13th International ERCIM/EWICS/ARTEMIS Workshop on Dependable Smart Embedded and Cyber-physical Systems and Systems-of-Systems (DECSoS 2018), Västerås, Sweden, September 2017.
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.
@inbook{Jesus:18a,
author = {Jesus, G., Casimiro, A. and Oliveira, A.},
title = {Dependable Outlier Detection in Harsh Environments Monitoring Systems},
bookTitle = {Computer Safety, Reliability, and Security - {SAFECOMP} 2018 Workshops,
ASSURE, DECSoS, SASSUR, STRIVE, WAISE,and ICCS, Trento, Italy, September
18, 2018, Proceedings},
pages = {224-233},
year = {2018},
doi = {https://doi.org/10.1007/978-3-319-99229-7_20},
abstractURL = {http://www.di.fc.ul.pt/~casim/papers/decsos18/decsos18.html},
documentURL = {http://www.di.fc.ul.pt/~casim/papers/decsos18/decsos18.pdf},
}