Avoiding congestion using RBF-GM controller for wireless sensor network

Abstract

In wireless sensor networks (WSNs), sink nodes are the bottleneck of network. As sensor network own its characteristics, the traditional congestion control strategy can't 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.