CurrentSense

Abstract

Sensor data quality plays a fundamental role in increasing the adoption of IoT devices for environmental data collection. Due to the nature of the deployment, i.e., in-the-wild and in harsh environments, coupled with limitations of low-cost components, sensors are prone to failures. A significant fraction of faults result from drift and catastrophic faults in sensors' sensing components leading to serious data inaccuracies. However, it is challenging to detect faults by analyzing just the sensor data as a faulty sensor data can mimic non-faulty data and an anomalous sensor reading need not represent a faulty data. Existing data-centric approaches rely on additional contextual information or sensor redundancy to detect such faults. This paper presents a systematic approach to detect faults and drifts, by devising a novel sensor fingerprint called CurrentSense. CurrentSense captures the electrical characteristics of the hardware components in a sensor, with working, drifted, and faulty sensors having distinct fingerprints. This fingerprint is used to determine the sensors' health, and compensate for drift or diagnose catastrophic faults without any contextual information. The CurrentSense approach is non-intrusive, and can be applied to a wide variety of environmental sensors. We show the working of the proposed approach with the help of air pollution sensors. We perform an extensive evaluation in both controlled setup and real-world deployments with 51 sensors across multiple cities for 8 months period. Our approach outperforms existing anomaly detectors and can detect and isolate faults with an F1 score of 98% and compensate for sensor drift errors by 86%.