earticle

영어

Missing data is a common phenomenon in the data collection process of wireless sensor network (WSN), and the missing data imputing is an important issue of WSN stream data mining. Currently WSN missing data imputing method has little considered about the dynamic characteristics of internal data time structure during the data collection process, which makes data imputing difficult to reflect the real monitoring change objectively. In order to analyze the internal structure and dynamics of WSN time sequence data systematically, with the equivalence relation of the monitored object the time domain can be regarded as a series of integral time granule (ie atomic time point set), a wireless sensor network timing information system (WTIS) is established. The system can reason logically at different time granularity, and a multiple optimal time granularity strategy of WTIS based on hierarchical successive approximation approach is proposed. Finally, based on the research, a multiple optimal time granularity WSN missing data clustering imputing algorithm is proposed. Compared with traditional fixed time granularity missing data imputing algorithm, experiments show that the algorithm can lower error rate when imputing WSN missing data.