Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/MSN.2014.13 | MSN |
Keywords | Field | DocType |
dst,awfa,wildfire monitoring,network lifetime,energy consumption reduction,dempster,inference mechanisms,alarm systems,multisensor data fusion algorithm,heterogeneous sensor data homogenization method,wireless sensor network,auxiliary algorithm,wildfire warning,dempster-shafer theory,judgment algorithm,adaptive weighted fusion algorithm,wilderness resource protection,disaster,uncertainty handling,fire warning,multisensor data fusion,sensor node data transmission,wildfires,wireless sensor networks,sensor numerical error,shafer theory,sensor fusion | Data mining,Data transmission,Computer science,Sensor fusion,Data fusion algorithms,Energy consumption,Wireless sensor network,Theory of evidence | Conference |
Citations | PageRank | References |
2 | 0.39 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juanjuan Zhao | 1 | 23 | 5.93 |
| Yongxing Liu | 2 | 2 | 0.39 |
| Yongqiang Cheng | 3 | 2 | 0.39 |
| Yan Qiang | 4 | 30 | 12.11 |
| Xiaolong Zhang | 5 | 2 | 0.72 |