Title: Reliable context capturing for smart offices using a sensor network

Authors: Hiroto Aida, Jin Nakazawa, Hideyuki Tokuda

Addresses: Graduate School of Media and Governance, Keio University, Fujisawa, Kanagawa, Japan. ' Graduate School of Media and Governance, Keio University, Fujisawa, Kanagawa, Japan. ' Faculty of Environment and Information Studies, Keio University, Fujisawa, Kanagawa, Japan

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.

Keywords: wireless sensor networks; WSNs; Bayesian networks; air conditioning; smart offices; context classification; digital offices; wireless networks; Bayesian inference; posterior probability tables; disruption tolerance; air conditioner control; comfort level; air conditioners.

DOI: 10.1504/IJAHUC.2011.040117

International Journal of Ad Hoc and Ubiquitous Computing, 2011 Vol.7 No.3, pp.174 - 183

Published online: 18 May 2011 *

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