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Open AccessArticle

Dynamic Field Monitoring Based on Multitask Learning in Sensor Networks

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Department of Industrial Engineering and Management, Peking University, Beijing 100871, China
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Author to whom correspondence should be addressed.
Sensors 2019, 19(7), 1533; https://doi.org/10.3390/s19071533
Received: 27 February 2019 / Revised: 19 March 2019 / Accepted: 21 March 2019 / Published: 29 March 2019
(This article belongs to the Special Issue Multivariate Data Analysis for Sensors and Sensor Arrays)
Field monitoring serves as an important supervision tool in a variety of engineering domains. An efficient monitoring would trigger an alarm timely once it detects an out-of-control event by learning the state change from the collected sensor data. However, in practice, multiple sensor data may not be gathered appropriately into a database for some unexpected reasons, such as sensor aging, wireless communication failures, and data reading errors, which leads to a large number of missing data as well as inaccurate or delayed detection, and poses a great challenge for field monitoring in sensor networks. This fact motivates us to develop a multitask-learning based field monitoring method in order to achieve an efficient detection when considerable missing data exist during data acquisition. Specifically, we adopt a log likelihood ratio (LR)-based multivariate cumulative sum (MCUSUM) control chart given spatial correlation among neighboring regions within the monitored field. To deal with the missing data problem, we integrate a multitask learning model into the LR-based MCUSUM control chart in the sensor network. Both simulation and real case studies are conducted to validate our proposed approach and the results show that our approach can achieve an accurate and timely detection for an out-of-control state when a large number of missing data exist in the sensor database. Our model provides an effective field monitoring strategy for engineering applications to accurately and timely detect the products with abnormal quality during production and reduce product losses. View Full-Text
Keywords: multitask learning; field monitoring; missing data; cumulative sum (CUSUM) control chart multitask learning; field monitoring; missing data; cumulative sum (CUSUM) control chart
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Wang, D.; Zhang, X. Dynamic Field Monitoring Based on Multitask Learning in Sensor Networks. Sensors 2019, 19, 1533.

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