Maximum Entropy-Based Auto Drift Correction Using High- and Low-Precision Sensors

Abstract

With the advancement in the Internet of Things (IoT) technologies, variety of sensors including inexpensive, low-precision sensors with sufficient computing and communication capabilities are increasingly deployed for monitoring large geographical areas. One of the problems with the use of inexpensive sensors is that they often suffer from random or systematic errors such as drift. The sensor drift is the result of slow changes that occur in the measurement driven by aging, loss of calibration, and changes in the phenomena being monitored over a time period. These drifting sensors need to be calibrated automatically for continuous and reliable monitoring. Existing methods for drift detection and correction do not consider the measurement errors or uncertainties present in those inexpensive low-precision sensors, hence, resulting in unreliable drift estimates. In this article, we propose a novel framework to automatically detect and correct the drifts by employing Bayesian Maximum Entropy (BME) and Kalman filtering (KF) techniques. The BME method is a spatiotemporal estimation method that incorporates the measurement errors of low-precision sensors as interval quantities along with the high-precision sensor measurements in their computations. Our scheme can be implemented in a centralized as well as in a distributed manner to detect and correct the drift generated in the sensors. For the centralized scheme, we compare several Kriging-based estimation techniques in combination with KF, and show the superiority of our proposed BME-based method in detecting and correcting the drift. We also propose a multivariate BME framework for drift detection, in which multiple features can be used to improve the drift estimates. To demonstrate the applicability of our distributed approach on a real-world application scenario, we implemented our algorithm on each wireless sensor node in order to perform in-network drift detection. The evaluation on real IoT datasets gathered from an indoor and an outdoor deployments reveal the superiority of our method in correctly identifying and correcting the drifts that develop in the sensors, in real time, compared to the existing approaches in the literature.