triaGeD: using compression for anomaly detection

IoT applications often require devices to continuously send huge amounts of sensor data to the cloud in order to detect anomalies. This paper proposes a novel preprocessing stage selecting small portions of the sensor data worth sending for further analysis, resulting in significant savings in transmission costs and processing time in the cloud, down to less than 1% of the complete stream, while achieving comparable detection results.