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Fault detection for binary sensors in smart home environments

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binarysensorfaults_15.pdf (2.923Mb)
Date
23/03/2015
Author
Ye, Juan
Stevenson, Graeme
Dobson, Simon
Keywords
Wireless sensor network
Fault detection
Activity recognition
Ontologies
QA75 Electronic computers. Computer science
NDAS
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Abstract
Experiments in assisted living confirm that such systems can provide context-aware services that enable occupants to remain active and independent. They also demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a technique that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets that include multiple individuals, and show consistent rates of anomaly detection across different environments.
Citation
Ye , J , Stevenson , G & Dobson , S 2015 , Fault detection for binary sensors in smart home environments . in 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom) . IEEE Computer Society , pp. 20-28 , IEEE International Conference on Pervasive Computing and Communications (PerCom 2015) , St Louis, Missouri , United States , 23/03/15 . https://doi.org/10.1109/PERCOM.2015.7146505
 
conference
 
Publication
2015 IEEE International Conference on Pervasive Computing and Communications (PerCom)
DOI
https://doi.org/10.1109/PERCOM.2015.7146505
Type
Conference item
Rights
© 2015. IEEE. This is the accepted mansucript of a conference paper originally submitted to the IEEE International Conference on Pervasive Computing and Communications, Fault detection for binary sensors in smart home environments Ye, J., Stevenson, G. & Dobson, S. 23 Mar 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom 2015). IEEE Computer Society, available from http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000551
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  • Computer Science Research
  • University of St Andrews Research
URL
http://www.percom.org/
URI
http://hdl.handle.net/10023/6588

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