ArticleSensors (Basel, Switzerland)2020
Towards Outlier Sensor Detection in Ambient Intelligent Platforms-A Low-Complexity Statistical Approach.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Lost in translation? Conceptions of privacy and independence in the technical development of AI-based AAL.Medicine, health care, and philosophy · 2023Article
- Prediction-Correction Techniques to Support Sensor Interoperability in Industry 4.0 Systems.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors.
Funding
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Abstract
Sensor networks in real-world environments, such as smart cities or ambient intelligent platforms, provide applications with large and heterogeneous sets of data streams. Outliers-observations that do not conform to an expected behavior-has then turned into a crucial task to establish and maintain secure and reliable databases in this kind of platforms. However, the procedures to obtain accurate models for erratic observations have to operate with low complexity in terms of storage and computational time, in order to attend the limited processing and storage capabilities of the sensor nodes in these environments. In this work, we analyze three binary classifiers based on three statistical prediction models-ARIMA (Auto-Regressive Integrated Moving Average), GAM (Generalized Additive Model), and LOESS (LOcal RegrESSion)-for outlier detection with low memory consumption and computational time rates. As a result, we provide (1) the best classifier and settings to detect outliers, based on the ARIMA model, and (2) two real-world classified datasets as ground truths for future research.
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