摘要
为解决当前无线传感器网络异常节点检测算法复杂度高且影响网络寿命的问题,提出一种基于压缩感知的无线传感器网络电量损耗异常节点识别方法。对存在异常节点的无线传感器网络建模,得到满足稀疏特性的节点电量损耗向量,用压缩感知方式通过数据整理、线性计算、重构与判决3个步骤完成对网络中异常节点的检测与定位。仿真实验通过模拟存在异常节点的无线传感器网络场景,在不同参数下验证了该算法的有效性。
Aiming at the problem that the current anomaly detection algorithms in wireless sensor networks consume a lot of resources and affect the network lifetime,a method of anomaly node identification and location based on compressed sensing was proposed.The wireless sensor network with anomalous nodes was modeled,and the node power loss vector satisfying the sparse characteristics was obtained.The anomalous nodes in the network were detected and located through data collation,linear calculation,reconstruction and decision by compressed sensing.Simulation experiments verify the effectiveness of the proposed algorithm under different parameters by simulating the scene of wireless sensor networks with abnormal nodes.
作者
孙璇
SUN Xuan(School of Information Management,Beijing Information Science and Technology University,Beijing 100192,China)
出处
《计算机工程与设计》
北大核心
2020年第10期2724-2728,共5页
Computer Engineering and Design
基金
北京市教委科研计划基金项目(KM201811232019)。
