TRIC: Triage Compressed Data at the Edge for Efficient Data Transmission in Anomaly Detection
Francesco Taurone, Jonas Dorsch, Qi Zhang, Daniel E. Lucani · 2024
As the usage of the Internet of Things (IoT) has spread across various sectors, the amount of IoT data generated has increased dramatically. A large fraction of IoT applications focuses on monitoring significant signals and events, using anomaly detection methods to identify and react to unusual activity. This is typically done by continuously transferring massive amounts of sensor data to the Cloud, where algorithms are run, and typically results in large communication, computing and storage loads with large bills to pay for these Cloud resources. This work presents a unique preprocessing stage that can select small chunks of the datasets worth forwarding for further analysis, resulting in significant reductions in cloud processing time and transmission costs. We compare the results of the proposed technique with two other alternative methods, using three datasets and four anomaly detectors. We show that our method outperforms the others in almost all scenarios, it reduces transmission and Cloud processing costs as much as 100 times while achieving comparable detection performances.