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

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.

Lost in translation? Conceptions of privacy and independence in the technical development of AI-based AAL

“…Empowering older adults to live an independent life is often not defined as an end itself, but as a crucial factor of an improved quality of life (e.g. Calderita et al 2020 ; Karakostas et al 2020 ; Martín et al 2020 ), well-being, or wellness of older adults (e.g. Grgurić et al 2019 ; Guerra et al 2020 ; Jovanov 2019 ).…”

Prediction-Correction Techniques to Support Sensor Interoperability in Industry 4.0 Systems

“…This time interval [n 1 , n 2 ] may refer to a past time period (11) (so we are performing an offline data curation), but we can also perform a real-time data curation if the current time instant n 0 belongs to the time interval [n 1 , n 2 ] under study (12). If operating in real time, the proposed curation solution is employed as a prediction mechanism for calculating future samples in advance.…”

“…Figure 1 shows the proposed algorithm to find that time series x * i [n], fulfilling the previous conditions (13), if it exists. (12). If operating in real time, the proposed curation solution is employed as a prediction mechanism for calculating future samples in advance.…”

“…and communication malfunctions (such as variables delays, jitter, packet losses, interferences, etc. ), among many other potential impacts [12]. Although all these malfunctions are not relevant if long-term analyses are performed (e.g., for statistical applications), they may cause a critical situation if an inadequate decision is made in real time based on a low-quality data flow.…”