2006 10th IEEE International Symposium on Wearable Computers (2006)
Oct. 11, 2006 to Oct. 14, 2006
Eleftheria Katsiri , DSE, Imperial College London, 180, Queens Gate, London SW7 2AZ, UK. Email: email@example.com
Alan Mycroft , Computer Laboratory, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK. Email: firstname.lastname@example.org
This paper discusses a middleware component, the likelihood estimation service (LES), that allows the application of Bayesian reasoning to a real sensor-driven environment. Using LES, first, a Bayesian network is learned from location data. Once trained, the network is used in order to estimate the likelihood of users' spatio-temporal properties, such as the likelihood of their sighting in specific rooms. The learning algorithm is evaluated by calculating a confidence level. The output of the system is a first-order-logic predicate that is maintained in the SCAFOS middleware as approximate knowledge, even when the sensors fail.
A. Mycroft and E. Katsiri, "Applying Bayesian Networks to Sensor-Driven Systems," 2006 10th IEEE International Symposium on Wearable Computers(ISWC), Montreux, Switzerland, 2006, pp. 149-150.