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

Ioannis Mavromatis, Adrian Sanchez-Mompo, Francesco Raimondo, James Pope, Marcello Bullo, Ingram Weeks, Vijay Kumar, Pietro Carnelli, George Oikonomou, Theodoros Spyridopoulos, Aftab Khan

Research output: Contribution to conferenceConference Paperpeer-review

3 Citations (Scopus)


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.
Original languageEnglish
Number of pages9
Publication statusPublished - 17 Mar 2023
EventIEEE Consumer Communications & Networking Conference - Las Vegas, United States
Duration: 8 Jan 202311 Jan 2023


ConferenceIEEE Consumer Communications & Networking Conference
Abbreviated titleCCNC 2023
Country/TerritoryUnited States
CityLas Vegas
Internet address

Bibliographical note

Funding Information:
This work was supported in part by Toshiba Europe Ltd. and in part by the SYNERGIA project (grant no. 53707, UK

Publisher Copyright:
© 2023 IEEE.


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