Earticle

초록

영어

In forest fire monitoring, in order to achieve the goal of reducing a large number of invalid and redundant data in wireless sensor network, improving the convergence rate of the wireless sensor network, prolonging the life cycle of nodes, improving the accuracy of fire report, this paper proposed an improved data integration method based on BP neural network. Data generated by various sensors can be integrated on the nodes with this method, the convergence speed of BP neural network can be improved by reference of real-time processing capacity of the node, and thus the energy consumption was reduced to a great extent. The experimental results showed that the proposed method can be well applied in fire monitoring sensor network, the monitoring accuracy was improved and the energy consumption of nodes was reduced, the capacity of wireless sensor network for forest fire monitoring was increased significantly.

목차

Abstract
 1. Introduction
 2. Algorithm Design
  2.1. Overall Architecture
  2.2. Algorithm Proposed
  2.3. Neural Network Training
  2.4. CN Transports the Obtained Results in step 3 to Terminal Node
 3. Experiment
  3.1. Implementation of the Center Node
  3.2. Experimental Procedure
 4. Experimental Results
 5. Conclusion
 Acknowledgement
 References

키워드

저자

• Youwei Shao [ School of Applied Electronics, Chongqing College of Electronic Engineering, Chongqing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605