Multisensor data fusion for wildfire warning

Juanjuan Zhao, Yongxing Liu, Yongqiang Cheng, Yan Qiang, Xiaolong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Wildfires are highly destructive disasters that spread quickly. The use of advanced technology to achieve early warnings of wildfires is essential for the protection of wilderness resources. Nowadays, the method of using wireless sensor networks for wildfire warning has been extensively studied by many researchers. In this paper, we propose and have implemented a multi-sensor data fusion algorithm for wildfire monitoring and warning based on adaptive weighted fusion algorithm (AWFA) and Dempster - Shafer theory (DST) of evidence. At the same time, we also have put forward some auxiliary algorithms for fire warning, including heterogeneous sensor data homogenization methods, a judgment algorithm for sensor numerical errors, and an evidence conflict solution of Dempster - Shafer theory of evidence. Experimental results show that this algorithm can ensure the timeliness and accuracy of the wildfire warning, effectively reduce the amount of data transmission of sensor nodes and the whole network, and reduce the energy consumption, thus prolonging the network lifetime.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages46-53
Number of pages8
ISBN (Electronic)9781479973941
DOIs
StatePublished - Feb 27 2014
Event10th IEEE International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014 - Maui, United States
Duration: Dec 19 2014Dec 21 2014

Publication series

NameProceedings - 2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014

Other

Other10th IEEE International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014
CountryUnited States
CityMaui
Period12/19/1412/21/14

Fingerprint

Data fusion
Homogenization method
Sensor data fusion
Sensors
Sensor nodes
Disasters
Data communication systems
Wireless sensor networks
Fires
Energy utilization
Monitoring

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Cite this

Zhao, J., Liu, Y., Cheng, Y., Qiang, Y., & Zhang, X. (2014). Multisensor data fusion for wildfire warning. In Proceedings - 2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014 (pp. 46-53). [7051749] (Proceedings - 2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MSN.2014.13
Zhao, Juanjuan ; Liu, Yongxing ; Cheng, Yongqiang ; Qiang, Yan ; Zhang, Xiaolong. / Multisensor data fusion for wildfire warning. Proceedings - 2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 46-53 (Proceedings - 2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014).
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abstract = "Wildfires are highly destructive disasters that spread quickly. The use of advanced technology to achieve early warnings of wildfires is essential for the protection of wilderness resources. Nowadays, the method of using wireless sensor networks for wildfire warning has been extensively studied by many researchers. In this paper, we propose and have implemented a multi-sensor data fusion algorithm for wildfire monitoring and warning based on adaptive weighted fusion algorithm (AWFA) and Dempster - Shafer theory (DST) of evidence. At the same time, we also have put forward some auxiliary algorithms for fire warning, including heterogeneous sensor data homogenization methods, a judgment algorithm for sensor numerical errors, and an evidence conflict solution of Dempster - Shafer theory of evidence. Experimental results show that this algorithm can ensure the timeliness and accuracy of the wildfire warning, effectively reduce the amount of data transmission of sensor nodes and the whole network, and reduce the energy consumption, thus prolonging the network lifetime.",
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Zhao, J, Liu, Y, Cheng, Y, Qiang, Y & Zhang, X 2014, Multisensor data fusion for wildfire warning. in Proceedings - 2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014., 7051749, Proceedings - 2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014, Institute of Electrical and Electronics Engineers Inc., pp. 46-53, 10th IEEE International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014, Maui, United States, 12/19/14. https://doi.org/10.1109/MSN.2014.13

Multisensor data fusion for wildfire warning. / Zhao, Juanjuan; Liu, Yongxing; Cheng, Yongqiang; Qiang, Yan; Zhang, Xiaolong.

Proceedings - 2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 46-53 7051749 (Proceedings - 2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Zhao, Juanjuan

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AU - Cheng, Yongqiang

AU - Qiang, Yan

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N2 - Wildfires are highly destructive disasters that spread quickly. The use of advanced technology to achieve early warnings of wildfires is essential for the protection of wilderness resources. Nowadays, the method of using wireless sensor networks for wildfire warning has been extensively studied by many researchers. In this paper, we propose and have implemented a multi-sensor data fusion algorithm for wildfire monitoring and warning based on adaptive weighted fusion algorithm (AWFA) and Dempster - Shafer theory (DST) of evidence. At the same time, we also have put forward some auxiliary algorithms for fire warning, including heterogeneous sensor data homogenization methods, a judgment algorithm for sensor numerical errors, and an evidence conflict solution of Dempster - Shafer theory of evidence. Experimental results show that this algorithm can ensure the timeliness and accuracy of the wildfire warning, effectively reduce the amount of data transmission of sensor nodes and the whole network, and reduce the energy consumption, thus prolonging the network lifetime.

AB - Wildfires are highly destructive disasters that spread quickly. The use of advanced technology to achieve early warnings of wildfires is essential for the protection of wilderness resources. Nowadays, the method of using wireless sensor networks for wildfire warning has been extensively studied by many researchers. In this paper, we propose and have implemented a multi-sensor data fusion algorithm for wildfire monitoring and warning based on adaptive weighted fusion algorithm (AWFA) and Dempster - Shafer theory (DST) of evidence. At the same time, we also have put forward some auxiliary algorithms for fire warning, including heterogeneous sensor data homogenization methods, a judgment algorithm for sensor numerical errors, and an evidence conflict solution of Dempster - Shafer theory of evidence. Experimental results show that this algorithm can ensure the timeliness and accuracy of the wildfire warning, effectively reduce the amount of data transmission of sensor nodes and the whole network, and reduce the energy consumption, thus prolonging the network lifetime.

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Zhao J, Liu Y, Cheng Y, Qiang Y, Zhang X. Multisensor data fusion for wildfire warning. In Proceedings - 2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 46-53. 7051749. (Proceedings - 2014 10th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2014). https://doi.org/10.1109/MSN.2014.13