Binary space partition-based anomaly detection algorithm in wireless sensor networks

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.

Keywords

• wireless sensor networks(wsns)
• anomaly detection
• binary space partition(bsp)
• mass estimation
• split point