Paper Info
Title
Data fusion as source for the generation of useful knowledge in context-aware systems.
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.
Year
DOI
Venue
2018
10.3233/JIFS-169500
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
Field
DocType
Bayesian Inference,context-aware systems,data fusion,data inconsistency,knowledge generation
Sensor fusion,Artificial intelligence,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
34
5
1064-1246
Citations 
PageRank 
References 
0
0.34
18
Authors
4
Name
Order
Citations
PageRank
Julio Muñoz100.34
Guillermo Molero-Castillo200.34
Edgard Benítez-Guerrero3910.06
Everardo Bárcenas4239.15