联合改进核FCM与智能优化SVR的WSNs链路质量预测

为提高无线传感器网络（WSNs）链路质量预测精度和降低噪声影响,提出了一种联合改进核FCM与智能优化SVR （improved kernel furry c-means and intelligent support vector regression,IKFCM-ISVR）的WSNs链路质量预测方案.首先将基于紧致度和离散度的有效性指数引入核FCM方法,实现样本集聚类个数自动划分;然后采用改进核FCM方法对链路质量样本数据进行处理,获得样本聚类隶属度;在此基础上,构建群居蜘蛛优化SVR预测模型,采用基于"动态折射"学习机制的群集蜘蛛对模型参数进行优化,得到不同聚类最佳SVR参数组合;最后采用IKFCM-ISVR算法对不同实验场景下的WSNs链路数据进行预测评估.仿真结果表明,同其它预测算法相比,该算法预测精度提高了36.8~68.4%.

Link Quality Prediction Algorithm Based on Improved Kernel FCM and Intelligent SVR for WSNs

In order to improve the prediction accuracy and reduce the noise influence of link quality for wireless sensor network (WSNs),a link quality prediction algorithm based on improved kernel FCM and intelligent SVR (IKFCM-ISVR) is proposed.Firstly,the validity index based on compactness and dispersion is introduced into the kernel FCM (KFCM) method,which realizes the automatic division of cluster number for samples.Then the improved kernel FCM method is used to process the data of link quality,and the membership degree of sample clustering is obtained.On this basis,the SVR prediction model based on social spider optimization (SSO) algorithm is constructed,and the SSO based on dynamic refraction learning mechanism is used to optimize the parameters,getting the best combination of SVR parameters for different clustering.Finally the IKFCM-ISVR algorithm is used to predict the WSNs link data in different experimental scenarios.The simulation results show that,compared with other prediction algorithms,the prediction accuracy of the algorithm is improved by 36.8~68.4%.