Fault Tolerant Regression for Sensor Data

Abstract

Many systems rely on predictive models using sensor data, with sensors being prone to occasional failures. From the operational point of view predictions need to be tolerant to sensor failures such that the loss in accuracy due to temporary missing sensor readings would be minimal. In this paper, we theoretically and empirically analyze robustness of linear predictive models to temporary missing data. We demonstrate that if the input sensors are correlated the mean imputation of missing values may lead to a very rapid deterioration of the prediction accuracy. Based on the theoretical results we introduce a quantitative measure that allows to assess how robust is a given linear regression model to sensor failures. We propose a practical strategy for building and operating robust linear models in situations when temporal sensor failures are expected. Experiments on six sensory datasets and a case study in environmental monitoring with streaming data validate the theoretical results and confirm the effectiveness of the proposed strategy.