A Communication Network Robustness Estimation Method Based on an Improved ELM

Abstract

The communication network robustness is becoming increasingly important because of its wider applications on higher reliability requirement systems, such as UAV, CBTC, etc. But communication network robustness estimation is becoming more difficult due to related various factors such as attacks, EMI, errors, mobility, fault-tolerant strategies, etc. With the purpose of estimating the robustness of communication network, most existing network robustness estimation researches mainly focus on failures in network physical layer and take complex network methods to estimate network robustness; recently, others have used Monte Carlo method to consider nodes mobility and several researches have summed up whole factors with weights to estimate the network robustness by AHP methods. Though these methods have largely been exploited to estimate the robustness of networks approximately, there is still no proper method to estimate the robustness of real communication network with consideration of nonlinear relationships among multiple factors. In this study, firstly, a double-layer network robustness model has been constructed; secondly, in order to take more robustness related factors into consideration, we have constructed a simulation platform based on NS-3; finally, wehave proposed a novel robustness estimation method based on improved ELM (extreme learning machine), which is powerful to estimate the nonlinear relationships. The novel robustness estimation method has been formed by four steps: simulation and data collection, improved EML, model training, and estimation. In the end, contrast analyzes have been made to illustrate three facts: (1) fault-tolerant strategies can improve the robustness obviously;(2) our double-layer network robustness model is better than the previous model; (3) the improved ELM has a better cross-validation accuracy which is above 95%.