Fusing sensor information for location estimation

Pervasive computing is an emerging computing paradigm that provides new improved services for everyone, everywhere and at all times. In this paper* we discuss a system which exploits data streams derived from sensors, in order to accurately estimate a key factor for pervasive computing and context aware applications: the location of a user. The term “sensors” includes Wi-Fi adapters, IR receivers, RFID tag readers, etc. The core of the system is the fusion engine which is based on Dynamic Bayesian Networks (DBNs), a powerful mathematical tool for integrating heterogeneous sensor observations. In closing, we provide an evaluation of the system as it comes out from the experimental results.

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