【摘要】 无线传感网络(Wireless Sensor Networks,WSNs)的性能依赖于所收集的数据质量。而最初,节点所感测的数据是粗糙的,需通过有效的数据检测算法将异常数据与正常数据进行区分。为此,提出基于二叉空间划分的异常数据检测(Binary Space Partition-based Anomaly Detection,BSP-AD)算法。BSP-AD算法通过二叉空间划分(Binary Space Partition, BSP)训练、测试数据。先通过训练数据,得到正常数据的区间范围,再通过此区间范围检测测试数据中异常部分。仿真结果表明,提出的BSP-AD算法能够准确地检测异常数据,并且计算成本和存储成本低于IDLO算法。更多还原

【Abstract】 The performance of wireless sensor networks(WSNs)depends on the quality of the data collected. At first, the data sensed by the node is rough, and an effective data detection algorithm should be used to distinguish abnormal data from normal data.Therefore, binary space partition-based anomaly detection(BSP-AD)algorithm is proposed in this paper. The BSP-AD algorithm trains and tests data through binary space partition(BSP)trees. Firstly, the range of normal data is obtained through the training data,and then some abnormal parts in the test data are detected through this range. Simulation results show that the proposed BSP-AD algorithm can accurately detect abnormal data, and the cost of calculation and storage is lower than IDLO algorithm.更多还原