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

Abstract

Missing sensor data is a common problem associated with the Internet of Things (IoT) ecosystems, which affect the accuracy of the 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 solve this issue. However, irregular temporal missing data locations is 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 the missing sensor data. Our approach; Bayesian Gaussian Process (BGaP); can impute missing data efficiently at any missing rate and for any temporal location using prior knowledge gathered on 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 the missing data which allowed us to observe long-term behavior changes that we would not have been able to observe otherwise.INDEX TERMS Missing Sensor Data, Missing Data Imputation, Bayesian Gaussian Process, Long-term Older adults Behavior Monitoring.

Artificial neural network-based data imputation for handling anomalous energy consumption readings in smart homes

“…An imputation method relying on the matrix profile distance was implemented for the IoT big data analysis (Lee et al, 2021). A Bayesian Gaussian imputation approach was discussed for IoT sensor data (Ahmed et al, 2022). A two-stage deep autoencoder and context encoder techniques were proposed to handle missing values in wind farm data (Liu and Zhang, 2021; Liao et al, 2022).…”