Bayesian maximum entropy and interacting multiple model based automatic sensor drift detection and correction in an IoT environment

Abstract

With the advancement in the Internet of Things (IoT) technologies, a 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 the drift that they develop over time. These drifting sensors need to be calibrated automatically for continuous and reliable monitoring. In this paper, we present a new methodology to automatically detect and correct both the smooth and steep drifts by employing Bayesian Maximum Entropy and Interacting Multiple Model based techniques. The evaluation on real IoT data gathered from an indoor and an outdoor deployment reveals the superiority and applicability of our method in correctly identifying and correcting the smooth and abrupt (sensor) drifts in the IoT environment.