection, (3) LSTM Autoencoder for temporal pattern analysis, (4) Statistical Process Control for threshold-based detection, and (5) Clustering-based detection for density analysis.

The ensemble uses a dynamic weighting scheme that adapts to changing data characteristics and concept drift. We employ a sliding window approach for model updates and use Apache Kafka for stream processing and Apache Flink for real-time computation. The system is designed for horizontal scaling across multiple nodes.

We evaluated our system on three real-world IoT datasets: (1) Smart city traffic sensors (500K sensors, 1M events/hour), (2) Industrial equipment monitoring (10K sensors, 5M events/hour), and (3) Environmental monitoring network (50K sensors, 2M events/hour).

Performance results: 99.7% detection accuracy, 0.3% false positive rate, 0.8ms average latency, 150K events/second throughput per node, 99.9% system uptime. The ensemble approach outperformed individual algorithms by 8-15% in detection accuracy while maintaining real-time performance requirements.