LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices

• DOI: 10.48550/arxiv.2211.01840
• Publication Date: 2023-01-08
• Authors: Mavromatis, Ioannis; Sanchez-Mompo, Ad...; Raimondo, Francesco; Pope, James; Bullo, Marcello; Weeks, Ingram; Kumar, Vijay; Carnelli, Pietro; Oikonomou, George; Spyridopoulos, Th...; Khan, Aftab
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Cryptography and Security; Computer Science - Distributed, Parallel, and Cluster Computing; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Distributed, Parallel, and Cluster Computing (cs.DC); Cryptography and Security (cs.CR); Machine Learning (cs.LG)

IEEE CCNC 2023, Las Vegas, USA<br/>Data integrity becomes paramount as the number of Internet of Things (IoT) sensor deployments increases. Sensor data can be altered by benign causes or malicious actions. Mechanisms that detect drifts and irregularities can prevent disruptions and data bias in the state of an IoT application. This paper presents LE3D, an ensemble framework of data drift estimators capable of detecting abnormal sensor behaviours. Working collaboratively with surrounding IoT devices, the type of drift (natural/abnormal) can also be identified and reported to the end-user. The proposed framework is a lightweight and unsupervised implementation able to run on resource-constrained IoT devices. Our framework is also generalisable, adapting to new sensor streams and environments with minimal online reconfiguration. We compare our method against state-of-the-art ensemble data drift detection frameworks, evaluating both the real-world detection accuracy as well as the resource utilisation of the implementation. Experimenting with real-world data and emulated drifts, we show the effectiveness of our method, which achieves up to 97% of detection accuracy while requiring minimal resources to run.<br/>