DocumentCode
2620228
Title
Study on NN in Coal Mine Wireless Sensor Networks Link Communication Quality Measurement
Author
Wang, Gang ; Chen, Gui-Zhen ; Zhang, Shen
Author_Institution
Sch. of Inf. & Eng., China Univ. of Mine & Technol., Xuzhou, China
Volume
7
fYear
2009
fDate
March 31 2009-April 2 2009
Firstpage
125
Lastpage
129
Abstract
Due to the quality of service issues that wireless communications for data transmission need to ensure, we use the SOM neural network for QoS pattern space convergence, and apply cluster results to the shortest path algorithm in sensor networks. First, we measures the packet loss rate in different communication distance and the noise power density in underground straight laneway. From these we obtain the SOM network input samples. After network training, the convergent vector matrix and the corresponding quality of service function are obtained. Then, we apply the quality of service to the shortest path tree structure, and evaluate the performance of pattern recognition in the shortest path tree structure by NS2 software. At last, the simulation based on the QoS routing algorithm by NS2 software verifies the superiority of this method.
Keywords
coal; learning (artificial intelligence); mining; quality of service; radiofrequency measurement; routing protocols; wireless sensor networks; NS2 software; QoS pattern space convergence; QoS routing algorithm; SOM neural network; coal mine wireless sensor networks; convergent vector matrix; network link communication quality measurement; noise power density; packet loss rate; quality of service; shortest path algorithm; Clustering algorithms; Convergence; Data communication; Density measurement; Neural networks; Power measurement; Quality of service; Tree data structures; Wireless communication; Wireless sensor networks; Neural network (NN); Quality of Service (QoS); Wireless sensor networks (WSNs);
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Engineering, 2009 WRI World Congress on
Conference_Location
Los Angeles, CA
Print_ISBN
978-0-7695-3507-4
Type
conf
DOI
10.1109/CSIE.2009.722
Filename
5170294
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