工程科学与技术2017，Vol.49Issue(2)：152-159,8.DOI:10.15961/j.jsuese.201601191
基于动态贝叶斯网络的WSNs链路质量预测
Link Quality Prediction for WSNs Based on Dynamic Bayesian Networks
摘要
Abstract
In wireless sensor networks,the link quality prediction is a basic issue in guarantying reliable data transmission and upper network protocol performance.In this paper,a link quality prediction mechanism based on dynamic Bayesian networks (DBN) was proposed.The link quality was evaluated by link signal quality,link stability and link asymmetry,instead of a single evaluation index which could lead to a bias evaluation of link quality.The K-means clustering algorithm was then used to discrete the parameters so as to get intervals of parameters respectively.The weight of each parameter was determined by the entropy value method which could eliminate the interference of subjective factors in the process of weighting.Besides,in order to overcome the defects of maximum membership,asymmetry closeness analysis method was employed to construct the comprehensive link quality level indicators.The DBN based link quality prediction model was constructed,after both an initial network and a transfer network of DBN were determined.Finally,DBN parameters were determined by EM (expectation maximization)algorithm.Experimental results showed that it was reasonable to level link quality by using asymmetry closeness analysis method and to predict link quality with DBN model.Compared with the 4C and FLI prediction model,the proposed model based on DBN achieved better accuracy.In a word,the proposed mechanism with DBN model has better accuracy and robustness,which has been evaluated in terms of link signal quality,link reliability,and link asymmetry.关键词
无线传感器网络/链路质量预测/动态贝叶斯网络/贴近度分析法Key words
wireless sensor networks/link quality prediction/dynamic Bayesian networks/closeness analysis method分类
信息技术与安全科学引用本文复制引用
舒坚,刘松,刘琳岚,谷小乐..基于动态贝叶斯网络的WSNs链路质量预测[J].工程科学与技术,2017,49(2):152-159,8.基金项目
国家自然科学基金资助项目(61363015 （）
61262020 （）
61501218 （）
61501217) （）
江西省高等学校科技落地计划资助项目(KJLD14054) （KJLD14054）
江西省教育厅科学技术重点资助项目(GJJ150702) （GJJ150702）
