The Role of Probabilistic Schemes in Multisensor Context-Awareness

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

[1]  Mel Siegel,et al.  Sensor data fusion for context-aware computing using dempster-shafer theory , 2004 .

[2]  Harry Chen,et al.  Intelligent agents meet semantic web in a smart meeting room , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[3]  George J. Klir,et al.  Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems - Selected Papers by Lotfi A Zadeh , 1996, Advances in Fuzzy Systems - Applications and Theory.

[4]  Albrecht Schmidt,et al.  Multi-Sensor Context-Awareness in Mobile Devices and Smart Artifacts , 2002, Mob. Networks Appl..

[5]  Jani Mäntyjärvi,et al.  Managing Context Information in Mobile Devices , 2003, IEEE Pervasive Comput..

[6]  Jin Song Dong,et al.  Semantic Space: an infrastructure for smart spaces , 2004, IEEE Pervasive Computing.

[7]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[8]  David Garlan,et al.  Context is key , 2005, CACM.

[9]  David Heckerman,et al.  Bayesian Networks for Data Mining , 2004, Data Mining and Knowledge Discovery.

[10]  Vesa T. Peltonen,et al.  Computational auditory scene recognition , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  Tao Gu,et al.  Toward an OSGi-based infrastructure for context-aware applications , 2004, IEEE Pervasive Computing.

[12]  Gregory D. Abowd,et al.  Providing architectural support for building context-aware applications , 2000 .

[13]  Bill N. Schilit,et al.  Context-aware computing applications , 1994, Workshop on Mobile Computing Systems and Applications.

[14]  Stuart C. Shapiro,et al.  A Model for Belief Revision , 1988, Artif. Intell..

[15]  Johan Himberg,et al.  Collaborative context recognition for handheld devices , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..