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sCalib: A Warehouse Sensor Fault Detection and Self-Calibration Technique for Sustainable IoT

The utility of the deployment of IoT sensor nodes in Smart Cities is mainly dependent on the accurate data generated by the sensors. Hence, automatic detection of sensor faults and self-calibration of the faulty sensors is essential for the uninterrupted operation of Sustainable IoT. This paper presents the sCalib technique for this purpose, considering the temperature sensors in a warehouse as an example. Suppose one of the sensors in the warehouse becomes inaccurate due to drift or similar reasons; in that case, sCalib can identify the fault and automatically calibrate the faulty sensor using another non-faulty sensor, which is placed nearby, as a standard sensor. The fault identification module was trained using several Machine Learning algorithms, among which the K Nearest Neighbours (KNN) produced highest accuracy results. The calibration module uses a straightforward algorithm, has low computational complexity and is capable of calibrating the sensor in a few minutes. This mechanism uses edge as a service for automatic fault detection and self-calibration and ensures that the deployed sensors can work for a long time without external interference.