Abstract
In the domain of ubiquitous computing, the ability to identify the occurrence of situations is a core function of being ’context-aware’. Given the uncertain nature of sensor information and inference rules, reasoning techniques that cater for uncertainty hold promise for enabling the inference process. In our work, we apply the Dempster Shafer theory of evidence to infer situation occurrence with minimal use of training data. We describe a set of evidential operations for sensor mass functions using context quality and evidence accumulation for continuous situation detection. We demonstrate how our approach enables situation inference with uncertain information using a case study based on a published smart home activity data set.
This work is partially supported by Enterprise Ireland under grant number CFTD 2005 INF 217a, “Platform for user-Centred design and evaluation of context-aware services” and by Science Foundation Ireland under grant numbers 07/CE/1147 “Clarity, the centre for sensor web technologies”, 03/CE2/I303-1 “Lero, the Irish Software Engineering Research Centre”, and 05/RFP/CMS0062 “Towards a semantics of pervasive computing”.
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McKeever, S., Ye, J., Coyle, L., Dobson, S. (2009). Using Dempster-Shafer Theory of Evidence for Situation Inference. In: Barnaghi, P., Moessner, K., Presser, M., Meissner, S. (eds) Smart Sensing and Context. EuroSSC 2009. Lecture Notes in Computer Science, vol 5741. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04471-7_12
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DOI: https://doi.org/10.1007/978-3-642-04471-7_12
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