Avoiding Congestion Using RBF-GM Controller for Wireless Sensor Network
In wireless sensor networks (WSNs), sink nodes are the bottleneck of network. As sensor network own its characteristics, the traditional congestion control strategy cant be used directly any longer. Most of the existing congestion control strategies and algorithms are not fully considered RTT. At the same time as the actual sensor network operating in the nonlinear, time delays and time-varying parameters such as interference factors, if the controller design parameters are fixed, not learning ability, then the actual running of the convergence is poor, slow convergence, not to control the length of queue. For the above-mentioned problems, the controller which is based on gray predicted Neural Network is proposed to cope with the large delays and time-varying network parameters. The gray GM (1, 1) model is utilized to compensate the time-delay, while RBF neural network is employed to design controller to reduce the number and interaction of tuning parameter. The simulation experimental results show that the integrated performance of the proposed algorithm is obviously superior to that of the existing schemes when the network configuration parameter is largely delayed.
Wireless sensor network congestion control RBF Gray prediction GM (1 1)
Tang Yifang Zhong Dafu
Dept.of Information Engineering University of Science and Technology Beijing Beijing, China Dept.of Dept.of Computer Engineering Technical Guangdong Institute of Science and Technology Guangzhou,China
国际会议
第四届国际计算机新科技与教育学术会议(2009 4th International Conference on Computer Science & Education)
南京
英文
275-278
2009-07-25（万方平台首次上网日期，不代表论文的发表时间）