Long Gaps Missing IoT Sensors Time Series Data Imputation: A Bayesian Gaussian Approach

Abstract

Missing sensor data is a common problem associated with Internet of Things ecosystems, which affects the accuracy of associated services such as adequate medical intervention for older adults living at home. This problem is caused by many factors, power down is one of them, communication failure and sensor failure are another two reasons. Multiple missing data imputation methods have been developed to address this issue. However, irregular temporal missing data locations are challenging to handle, due to lack of knowledge of their occurrence probability and their random temporal location. In this paper, we propose a Bayesian Gaussian Process based imputation technique that accounts for temporal forcing to fill in missing sensor data. Our approach; Bayesian Gaussian Process (BGaP); can efficiently impute missing data at any missing rate and for any temporal location using prior knowledge gathered from past observations. We illustrated how our approach performs using real data collected from sensors deployed in the residence of 10 older adults over a two-year period. Using our novel approach, we were able to impute all missing data which allowed us to observe long-term behavior changes that we would not have been able to observe otherwise.