  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.
