Next Article in Journal
All-Fiber Measurement of Surface Tension Using a Two-Hole Fiber
Next Article in Special Issue
A New Architecture Based on IoT and Machine Learning Paradigms in Photovoltaic Systems to Nowcast Output Energy
Previous Article in Journal
Regional Optimization Dynamic Algorithm for Node Placement in Wireless Sensor Networks
Previous Article in Special Issue
A Meta-Model Integration for Supporting Knowledge Discovery in Specific Domains: A Case Study in Healthcare
Open AccessArticle

Towards Outlier Sensor Detection in Ambient Intelligent Platforms—A Low-Complexity Statistical Approach

1
ETSI de Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain
2
ETSI Sistemas Informáticos, Universidad Politécnica de Madrid, Calle de Alan Turing s/n, 28031 Madrid, Spain
3
ETSI en Topografía, Geodesia y Cartografía, Universidad Politécnica de Madrid, Camino de la Arboleda s/n, 28031 Madrid, Spain
*
Author to whom correspondence should be addressed.
This manuscript is an extended version of the conference paper “Automatic detection of erratic sensor observations in AmI platforms: A statistical approach” presented at the 13th International Conference on Ubiquitous Computing and Ambient Intelligence UCAmI 2019, Toledo, Spain, 2–5 December 2019.
Sensors 2020, 20(15), 4217; https://doi.org/10.3390/s20154217
Received: 30 June 2020 / Revised: 23 July 2020 / Accepted: 24 July 2020 / Published: 29 July 2020
(This article belongs to the Special Issue Selected Papers from UCAmI 2019)
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. View Full-Text
Keywords: abnormal data; Ambient Intelligence platform; binary classifier; outlier detection; prediction model; sensor abnormal data; Ambient Intelligence platform; binary classifier; outlier detection; prediction model; sensor
Show Figures

Figure 1

MDPI and ACS Style

Martín, D.; Fuentes-Lorenzo, D.; Bordel, B.; Alcarria, R. Towards Outlier Sensor Detection in Ambient Intelligent Platforms—A Low-Complexity Statistical Approach. Sensors 2020, 20, 4217.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop