基于数据融合的无线传感器网络林火监控算法

doi:10.11896/j.issn.1002-137X.2015.11.033

Abstract

Abstract: Aiming at the existing problems in the application of wireless sensor networks in forest fire monitoring,we proposed a hierarchical clustering data fusion algorithm.In-cluster sensor node uses the weighted average method to achieve data-level fusion of the raw data,which reduces the redundancy of raw data and the traffic from the in-cluster sensor nodes to the cluster head node.Then,we established a D-S evidence theory based recognition framework in the cluster head node.Through the decision-level fusion of the feedback signal of the cluster members,the recognition accuracy of fire event and robustness of the network are improved.The experimental results show that the algorithm can effectively eliminate redundant data in wireless sensor networks,and it can work correctly in the case that the number of faulty nodes does not exceed 40% of the total number of nodes.

Key words: Forest fire monitoring,Wireless sensor network,Data fusion,Weighted average method,D-S evidence theory

LIU Yong-xing, ZHAO Juan-juan and CHANG Xiao-min. Forest Fire Monitoring Algorithm Based on Wireless Sensor Network Data Fusion[J].Computer Science, 2015, 42(11): 158-163.

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