Nowadays there is rarely a scenario where all the data can be fit nicely into one relational database management system or any other single data model. In acknowledgement of this fact a new concept of Dataspaces was introduced, according to which a dataspace system is assumed to be a hybrid of a search engine, a databases management system, an information integration system and a data sharing system. However the concept was presented in a visionary way and its implementation in the real world scenario has opened up many complex research challenges. Moreover so far the efforts put forth by the research community are quite disparate and it is highly desirable to have a unified effort which would hopefully enable rapid progress. Furthermore due to very high end-user expectations of such systems there are a lot of challenges and problems that need to be resolved and much scope for future work remains.

In smart environments, a multi-robot system is difficult to achieve a high confidence level of information for reliable decision making. The pieces of sensed information obtained from a multi-robot system have different degrees of uncertainty. Generated contexts about the situation of the environment can be conflicting even if they are acquired by simultaneous operations. In particular, unpredictable temporal changes in sensory information reduce the confidence level of information then lead to wrong decision making. In order to solve this problem, we propose a reasoning method based on Dynamic Evidential Fusion Network (DEFN). First, we reduce conflicting information in multi-sensor networks using Evidential Fusion Network (EFN). Second, we improve the confidence level of information using Temporal Belief Filtering (TBF) and Normalized Weighting technique. We distinguish a sensor reading error from sensed information. Finally, we compare our approach with a fusion process based on Dynamic Bayesian Networks (DBNs) using paired observations so as to show the improvement of our proposed method.

Abstract A image fusion technique and its edge keeping methods are discussed for improving fusion efficiency and accuracy