Work

Automatic Detection of Erratic Sensor Observations in Ami Platforms: A Statistical Approach †

Year: 2019

Type: conference-paper

Abstract: This paper addresses the problem of data aggregation platforms operating in heterogeneous Ambient Intelligence Environments. In these platforms, device interoperability is a challenge and erratic sensor observations are difficult to be detected. We propose ADES (Automatic Detection of Erratic Sensors), a statistical approach to detect erratic behavior in sensors and annotate those errors in a semantic platform. To do that, we propose three binary classification systems based on statistical tests for erratic observation detection, and we validate our approach by verifying whether ADES is able to classify sensors by its observations correctly. Results show that the first two classifiers (constant and random observations) had good accuracy rates, and they were able to classify most of the samples. In addition, all of the classifiers obtained a very low false positive rate.

Source: International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI)

DOI: 10.3390/proceedings2019031055

Authors: Diego Martín, Borja Bordel, Ramón Alcarria

Institutions: Universidad Politécnica de Madrid, Universidad Complutense de Madrid

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Ministerio de Economía y Competitividad

Award: G107954607