Large-scale Sensor Network Data Sequence Is The Research And Analysis Of The Method

In recent years, sensor-net based applications appeared in large numbers, and are widely used in industrial control, medical care, military surveillance, intelligent agriculture and dangerous source monitoring fields. With the development of sensor-net technology, the scale of sensor-net data is rapidly increasing. There will be a certain data uncertainty in every stage of sensor-net data processing. With the republish of the data, the data uncertainty will be superimposed and enlarged. The data uncertainty may eventually affect the quality of the outcome of the sensor-net based applications.Data provenance documents the inputs, entities, systems, and processes that influence data of interest, in effect providing a historical record of the data and its origins. Data origin tracking is a technique which is used to track the data through all transformations, analysis, and interpretations based on its data provenance. It can help sensor-net based applications to track their data quality, diagnose faults and analysis credible abnormal reasons. Traditional methods can not cope with the huge amounts of data in large-scale sensor-nets. Therefore the study on data source tracking for large-scale sensor-nets becomes very important.This paper studies the data origin tracking methods for large-scale sensor-nets. First, a novel data provenance model based on time sequence inferring is proposed. Then, after analysising the deficiencies of existing data origin tracking methods for large-scale sensor-nets, this paper proposes a runtime inferring data source tracking method which has lower storage overhead, and on the basis of this method, an extended runtime inferring data source tracking method which supports data reproducing is proposed. Finally, the storage overhead and time overhead of the two proposed data origin tracking methods are analyzed, and compared with existing methods. The experimental results show that the proposed runtime inferring data origin tracking method has lower storage overhead and time overhead in many different scenarios. And the proposed extended runtime inferring data origin tracking method supports data reproducing with lower storage overhead in a numerous scenarios. Among the related techniques in this paper,2have already declared invention patent,3have declared utility model patent, and they have been applied in the safety evaluation public service platform of the Web of Things in Wuxi.