Basic probability assignment (BPA) has the key role in multi-sensor data fusion. This paper proposed a way to determine BPA using the various signals acquired from the sensors. It described the analysis of signals detected by the sensors. The determined BPA were used for multi-sensor data fusion to infer and recognize the context targeted by a wireless sensor networks. To determine BPA, the change rate was calculated and assessed to be reflected. The method enabled context inference using the sensor signals even when there was no advanced information of the situation.

Time variation is the essential component of the context awareness. It is a beneficial way not only including time lapse but also clustering time interval for the context inference using the information from sensor mote. In this study, we proposed a novel way of clustering based multi-sensor data fusion for the context inference. In the time interval, we fused the sensed signal of each time slot, and fused again with the results of th first fusion. We could reach the enhanced context inference with assessing the segmented signal according to the time interval at the Dempster-Shafer evidence theory based multi-sensor data fusion.

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The importance of common life safety is getting growth with modern society progress. This paper shows the extended way of assessment of danger that outcomes better result than the previous assessment based on crime frequency assessment. The assessment of this paper involves time, illusion degree, floating population and the personal condition. The evidence theory is not beneficial when the number of component increases, the assessment is more complex than before. This research solves the problem through the fuzzy theory adoption. This paper shows more advanced result that can express better danger awareness and get the adjective expression.