Abstract
Wireless Sensor Networks used in aquatic environments for continuous monitoring are typically subject to physical or environmental factors that create anomalies in collected data. A possible approach to identify and correct these anomalies, hence to improve the quality of data, is to use artificial neural networks, as done by the previously proposed ANNODE (Artificial Neural Network-based Outlier Detection) framework [1].
In this paper we propose ANNODE+, which extends the ANNODE framework by detecting missing data in addition to outliers. We also describe the design and implementation of ANNODE+, implemented in Python to exploit readily available machine learning (ML) tools and libraries, also allowing online processing of incoming measurements. To evaluate the ANNODE+ capabilities, we used a dataset from a sensor deployment in Seixal's bay, Portugal. This dataset includes measurements of water level, temperature and salinity. We observed that our implementation of ANNODE+ performed as intended, being able to detect injected anomalies and successfully correcting them.
- G. Jesus, A. Casimiro, and A. Oliveira, "Using machine learning for dependable outlier detection in environmental monitoring systems," ACM Trans. Cyber-Phys. Syst., vol. 5, jul 2021.Google Scholar
- B. O'Flyrm, R. Martinez, J. Cleary, C. Slater, F. Regan, D. Diamond, and H. Murphy, "Smartcoast: A wireless sensor network for water quality monitoring," pp. 815 -- 816, 11 2007.Google Scholar
- G. Jesus, A. Casimiro, and A. Oliveira, "A survey on data quality for dependable monitoring in wireless sensor networks," Sensors, vol. 17, no. 9, p. 2010, 2017.Google Scholar
Cross Ref
- H. Teh, A. Kempa-Liehr, and K.Wang, "Sensor data quality: a systematic review," Journal of Big Data, vol. 7, 02 2020.Google Scholar
Cross Ref
- J. Gomes, M. Rodrigues, A. Azevedo, G. Jesus, J. Rogeiro, and A. Oliveira, "Managing a coastal sensors network in a nowcast-forecast information system," in 2013 Eighth International Conference on Broadband and Wireless Computing, Communication and Applications, pp. 518--523, IEEE, 2013.Google Scholar
- B. Brentan, G. Meirelles, M. Herrera, E. Luvizotto Jr, and J. Izquierdo, "Correlation analysis of water demand and predictive variables for short-term forecasting models," Mathematical Problems in Engineering, vol. 2017, 12 2017.Google Scholar
Cross Ref
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