时空协作的WSNs节点异常检测算法
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摘要
为了降低无线传感器网络中异常节点产生的错误信息对WSNs服务质量的不利影响,利用节点数据时空相关性建立了可靠邻居筛选模型及节点感知数据稳定性评估模型。针对现有的依赖数据时空相关性的异常检测技术中对邻节点数据及其本身可靠性的忽略,建立了一种基于历史数据及可靠邻居协作的两步节点异常数据检测算法NADST。实验结果表明,该算法能在实现高检测精度的同时将虚警率控制在较低水平,且算法具有较强的容错能力。
In order to reduce the adverse effect of the faulty information generated by abnormalnodes on the QoS of WSNs, a reliable neighbor screening model and data stability assessment model are established based on spatial-temporal correlation of node sensed data. Aiming at the neglect of the data of neighbor nodes and their own reliability, a two-step node anomalydetection algorithm NADST based on historical data and the cooperation of reliable neighbors is proposed.The experimental results show that the algorithm can achieve higher correct detection accuracy while controlling the false alarm rate at a low level, and has a strong fault tolerant ability.
In order to reduce the adverse effect of the faulty information generated by abnormalnodes on the QoS of WSNs, a reliable neighbor screening model and data stability assessment model are established based on spatial-temporal correlation of node sensed data. Aiming at the neglect of the data of neighbor nodes and their own reliability, a two-step node anomalydetection algorithm NADST based on historical data and the cooperation of reliable neighbors is proposed.The experimental results show that the algorithm can achieve higher correct detection accuracy while controlling the false alarm rate at a low level, and has a strong fault tolerant ability.
引文
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[2]Gong W,Liu K,Liu Y.Directional diagnosis for wireless sensor networks[J].IEEE Transactions on Parallel&Distributed Systems,2015,26(5):1290-1300.
[3]Jiang J,Han G,Wang F,et al.An efficient distributed trust model for wireless sensor networks[J].IEEE Transactions on Parallel&Distributed Systems,2015,26(5):1228-1237.
[4]Hajjej B,Boussaid B,Zouinkhi A,et al.Fault detection design for temperature sensing unit of a wireless sensor node[C]//Proceedings of International Conference on Modelling,Identification and Control,2016:1-6.
[5]贾银亮,常相茂.一种无线传感器网络簇头选择算法--基于簇头发送能耗[J].计算机工程与应用,2017,53(22):82-86.
[6]周东华.现代故障诊断与容错控制[M].北京:清华大学出版社,2000.
[7]Xu Xiaolong,Geng Weijian,Yang Geng,et al.Survey of distributed fault detection algorithms for wireless sensor networks[J].Application Research of Computers,2012,29(12):4420-4425.
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[9]Chen J,Kher S,Somani A.Distributed fault detection of wireless sensor networks[C]//Proceedings of the 2006Workshop on DIWANS,2006:65-72.
[10]Yuan H,Zhao X,Yu L.A distributed Bayesian algorithm for data fault detection in wireless sensor networks[C]//Proceedings of International Conference on Information Networking,2015:63-68.
[11]Alshammari K,Ahmed A E S.An efficient approach for detecting nodes failures in wireless sensor network based on clustering[C]//Proceedings of International Symposium on Networks,Computers and Communications,2017.
[12]Titouna C,Aliouat M,Gueroui M.FDS:Fault detection scheme for wireless sensor networks[J].Wireless Personal Communications,2015,86(2):1-14.
[13]余成波,李芮,何强,等.基于粒子群算法及高斯分布的WSN节点故障诊断[J].振动、测试与诊断,2013,33(1):149-152.
[14]Banerjee I,Chanak P,Rahaman H,et al.Effective fault detection and routing scheme for Wireless Sensor Networks[J].Computers&Electrical Engineering,2014,40(2):291-306.
[15]Liu K,Ma Q,Gong W,et al.Self-diagnosis for detecting system failures in large-scale wireless sensor networks[J].IEEE Transactions on Wireless Communications,2014,13(10):5535-5545.