In this paper, we proposed a scheme based on Monte Carlo algorithm to test whether or not the nodes are redundant for realizing the node density control in the sensor network. The computational complexity is only O(n)...In this paper, we proposed a scheme based on Monte Carlo algorithm to test whether or not the nodes are redundant for realizing the node density control in the sensor network. The computational complexity is only O(n). We also established the coverage collision detection and back-off mechanism applied in the wireless sensor network. The simulation results show that the system can cover all the interested area with the smallest number of nodes and a coverage void will not appear during the course of state-transition. The coverage collision detection and back-off mechanism proposed in this article can be applied when the nodes have either synchronous or asynchronous mechanism. It also provides a stable stage with the length of the time that can be adjusted.展开更多
This paper proposes a method for improving the precision of Network Traffic Prediction based on the Maximum Correntropy Criterion(NTPMCC),where the nonlinear characteristics of network traffic are considered.This meth...This paper proposes a method for improving the precision of Network Traffic Prediction based on the Maximum Correntropy Criterion(NTPMCC),where the nonlinear characteristics of network traffic are considered.This method utilizes the MCC as a new error evaluation criterion or named the cost function(CF)to train neural networks(NN).MCC is based on a new similarity function(Generalized correlation entropy function,Correntropy),which has as its foundation the Parzen window evaluation and Renyi entropy of error probability density function.At the same time,by combining the MCC with the Mean Square Error(MSE),a mixed evaluation criterion with MCC and MSE is proposed as a cost function of NN training.According to the traffic network characteristics including the nonlinear,non-Gaussian,and mutation,the Elman neural network is trained by MCC and MCC-MSE,and then the trained neural network is used as the model for predicting network traffic.The simulation results based on the evaluation by Mean Absolute Error(MAE),MSE,and Sum Squared Error(SSE)show that the accuracy of the prediction based on MCC is superior to the results of the Elman neural network with MSE.The overall performance is improved by about 0.0131.展开更多
文摘In this paper, we proposed a scheme based on Monte Carlo algorithm to test whether or not the nodes are redundant for realizing the node density control in the sensor network. The computational complexity is only O(n). We also established the coverage collision detection and back-off mechanism applied in the wireless sensor network. The simulation results show that the system can cover all the interested area with the smallest number of nodes and a coverage void will not appear during the course of state-transition. The coverage collision detection and back-off mechanism proposed in this article can be applied when the nodes have either synchronous or asynchronous mechanism. It also provides a stable stage with the length of the time that can be adjusted.
基金supported in part by the National Natural Science Foundation of China under Grant No.61071126the National Radio Project under Grants No. 2010ZX03004001, No.2010ZX03004-002, No.2011ZX03002001
文摘This paper proposes a method for improving the precision of Network Traffic Prediction based on the Maximum Correntropy Criterion(NTPMCC),where the nonlinear characteristics of network traffic are considered.This method utilizes the MCC as a new error evaluation criterion or named the cost function(CF)to train neural networks(NN).MCC is based on a new similarity function(Generalized correlation entropy function,Correntropy),which has as its foundation the Parzen window evaluation and Renyi entropy of error probability density function.At the same time,by combining the MCC with the Mean Square Error(MSE),a mixed evaluation criterion with MCC and MSE is proposed as a cost function of NN training.According to the traffic network characteristics including the nonlinear,non-Gaussian,and mutation,the Elman neural network is trained by MCC and MCC-MSE,and then the trained neural network is used as the model for predicting network traffic.The simulation results based on the evaluation by Mean Absolute Error(MAE),MSE,and Sum Squared Error(SSE)show that the accuracy of the prediction based on MCC is superior to the results of the Elman neural network with MSE.The overall performance is improved by about 0.0131.
