Abstract
Sensor networks have been widely applied in harsh environment monitoring. Fire monitoring is one of the extensive applications. But existing fire monitoring systems based on sensor networks fall into two problems. First, since sensing ability of sensor nodes is limited, the fire alarm may be delay or even fail to report. Next, because of the fire’s uncertainty, it is difficult to accurately determine whether the fire break out or not. This paper proposes a new framework of fire monitoring system based on sensor networks to conquer the above two problems. The system consists of data collection mechanism adopting improved time series prediction algorithm (for short TSDC) and fire detection mechanism using neural network model. Experiment results show that our fire monitoring system can recognize the flaming fire nearly 100%, and fire warning delay can be controlled within 30s. The slow smoldering fire recognition rate can be controlled within 80%, alarm delay can be controlled within 1 minute.
This work is supported by Program for New Century Excellent Talents in University under grant No.NCET-11-0955, Programs Foundation of Heilongjiang Educational Committee for New Century Excellent Talents in University under grant No.1252-NCET-011, Program for Group of Science and Technology Innovation of Heilongjiang Educational Committee under grant No.2011PYTD002, the Science and Technology Research of Heilongjiang Educational Committee under grant No.12511395, the Science and Technology Innovation Research Project of Harbin for Young Scholar under grant No.2008RFQXG107, 2009RFQX080 and No.2011RFXXG014, the National Natural Science Foundation of China under grant No.61070193, 60803015, Heilongjiang Province Founds for Distinguished Young Scientists under Grant No.JC201104, Heilongjiang Province Science and Technique Foundation under Grant No.GC09A109, The Natural Science Foundation of Heilongjiang Province under Grant No. F201038.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chunlei, W., Yu, H., Qiaolin, C., Xin, L.: Design and realization of fire monitor system based on wireless sensor network. In: Proceedings of 2007 Computer Engineering and Design, vol. 28(10), pp. 2310–2320 (2007)
Li, Y., Qin, H., Xu, W., Zhang, L.: A WSN Based Information Acquisition System of Fire Risk. In: Proceedings of 2008 Electronics and Packaging, pp. 32–44 (2008)
Tu, D., Liu, S., Xie, W., Zhang, Y.: A Fire Monitoring System In ZigBee Wireless Network. In: Proceedings of IEEE WiCOM 2009, pp. 1–5 (2009)
Liu, S., Xie, W., Zhang, Y.: Research and implementation of WSN in fire safety Applications. In: Proceedings of IEEE WiCOM 2010, pp. 1–4 (2010)
da Penha Jr., O.S., Nakamura, E.F.: Fusing Light and Temperature Data for Fire Detection. In: Proceedings of IEEE ISCC 2010, pp. 107–112 (2010)
Liu, S., Tu, D., Zhang, Y.: Multiparameter Fire Detection Based on Wireless Sensor Network. In: Proceedings of IEEE ICIS 2009, pp. 203–206 (2009)
Sung, W.-T., Chen, C.-H., Chen, J.-H., Liu, Y.-F.: Multi-Sensors Data fusion for Precise Measurement based on ZigBee WSN via Fuzzy Control. In: Proceedings of 2010 3CA, pp. 156–159 (2010)
Liu, S., Zhang, Y., Guo, I.: Multiparameter Fire Detection Node Based on Wireless Sensor Network. Proceedings of 2010 Journal of Sensors and Actuators, 883–887 (2010)
Soliman, H., Sudan, K., Mishra, A.: A Smart Forest-Fire Early Detection Sensory System Another Approach of Utilizing Wireless Sensor and Neural Networks. In: Proceedings of IEEE Sensors 2010, pp. 190–194 (2010)
Tan, W., Wang, Q., Huang, H., Guo, Y., Zhang, G.: Mine Fire Detection System Based on Wireless Sensor Network. In: Proceedings of IEEE ICIA 2007, pp. 148–152 (2007)
Bahrepour, M., Meratnia, N., Paul, J.M.: HavingaSensor Fusion-based Event Detection in Wireless Sensor Networks. In: Proceedings of IEEE MobiQuitous 2009, pp. 1–9 (2009)
Zhang, L., Fang, G.: Design Implementation of Automatic Fire Alarm System based on Wireless Sensor Networks. In: Proceedings of IEEE ISIP 2009, pp. 410–413 (2009)
Ren, H., Sun, J., Tian, Z., Wang, H.: Mine fire identification method with multi-parameters based on neural network. In: Proceedings of 2007 Journal of Liaoning Technical University (2007)
Da Penha Osman, S., Nakamura Eduardo, F.: Fusing Light and Temperature Data for Fire Detection. In: Proceedings of IEEE ISCC 2010, pp. 107–112 (2010)
Liu, B., Zhang, Y., Gan, F., Wang, D.: Design Intelligent Muti-sensor Fire Monitoring Based on DSP. In: Proceedings of IEEE ICEMI 2007, pp. 779–785 (2007)
Tulone, D., Madden, S.: PAQ: Time Series Forecasting for Approximate Query Answering in Sensor Networks. In: Römer, K., Karl, H., Mattern, F. (eds.) EWSN 2006. LNCS, vol. 3868, pp. 21–37. Springer, Heidelberg (2006)
Tulone, D., Madden, S.: An energy-efficient querying framework in sensor networks for detecting node similarities. In: Proceedings of IEEE MSWiM 2006, pp. 191–300 (2006)
Lu, C.J., Lee, T.S., Chiu, C.C.: Financial time series forecasting using independent component analysis and support vector regression. Proceedings of 2009 Decision Support Systems 47(2), 115–125 (2009)
Le Borgne, Y.-A., Santini, S., Bontempi, G.: Adaptive Model Selection for Time Series Prediction in Wireless Sensor Networks. Proceedings of 2007 Elsevier 87(12), 1–28 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, L., Sun, Y., Li, J., Ren, Q., Ren, M. (2012). A Framework of Fire Monitoring System Based on Sensor Networks. In: Wang, X., Zheng, R., Jing, T., Xing, K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2012. Lecture Notes in Computer Science, vol 7405. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31869-6_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-31869-6_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31868-9
Online ISBN: 978-3-642-31869-6
eBook Packages: Computer ScienceComputer Science (R0)