Research on dynamic data fusion algorithm based on context awareness

In traditional data fusion algorithms based on context awareness, time-varying application situations and context acquisition cost are not considered, which leads to inaccurate situation prediction and low applicability of data fusion. In this paper, a space-based context model is introduced, in which the sensors´ history from three aspects, the context attribute, the context state and the situation space are described. Then optional attributes with the maximum overall utility are chosen by Dynamic Bayesian Networks. After that, the related situation prediction is obtained through data fusion. A data fusion algorithm CFACA (Context Fusion Algorithm based on Context Awareness) proposed in this paper gives a dynamic data fusion method. In the end, the simulation of this algorithm is discussed and the results show the effectiveness of the CFACA.