Reliable context capturing for smart offices using a sensor network Online publication date: Wed, 18-May-2011
by Hiroto Aida, Jin Nakazawa, Hideyuki Tokuda
International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC), Vol. 7, No. 3, 2011
Abstract: In order to realise smart offices, which provides users with comfort via various digital services, we need to acquire context information about the target space. Contexts can be obtained from raw sensor data using context classification methods, such as Bayesian network. However, packet losses and disrupted communications in wireless sensor networks disables the context classification methods to collect all the necessary data, hence reduce quality of contexts. In this paper, we propose Reliable Hybrid Bayesian Inference Mechanism (RHBIM) that features in-network disruption-tolerant Bayesian inference with server-side calculation of Posterior Probability Tables. In this paper, we show the design and implementation of the mechanism with a range of disruption-tolerance schemes, and apply the mechanism to an application that controls air conditioners based on the ('comfort level') context. We also show the effectiveness of the mechanism comparing the different disruption-tolerance schemes.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook