基于综合性评估的无线链路质量分类预测机制  

A Classification Prediction Mechanism Based on Comprehensive Assessment for Wireless Link Quality

作　　者：郭志强[1] 王沁[1] 万亚东[1] 李默涵[1] 

机构地区：[1]北京科技大学计算机与通信工程学院,北京100083

出　　处：《计算机研究与发展》2013年第6期1227-1238,共12页Journal of Computer Research and Development

基　　金：国家"八六三"高技术研究发展计划基金项目(2011AA040101);国家自然科学基金项目(61003251);高等学校博士学科点专项科研基金项目(20100006110015);北京市科技计划项目(Z111100054011078)

摘　　要：在无线传感器网络的应用中,对无线链路质量进行有效地评估和预测是网络协议设计中的一个基础性问题,特别是对于提高数据的传输可靠性.从刻画无线链路质量的多维角度出发,基于模糊逻辑设计了一个综合性链路质量指标(fuzzy-logic based link quality index,FLI),体现了无线链路的可靠性、波动性和丢包突发性对于链路数据传输可靠性的影响.然后基于FLI准则,利用贝叶斯网络设计了一种对无线链路质量进行分类预测的机制.通过3个实际无线传感器网络研究平台的链路数据集进行实验分析和对比,该机制中的分类预测器的平均预测精度约为85%.相比于4C预测器,在保证平均预测精度的同时,克服了其预测精度在分类界限处的畸变下滑现象,使预测精度的分布均匀化.In the applications of wireless sensor networks （WSN）, it is a fundamental issue to effectively estimate and predict the quality of wireless links for the network protocol design, such as reliable WSN deployment, routing policy and resource management protocol, especially in respect of the reliability of data delivery. In this paper, we characterize the quality of wireless links from a perspective of multiple dimensions and propose a comprehensive quality index of wireless links （referred as fuzzy-logic based link quality index, FLI）, which overcomes the defects of the single link quality indicator. FLI takes the link reliability, the link vibration and the burstiness of packet loss into consideration, which affects the reliable data delivery. Further, we design a mechanism based on Bayesian classifier to classify and predict wireless links based on the FLI metric. Taking the limited computing and storage resources in the WSN into account, the prediction mechanism uses offline model training and online classification prediction. Then it is tested and verified in the wireless link databases from three real WSN research testbeds, and the results show that our classifier achieves an average prediction accuracy of 85%. In comparison with the 4C approach, it avoids the sudden drop of prediction accuracy on intermediate quality links shown in the 4C, while maintaining a higher average accuracy. In other words, the distribution of prediction accuracy is uniform.

关 键 词：无线传感器网络 链路质量评估 分类预测 模糊逻辑 贝叶斯网络 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]