Dependable Outlier Detection in Harsh Environments Monitoring Systems
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Environmental monitoring systems are composed by sensor networks deployed in uncertain and harsh conditions, vulnerable to external disturbances, posing challenges to the comprehensive system characterization and modelling. When unexpected sensor measurements are produced, there is a need to detect and identify, in a timely manner, if they stem from a failure behavior or if they indeed represent some environment-related process. Existing solutions for fault detection in environmental sensor networks do not portray the required sensitivity for the differentiation of these processes or they are unable to meet the time constraints of the affected cyber-physical systems.
We have been developing a framework for dependable detection of failures in harsh environments monitoring systems, aiming to improve the overall sensor data quality. Herein we present the application of an early framework implementation to an aquatic sensor network dataset, using neural networks to model sensors’ behaviors, correlated data between neighbor sensors, and a statistical technique to detect the presence of outliers in the datasets.
KeywordsDependability Data quality Outlier detection Machine learning Neural networks Water monitoring
The authors thank António Baptista and the CMOP SATURN team for their support in the Columbia river analysis. This work was partially supported by the FCT, through the LASIGE Research Unit, Ref. UID/CEC/00408/2013, PhD Grant SFRH/BD/82489/2011 and by H2020 WADI—EC Grant Agreement No. 689239.
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