Data fusion as source for the generation of useful knowledge in context-aware systems

Issue title: Intelligent and Fuzzy Systems applied to Language & Knowledge Engineering

Guest editors: David Pinto, Vivek Kumar Singh, Aline Villavicencio, Philipp Mayr-Schlegel and Efstathios Stamatatos

Article type: Research Article

Authors: Muñoz, Julio^{a; *} | Molero-Castillo, Guillermo^{a; b} | Benítez-Guerrero, Edgard^a | Bárcenas, Everardo^{a; b}

Affiliations: [a] Facultad de Estadística e Informática, Universidad Veracruzana, Av. Xalapa s/n, Obrero Campesina 91020, Xalapa, Veracruz, Mexico | [b] CONACYT-Universidad Veracuzana, Av. Xalapa s/n, Obrero Campesina 91020, Xalapa, Veracruz, Mexico

Correspondence: [*] Corresponding author. Julio Muñoz, Facultad de Estadística e Informática, Universidad Veracruzana, Av. Xalapa s/n, Obrero Campesina 91020, Xalapa, Veracruz, Mexico. E-mail: juliomunoz@uv.mx.

Note: [1] WMO – World Meteorogical Organization stablished in 1950.

Note: [2] ASHRAE, American Society of Heating, Refrigerating and Air-Conditioning Engineers.

Abstract: Nowadays, context-aware systems use data obtained from various sources to adapt and provide services of interest to users according to their needs, location or interaction with the corresponding environment. However, the use of heterogeneous sources creates a huge amount of data that may differ in format, transmission speed and may be affected by environmental noise. This generates some inconsistency in data, which must be detected in time to avoid erroneous analysis. This is done using data fusion, which is the action for integrating diverse sources to be analyzed according to a given context. In this work, we propose a scheme of data fusion of heterogeneous sources, supported by a distributed architecture and Bayesian inference as fusion method. As a practical experiment, data were collected from three DHT22 sensors, whose measurements were relative humidity and temperature. The purpose of the experiment was to analyze the variation of these measurements over 24 hours, and fusion them to obtain integrated data. This proposed of data fusion represents an important field of action for the knowledge generation of interest in context-aware systems, for example for the analysis of the environment in order to take advantage of the use of energy and provide a comfortable working environment for the users.

Keywords: Bayesian Inference, context-aware systems, data fusion, data inconsistency, knowledge generation

DOI: 10.3233/JIFS-169500

Journal: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 3165-3176, 2018