SNRepair: Systematically Addressing Sensor Faults and Self-Calibration in IoT Networks
Aparna Sinha, Debanjan Das
Abstract
An accurate and robust technique for sensor fault diagnosis proves useful for an uninterrupted supply of correct monitoring data across the Internet-of-Things (IoT) network. The manual checking and calibration of thousands of sensors deployed in the IoT network is a challenging task. Furthermore, most calibration techniques require additional hardware support for calibration. To address these issues, a unique IoT-based framework, SNRepair, has been proposed, which uses modified deep reinforcement learning (DRL) for detecting different types of sensor faults, such as bias, drift, complete failure (CF), and precision degradation (PD). This technique is also capable of self-calibrating the faulty sensors automatically using the historical healthy data of the same sensor. The fault detection module identifies four types of sensor faults with 96.17% accuracy. The self-calibration module can calibrate the faulty sensors within a few seconds, thereby ensuring the uninterrupted availability of accurate monitoring information. The proposed model is robust as it can work efficiently even in noisy environments with accurate results.