The Role of Probabilistic Schemes in Multisensor Context-Awareness

Author

Dargie, Waltenegus

Author_Institution

Dept. of Comput. Networks, Dresden Tech. Univ.

fYear

2007

fDate

19-23 March 2007

Firstpage

27

Lastpage

32

Abstract

This paper investigates the role of existing "probabilistic" schemes to reason about various everyday situations on the basis of data from multiple heterogeneous physical sensors. The schemes we discuss are fuzzy logic, hidden Markov models, Bayesian networks, and Dempster-Schafer theory of evidence. The paper also presents a conceptual architecture and identifies the suitable scheme to be employed by each component of the architecture. As a proof-of-concept, we will introduce the architecture we implemented to model various places on the basis of data from temperature, light intensity and relative humidity sensors

Keywords

belief networks; fuzzy logic; hidden Markov models; probability; sensor fusion; Bayesian networks; Dempster-Schafer theory; fuzzy logic; hidden Markov models; multisensor context-awareness; probabilistic schemes; Bayesian methods; Computer architecture; Computer networks; Context-aware services; Fuzzy logic; Hidden Markov models; Humans; Humidity; Sensor phenomena and characterization; Temperature sensors; Context; Context Modelling; Context Reasoning; Context-Aware Computing;

fLanguage

English

Publisher

ieee

Conference_Titel

Pervasive Computing and Communications Workshops, 2007. PerCom Workshops '07. Fifth Annual IEEE International Conference on

Conference_Location

White Plains, NY

Print_ISBN

0-7695-2788-4

Type

conf

DOI

10.1109/PERCOMW.2007.115

Filename

4144788