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Sensors 2017, 17(3), 491; doi:10.3390/s17030491

Dynamic Context-Aware Event Recognition Based on Markov Logic Networks

School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
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Academic Editors: Ioannis Kompatsiaris, Thanos G. Stavropoulos and Antonis Bikakis
Received: 6 January 2017 / Revised: 23 February 2017 / Accepted: 25 February 2017 / Published: 2 March 2017
(This article belongs to the Special Issue Sensors for Ambient Assisted Living, Ubiquitous and Mobile Health)
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Abstract

Event recognition in smart spaces is an important and challenging task. Most existing approaches for event recognition purely employ either logical methods that do not handle uncertainty, or probabilistic methods that can hardly manage the representation of structured information. To overcome these limitations, especially in the situation where the uncertainty of sensing data is dynamically changing over the time, we propose a multi-level information fusion model for sensing data and contextual information, and also present a corresponding method to handle uncertainty for event recognition based on Markov logic networks (MLNs) which combine the expressivity of first order logic (FOL) and the uncertainty disposal of probabilistic graphical models (PGMs). Then we put forward an algorithm for updating formula weights in MLNs to deal with data dynamics. Experiments on two datasets from different scenarios are conducted to evaluate the proposed approach. The results show that our approach (i) provides an effective way to recognize events by using the fusion of uncertain data and contextual information based on MLNs and (ii) outperforms the original MLNs-based method in dealing with dynamic data. View Full-Text
Keywords: event recognition; sensing data; information fusion; Markov logic networks; dynamic uncertainty event recognition; sensing data; information fusion; Markov logic networks; dynamic uncertainty
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Liu, F.; Deng, D.; Li, P. Dynamic Context-Aware Event Recognition Based on Markov Logic Networks. Sensors 2017, 17, 491.

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