Multi-Sensor Building Fire Alarm System with Information Fusion Technology Based on D-S Evidence Theory

Multi-sensor and information fusion technology based on Dempster-Shafer evidence theory is applied in the system of a building fire alarm to realize early detecting and alarming. By using a multi-sensor to monitor the parameters of the fire process, such as light, smoke, temperature, gas and moisture, the range of fire monitoring in space and time is expanded compared with a single-sensor system. Then, the D-S evidence theory is applied to fuse the information from the multi-sensor with the specific fire model, and the fire alarm is more accurate and timely. The proposed method can avoid the failure of the monitoring data effectively, deal with the conflicting evidence from the multi-sensor robustly and improve the reliability of fire warning significantly.

[1]  Yibing Li,et al.  The application of improving space-time DS evidence theory in distinguishing vehicle , 2009, 2009 Asia Pacific Conference on Postgraduate Research in Microelectronics & Electronics (PrimeAsia).

[2]  John C. Pearson,et al.  Neural Network Approach To Sensory Fusion , 1988, Defense, Security, and Sensing.

[3]  Naixue Xiong,et al.  An Emergency-Adaptive Routing Scheme for Wireless Sensor Networks for Building Fire Hazard Monitoring , 2010, Sensors.

[4]  Wen-Tsai Sung,et al.  Evidence-based multi-sensor information fusion for remote health care systems , 2013 .

[5]  Ronald Beaubrun,et al.  Experiments for Fire Detection Using a Wireless Sensor Network , 2012 .

[6]  Alun D. Preece,et al.  A distributed architecture for heterogeneous multi sensor-task allocation , 2011, 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS).

[7]  Alun D. Preece,et al.  Demo: A distributed architecture for heterogeneous multi sensor-task allocation , 2011, 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS).

[8]  Zeljko J. Aleksic.  Minimization of the optical smoke detector false alarm probability by optimizing its frequency characteristic , 2000, IEEE Trans. Instrum. Meas..

[9]  Jun Xu,et al.  Information Fusion Algorithm for Electromechanical Equipment Based on DS Evidence Theory , 2013 .

[10]  Wang Xue,et al.  Application of fuzzy data fusion in multi-sensor fire monitoring , 2012, 2012 International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA).

[11]  Hugh F. Durrant-Whyte,et al.  Sensor Models and Multisensor Integration , 1988, Int. J. Robotics Res..

[12]  Changying Li,et al.  Neural network and Bayesian network fusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection , 2007 .

[13]  Xiao Wang,et al.  Trust Management Scheme Based on D-S Evidence Theory for Wireless Sensor Networks , 2013, Int. J. Distributed Sens. Networks.

[14]  Mongi A. Abidi,et al.  Data fusion in robotics and machine intelligence , 1992 .

[15]  M. Singh,et al.  An Evidential Reasoning Approach for Multiple-Attribute Decision Making with Uncertainty , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[16]  P. L. Bogler,et al.  Shafer-dempster reasoning with applications to multisensor target identification systems , 1987, IEEE Transactions on Systems, Man, and Cybernetics.