Online Bayesian Data Fusion in Environment Monitoring Sensor Networks
Assuring reliable data collection in environment monitoring sensor network is a major design challenge. This paper gives a novel Bayesian model to reliably monitor physical phenomenon. We briefly review the errors on the data transfer channel between the sensor quantifying the physical phenomenon an...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-04-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/945894 |
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| _version_ | 1832547851808276480 |
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| author | Yang Dingcheng Wang Zhenghai Xiao Lin Zhang Tiankui |
| author_facet | Yang Dingcheng Wang Zhenghai Xiao Lin Zhang Tiankui |
| author_sort | Yang Dingcheng |
| collection | DOAJ |
| description | Assuring reliable data collection in environment monitoring sensor network is a major design challenge. This paper gives a novel Bayesian model to reliably monitor physical phenomenon. We briefly review the errors on the data transfer channel between the sensor quantifying the physical phenomenon and the fusion node, and a discrete K -ary input and K -ary output channel is presented to model the data transfer channel, where K is the number of quantification levels at the sensor. Then, discrete time series models are used to estimate the mean value of the physical phenomenon, and the estimation error is modeled as a Gaussian process. Finally, based on the transition probability of the proposed data transfer channel and the probability of the estimated value transited to specific quantification levels, the level with the maximum posterior probability is decided to be the current value of the physical phenomenon. Evaluations based on real sensor data show that significant gain can be achieved by the proposed algorithms in environment monitoring sensor networks compared with channel-unaware algorithms. |
| format | Article |
| id | doaj-art-7b6c4400767f49c28e8e6946fd2fcbf8 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2014-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-7b6c4400767f49c28e8e6946fd2fcbf82025-02-03T06:43:06ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-04-011010.1155/2014/945894945894Online Bayesian Data Fusion in Environment Monitoring Sensor NetworksYang Dingcheng0Wang Zhenghai1Xiao Lin2Zhang Tiankui3 Information Engineering School, Nanchang University, Nanchang 330031, China Southwest Institute of Electronic Technology, Chengdu 610036, China Information Engineering School, Nanchang University, Nanchang 330031, China Beijing University of Posts and Telecommunications, Beijing 100876, ChinaAssuring reliable data collection in environment monitoring sensor network is a major design challenge. This paper gives a novel Bayesian model to reliably monitor physical phenomenon. We briefly review the errors on the data transfer channel between the sensor quantifying the physical phenomenon and the fusion node, and a discrete K -ary input and K -ary output channel is presented to model the data transfer channel, where K is the number of quantification levels at the sensor. Then, discrete time series models are used to estimate the mean value of the physical phenomenon, and the estimation error is modeled as a Gaussian process. Finally, based on the transition probability of the proposed data transfer channel and the probability of the estimated value transited to specific quantification levels, the level with the maximum posterior probability is decided to be the current value of the physical phenomenon. Evaluations based on real sensor data show that significant gain can be achieved by the proposed algorithms in environment monitoring sensor networks compared with channel-unaware algorithms.https://doi.org/10.1155/2014/945894 |
| spellingShingle | Yang Dingcheng Wang Zhenghai Xiao Lin Zhang Tiankui Online Bayesian Data Fusion in Environment Monitoring Sensor Networks International Journal of Distributed Sensor Networks |
| title | Online Bayesian Data Fusion in Environment Monitoring Sensor Networks |
| title_full | Online Bayesian Data Fusion in Environment Monitoring Sensor Networks |
| title_fullStr | Online Bayesian Data Fusion in Environment Monitoring Sensor Networks |
| title_full_unstemmed | Online Bayesian Data Fusion in Environment Monitoring Sensor Networks |
| title_short | Online Bayesian Data Fusion in Environment Monitoring Sensor Networks |
| title_sort | online bayesian data fusion in environment monitoring sensor networks |
| url | https://doi.org/10.1155/2014/945894 |
| work_keys_str_mv | AT yangdingcheng onlinebayesiandatafusioninenvironmentmonitoringsensornetworks AT wangzhenghai onlinebayesiandatafusioninenvironmentmonitoringsensornetworks AT xiaolin onlinebayesiandatafusioninenvironmentmonitoringsensornetworks AT zhangtiankui onlinebayesiandatafusioninenvironmentmonitoringsensornetworks |