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Online Cleaning of Wireless Sensor Data Resulting in Improved Context Extraction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 60 - Number 15 |
| Year of Publication: 2012 |
| Authors: Sangeeta Mittal, Alok Aggarwal, S. L. Maskara |
10.5120/9769-4326
|
Sangeeta Mittal, Alok Aggarwal, S. L. Maskara . Online Cleaning of Wireless Sensor Data Resulting in Improved Context Extraction. International Journal of Computer Applications. 60, 15 ( December 2012), 24-32. DOI=10.5120/9769-4326
Abstract
Wireless Sensors enable fine grain monitoring of activities of individual and social interest. Typically these sensors sense & send data continuously directly or through other sensor nodes to a base station. Wireless Sensor Data are inherently noisy and have frequent random spikes due to dynamic nature of the medium. Hence, the decision at the receiving node based on such data is likely to be erroneous. Erroneous data and decisions may affect its transformation to meaningful form like 'context'. It is therefore desirable to clean the data for improved context extraction. Bayesian Belief Networks are used here to quantitatively encode the dependencies among various sensors. These dependencies are then used to estimate missing data and also to detect and recover from errors. Cleaned data is then used for deriving Contextual Information and it results in improved context feature calculation. In this paper five algorithms for Bayesian Belief Network Construction have been evaluated and their performance of classification studied. Conjunctive rules are defined to map the sensors to already defined context. A secondary data obtained from weather sensor boards installed at Intel research lab at Berkeley have been used to demonstrate the approach.
References
- IST Advisory Group, Scenarios for Ambient Intelligence in 2010, European Commission, 2001.
- A. Subramanya,A. Raj,J. Bilmes, and D. Fox, " Recognizing activities and spatial context using wearable sensors," In proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (UAI), 2006.
- Claudio Bettini, Oliver Brdiczka, Karen Henricksen, Jadwiga Indulska, Daniela Nicklas, Anand Ranganathan, and Daniele Riboni, " A survey of context modelling and reasoning techniques", Pervasive Mobile Computing 6,( 2), April 2010, 161-180.
- Mittal, S. ; Aggarwal, A. ; Maskara, S. L. , Application of Bayesian Belief Networks for context extraction from wireless sensors data , In Proceedings of 14th International Conference on Advanced Communication Technology (ICACT),2012 , Page(s): 410 - 415 ,2012.
- J. Cheng and R. Greiner, Learning Bayesian Belief Network Classifiers: Algorithms and System, Lecture Notes in Computer Science, (2056) pages 141. 151, Springer Verlag, 2001.
- Friedman, Geiger and Goldszmidt (1997)] Friedman, N. , Geiger, D. , Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29, pp. 131 – 163.
- GF Cooper, E Herskovits, "A Bayesian method for the induction of probabilistic networks from data", Mach Learning 9(4):309–347(1992)
- O. C. H. François and P. Leray. Learning the tree augmented naive bayes classifier from incomplete datasets. In Proceedings of the Third European Workshop on Probabilistic Graphical Models (PGM'06), pages 91–98, Prague, Czech Republic, Sep 2006.
- J. Heckerman, D. , Meek, C. & Cooper, G. (1999). A Bayesian Approach to Causal Discovery. In Glymour, C. and G. Cooper, (ed. ), Computation, Causation, and Discovery, 141-165. MIT Press.
- S. B. Kotsiantis. : Supervised Machine Learning: A Review of Classification Techniques. In Proceeding of Emerging Artificial Intelligence Applications in Computer Engineering: Real World AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, 2007.
- Leray P, Francois O: BNT structure learning package: documentation and experiments. Technical Report 2004.
- Intel Berkley Research lab [Online]. http://db. csail. mit. edu/labdata/labdata. html
- A. C and Y. PS, "A framework for clustering uncertain data streams," in Proceedings of IEEE 24rd International Conference on Data Engineering, 2008, pp. 150–159.
- J. Han and M. Kamber, Data mining: Concepts and Techniques, 2nd ed. , Morgan Kaufmann, 2009.
- Francesco Chiti, Romano Fantacci, Francesco Archetti, Enza Messina, and Daniele Toscani, "An integrated communications framework for context aware continuous monitoring with body sensor networks", IEEE Journal of Selected Areas in Communication, 27(4) (May 2009), 379-386.
- M. Raymer, T. Doom, L. Kuhn, and W. Punch, "Knowledge discovery in medical and biological datasets using a hybrid bayes classifier/evolutionary algorithm raymer", IEEE Trans Syst. , Man, Cybern. B, Cybern. , vol. 33, no. 5, pp. 802 - 813 , 2003.
- Pham D, Ruz G (2009) Unsupervised training of Bayesian networks for data clustering. Proc Royal Soci A 465(2109):2927–2948.
- Chandola, A. Banerjee and V. Kumar (2007) Outlier detection: a survey, Technical Report. Univeristy of Minnesota, USA.
- Bruno M. Nogueira, Tadeu R. A. Santos, and Luis E. Zárate. Comparison of classifiers efficiency on missing values recovering: Application in a marketing database with massive missing data. In Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2007), 2007.
- W. W. Cohen, "Fast effective rule induction," in Proc. of the 12th Intl. Conf. on Machine Learning, 1995, pp. 115–123.
- Gu, T. , Wang, X. H. , Pung, H. K. , Zhang, D. Q. : An Ontology-based Context Model in Intelligent Environments. In: Proceedings of communication Networks and Distributed Systems Modeling and Simulation Conference, San Diego, California, USA, pp. 270–275 , 2004.
- Sangeeta Mittal,Alok Aggarwal, S. L. Maskara, "Contemporary Developments in Wireless Sensor Networks", International Journal of Modern Education and Computer Science, vol. 4, no. 3, pp. 1-13, 2012.
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