Dempster-Shafer Information Filtering in Multi-Modality Wireless Sensor Networks
A framework to estimate the state of dynamically
varying environment where data are generated from heterogeneous
sources possessing partial knowledge about the environment is presented.
This is entirely derived within Dempster-Shafer and Evidence
Filtering frameworks. The belief about the current state is expressed
as belief and plausibility functions. An addition to Single Input
Single Output Evidence Filter, Multiple Input Single Output Evidence
Filtering approach is introduced. Variety of applications such as
situational estimation of an emergency environment can be developed
within the framework successfully. Fire propagation scenario is used
to justify the proposed framework, simulation results are presented.
<p>[1] B.-K. Kim, S.-H. Hong, Y.-S. Jeong, and D.-S. Eom, “The study of
applying sensor networks to a smart home,” in Networked Computing
and Advanced Information Management, 2008. NCM ’08. Fourth International
Conference on, vol. 1, pp. 676–681, 2008.
[2] C. Suh and Y.-B. Ko, “Design and implementation of intelligent home
control systems based on active sensor networks,” Consumer Electronics,
IEEE Transactions on, vol. 54, no. 3, pp. 1177–1184, 2008.
[3] B. Chen and J. Wang, “Design of a multi-modal and high computation
power wireless sensor node for structural health monitoring,” in
Mechtronic and Embedded Systems and Applications, 2008. MESA 2008.
IEEE/ASME International Conference on, pp. 420–425, 2008.
[4] S.Gamwarige and E.C.Kulasekere, “An energy efficient distributed
clustering algorithm for ad-hoc deployed wireless sensor networks in
building monitoring applications,” Electronic Journal of Structural Engineering
(eJSE) Special Issue: Sensor Network on Building Monitoring:
from Theory to Real Application, pp. 11–27, 2009.
[5] L. Gui, T. Val, and A. Wei, “Improving localization accuracy using selective
3-anchor dv-hop algorithm,” in Vehicular Technology Conference
(VTC Fall), 2011 IEEE, pp. 1–5, 2011.
[6] K.Premaratne, D. A. Dewasurendra, and P. H. Bauer, “Evidence combination
in an environment with heterogeneous sources,” IEEE Trans.Syst.,
vol. 37, no. 3, pp. 298–309, 2007.
[7] J. L. D. L. Hall, Handbook of Multisensor Data Fusion:Theory and
Practice. The Electrical Engineering and Applied Signal Processing
Series, second ed., 2008.
[8] S. Blackman and R. Popoli, Design and Analyis of Modern Tracking
Systems. Norwood, MA: Artech House, 1999.
[9] J. L. G. Shafer, A Mathematical Theory of Evidence. NJ: Princeton
Univ. Press, 1976.
[10] D. Dewasurendra, P. Bauer, and K. Premaratne, “Evidence filtering,”
Signal Processing, IEEE Transactions on, vol. 55, no. 12, pp. 5796–
5805, 2007.
[11] B. Rao and H. Durrant-Whyte, “Fully decentralised algorithm for
multisensor kalman filtering,” Control Theory and Applications, IEE
Proceedings D, vol. 138, no. 5, pp. 413–420, 1991.
[12] R. Olfati-Saber and J. Shamma, “Consensus filters for sensor networks
and distributed sensor fusion,” in Decision and Control, 2005 and 2005
European Control Conference. CDC-ECC ’05. 44th IEEE Conference
on, pp. 6698–6703, 2005.
[13] A. Dhital, P. Closas, and C. Fernandez-Prades, “Bayesian filters for
indoor localization using wireless sensor networks,” in Satellite Navigation
Technologies and European Workshop on GNSS Signals and Signal
Processing (NAVITEC), 2010 5th ESA Workshop on, pp. 1–7, 2010.
[14] X. Sheng, Y.-H. Hu, and P. Ramanathan, “Distributed particle filter with
gmm approximation for multiple targets localization and tracking in
wireless sensor network,” in Information Processing in Sensor Networks,
2005. IPSN 2005. Fourth International Symposium on, pp. 181–188,
2005.
[15] D. Hui-ying, C. Bin, and Y. Yue-ping, “Application of particle filter for
target tracking in wireless sensor networks,” in Communications and
Mobile Computing (CMC), 2010 International Conference on, vol. 3,
pp. 504–508, 2010.
[16] T. D. Nguyen, H. T. Trinh, V. D. Nguyen, N. T. Nguyen, T. T. Nguyen,
and W. Koichiro, “A new evaluation of particle filter algorithm and
apply it to the wireless sensor networks,” in Computing, Management
and Telecommunications (ComManTel), 2013 International Conference
on, pp. 169–174, 2013.
[17] T. Reineking, “Particle filtering in the dempstershafer theory,” International
Journal of Approximate Reasoning, vol. 52, p. 11241135, 2011.
[18] D. E.C.Kulasekere, K.Premaratne, M.-L. Shyu, and P. H. Bauer, “Conditioning
and updating evidence,” Int. J. of Approx. Reasoning, vol. 36,
no. 1, pp. 75–108, 2004.
[19] D. Dewasurendra, P. Bauer, and K. Premaratne, “Distributed evidence
filtering: the recursive case,” in Circuits and Systems, 2006. ISCAS 2006.
Proceedings. 2006 IEEE International Symposium on, pp. 4 pp.–, 2006.
[20] J. H. R. Fagin, “A new approach to updating beliefs,” in Uncertainty
in Artificial Intelligence, P. Bonissone, M. Henrion, L.Kanal, and J.
Lemmer, Eds. New York: Elsevier, p. 347374, 1991.
[21] K. Sentz and S. Ferson, “Combination of evidence in dempster-shafer
theory,” tech. rep., 2002.
[22] G. Forney, “Smokeview (Version 5), A Tool for Visualizing Fire
Dynamics Simulation Data, Volume II: Technical Reference Guide,”
NIST Special Publication 1017-2, National Institute of Standards and
Technology, Gaithersburg, Maryland, May 2009.</p>
<p>[1] B.-K. Kim, S.-H. Hong, Y.-S. Jeong, and D.-S. Eom, “The study of
applying sensor networks to a smart home,” in Networked Computing
and Advanced Information Management, 2008. NCM ’08. Fourth International
Conference on, vol. 1, pp. 676–681, 2008.
[2] C. Suh and Y.-B. Ko, “Design and implementation of intelligent home
control systems based on active sensor networks,” Consumer Electronics,
IEEE Transactions on, vol. 54, no. 3, pp. 1177–1184, 2008.
[3] B. Chen and J. Wang, “Design of a multi-modal and high computation
power wireless sensor node for structural health monitoring,” in
Mechtronic and Embedded Systems and Applications, 2008. MESA 2008.
IEEE/ASME International Conference on, pp. 420–425, 2008.
[4] S.Gamwarige and E.C.Kulasekere, “An energy efficient distributed
clustering algorithm for ad-hoc deployed wireless sensor networks in
building monitoring applications,” Electronic Journal of Structural Engineering
(eJSE) Special Issue: Sensor Network on Building Monitoring:
from Theory to Real Application, pp. 11–27, 2009.
[5] L. Gui, T. Val, and A. Wei, “Improving localization accuracy using selective
3-anchor dv-hop algorithm,” in Vehicular Technology Conference
(VTC Fall), 2011 IEEE, pp. 1–5, 2011.
[6] K.Premaratne, D. A. Dewasurendra, and P. H. Bauer, “Evidence combination
in an environment with heterogeneous sources,” IEEE Trans.Syst.,
vol. 37, no. 3, pp. 298–309, 2007.
[7] J. L. D. L. Hall, Handbook of Multisensor Data Fusion:Theory and
Practice. The Electrical Engineering and Applied Signal Processing
Series, second ed., 2008.
[8] S. Blackman and R. Popoli, Design and Analyis of Modern Tracking
Systems. Norwood, MA: Artech House, 1999.
[9] J. L. G. Shafer, A Mathematical Theory of Evidence. NJ: Princeton
Univ. Press, 1976.
[10] D. Dewasurendra, P. Bauer, and K. Premaratne, “Evidence filtering,”
Signal Processing, IEEE Transactions on, vol. 55, no. 12, pp. 5796–
5805, 2007.
[11] B. Rao and H. Durrant-Whyte, “Fully decentralised algorithm for
multisensor kalman filtering,” Control Theory and Applications, IEE
Proceedings D, vol. 138, no. 5, pp. 413–420, 1991.
[12] R. Olfati-Saber and J. Shamma, “Consensus filters for sensor networks
and distributed sensor fusion,” in Decision and Control, 2005 and 2005
European Control Conference. CDC-ECC ’05. 44th IEEE Conference
on, pp. 6698–6703, 2005.
[13] A. Dhital, P. Closas, and C. Fernandez-Prades, “Bayesian filters for
indoor localization using wireless sensor networks,” in Satellite Navigation
Technologies and European Workshop on GNSS Signals and Signal
Processing (NAVITEC), 2010 5th ESA Workshop on, pp. 1–7, 2010.
[14] X. Sheng, Y.-H. Hu, and P. Ramanathan, “Distributed particle filter with
gmm approximation for multiple targets localization and tracking in
wireless sensor network,” in Information Processing in Sensor Networks,
2005. IPSN 2005. Fourth International Symposium on, pp. 181–188,
2005.
[15] D. Hui-ying, C. Bin, and Y. Yue-ping, “Application of particle filter for
target tracking in wireless sensor networks,” in Communications and
Mobile Computing (CMC), 2010 International Conference on, vol. 3,
pp. 504–508, 2010.
[16] T. D. Nguyen, H. T. Trinh, V. D. Nguyen, N. T. Nguyen, T. T. Nguyen,
and W. Koichiro, “A new evaluation of particle filter algorithm and
apply it to the wireless sensor networks,” in Computing, Management
and Telecommunications (ComManTel), 2013 International Conference
on, pp. 169–174, 2013.
[17] T. Reineking, “Particle filtering in the dempstershafer theory,” International
Journal of Approximate Reasoning, vol. 52, p. 11241135, 2011.
[18] D. E.C.Kulasekere, K.Premaratne, M.-L. Shyu, and P. H. Bauer, “Conditioning
and updating evidence,” Int. J. of Approx. Reasoning, vol. 36,
no. 1, pp. 75–108, 2004.
[19] D. Dewasurendra, P. Bauer, and K. Premaratne, “Distributed evidence
filtering: the recursive case,” in Circuits and Systems, 2006. ISCAS 2006.
Proceedings. 2006 IEEE International Symposium on, pp. 4 pp.–, 2006.
[20] J. H. R. Fagin, “A new approach to updating beliefs,” in Uncertainty
in Artificial Intelligence, P. Bonissone, M. Henrion, L.Kanal, and J.
Lemmer, Eds. New York: Elsevier, p. 347374, 1991.
[21] K. Sentz and S. Ferson, “Combination of evidence in dempster-shafer
theory,” tech. rep., 2002.
[22] G. Forney, “Smokeview (Version 5), A Tool for Visualizing Fire
Dynamics Simulation Data, Volume II: Technical Reference Guide,”
NIST Special Publication 1017-2, National Institute of Standards and
Technology, Gaithersburg, Maryland, May 2009.</p>
@article{"International Journal of Electrical, Electronic and Communication Sciences:56501", author = "D.M. Weeraddana and K.S. Walgama and E.C. Kulasekere", title = "Dempster-Shafer Information Filtering in Multi-Modality Wireless Sensor Networks", abstract = "A framework to estimate the state of dynamically
varying environment where data are generated from heterogeneous
sources possessing partial knowledge about the environment is presented.
This is entirely derived within Dempster-Shafer and Evidence
Filtering frameworks. The belief about the current state is expressed
as belief and plausibility functions. An addition to Single Input
Single Output Evidence Filter, Multiple Input Single Output Evidence
Filtering approach is introduced. Variety of applications such as
situational estimation of an emergency environment can be developed
within the framework successfully. Fire propagation scenario is used
to justify the proposed framework, simulation results are presented.
", keywords = "Dempster-Shafer Belief theory, Evidence Filtering,
Evidence Fusion, Sensor Modalities, Wireless Sensor Networks", volume = "7", number = "7", pages = "861-7", }
