SSIM - A Deep Learning Approach for Recovering Missing Time Series Sensor Data

02/26/2020
by   ivan, et al.
1

Missing data are unavoidable in wireless sensor networks, due to issues such as network communication outage, sensor maintenance or failure, etc. Although a plethora of methods have been proposed for imputing sensor data, limitations still exist. First, most methods give poor estimates when a consecutive number of data are missing. Second, some methods reconstruct missing data based on other parameters monitored simultaneously. When all the data are missing, these methods are no longer effective. Third, the performance of deep learning methods relies highly on a massive number of training data. Moreover in many scenarios, it is difficult to obtain large volumes of data from wireless sensor networks. Hence, we propose a new sequence-to-sequence imputation model (SSIM) for recovering missing data in wireless sensor networks. The SSIM uses the state-of-the-art sequence-to-sequence deep learning architecture, and the long short-term memory network is chosen to utilize both past and future information for a given time. Moreover, a variable-length sliding window algorithm is developed to generate a large number of training samples so the SSIM can be trained with small data sets. We evaluate the SSIM by using realworld time series data from a water quality monitoring network. Compared to methods like ARIMA, seasonal ARIMA, matrix factorization, multivariate imputation by chained equations, and expectation–maximization, the proposed SSIM achieves up to 69.2%, 70.3%, 98.3%, and 76% improvements in terms of the root mean square error, mean absolute error, mean absolute percentage error (MAPE), and symmetric MAPE, respectively, when recovering missing data sequences of three different lengths. The SSIM is therefore a promising approach for data quality control in wireless sensor networks.

READ FULL TEXT

page 1

page 4

page 8

page 11

research
02/25/2020

Sequence-to-Sequence Imputation of Missing Sensor Data

Although the sequence-to-sequence (encoder-decoder) model is considered ...
research
03/01/2022

Recovery of Missing Sensor Data by Reconstructing Time-varying Graph Signals

Wireless sensor networks are among the most promising technologies of th...
research
05/05/2020

Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images

Sufficient high-quality traffic data are a crucial component of various ...
research
03/02/2021

Missing Value Imputation on Multidimensional Time Series

We present DeepMVI, a deep learning method for missing value imputation ...
research
02/02/2023

Conditional expectation for missing data imputation

Missing data is common in datasets retrieved in various areas, such as m...
research
07/20/2023

Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstruction

Sensing is one of the most fundamental tasks for the monitoring, forecas...
research
10/07/2021

Predictive Maintenance for General Aviation Using Convolutional Transformers

Predictive maintenance systems have the potential to significantly reduc...

Please sign up or login with your details

Forgot password? Click here to reset