PSFQ: a reliable transport protocol for wireless sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
A Survey of Context-Aware Mobile Computing Research
A Survey of Context-Aware Mobile Computing Research
An adaptive energy-efficient MAC protocol for wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Versatile low power media access for wireless sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Learning Bayesian Networks
A new community-based context distribution approach for large-scale pervasive systems
International Journal of Ad Hoc and Ubiquitous Computing
A new community-based context distribution approach for large-scale pervasive systems
International Journal of Ad Hoc and Ubiquitous Computing
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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.