无线传感器网络中异常读数检测算法研究*

针对无线传感器网络的离群点检测算法由于没有充分考虑数据的时空关联性和网络的分布特性,导致检测精度低、通信量大和计算复杂度高等局限，提出了基于时空关联的分布计算与过滤的在线离群点检测算法。该算法在各传感器节点上利用传感器读数的时间关联性生成候选离群点，并利用空间关联性对候选离群点进行过滤得到局部离群点，最终将所有传感器节点上的局部离群点集中到sink节点上获得全局离群点。利用时空关联性提高了检测精度，利用分布计算与过滤减少了通信量和计算量，理论分析和实验结果均表明该算法优于现有算法。

Anomaly reading detection algorithm in WSN

Found that the existing outlier detection algorithms in WSN are of some disadvantages such as lower detection precision, higher communication complexity and computational complexity due to not enough consideration of the spatio-temporal correlation of data and the characteristic of distribution networks. This paper proposed a novel distributed on-line outlier detection algorithm based on spatio-temporal correlation. In each sensor node, using sliding window technique generated a set of candidate outliers based time-correlated sensor readings, and using filtering technology generated a set of local outliers based spatial neighborhood. Ultimately, in sink sensor node, collecting whole local outliers in all nodes obtained the set of global outliers according to the outlying degree. Using spatial and temporal correlation improved the detection accuracy, and using distributed computing reduced the amount of communication and computation. Theoretical analysis and experimental results show that the proposed algorithm is superior to existing algorithms.