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The growing number of IoT devices and their use to monitor the operation ofmachines and equipment increases interest in anomaly detection algorithmsrunning on devices. However, the difficulty is the limitations of the availablecomputational and memory resources on the devices. In the case ofmicrocontrollers (MCUs), these are single megabytes of program and severalhundred kilobytes of working memory. Consequently, algorithms must beappropriately matched to the capabilities of the devices. In the paper, weanalyse the processing pipeline for anomaly detection and implementation of theLocal Outliner Factor (LOF) algorithm on a MCU. We also show that it ispossible to train such an algorithm directly on the device, which gives greatpotential to use the solution in real devices.

Online Anomaly Detection Based On Reservoir Sampling and LOF for IoT devices