Discriminating Event Information for Calculation of Basic Probability Assignment

Abstract

This study proposed a novel way to calculate Basic Probability Assignment(BPA), which is crucial in the data fusion. The study mapped sensing signal with a linear discriminant function analysis and assessed the sizes with time. This was beneficial to get a clue for context inference with using the Dempster-Shafer Theory and to determine BPA based on the size changing of mapping data in time intervals. The study provides with the way of context inference for fast detecting a local event that affects the whole area. Data fusion process with multi-sensors has caught increasingly more attention nowadays. Fuzzy Theory (FT) and Dempster-Shafer Theory (DST) have been used much as a method to process heterogeneous signals and data fusion although these theories were designed originally to express the ambiguity and uncertainty of events occurring in the real world. Multi-sensors data fusion process is applied to acquire information with high quality by using some kinds of sensors in biometrics, geographic information, network security, robot control, etc. Multi-sensors data fusion is also important to acquire context information with high quality in the Ubiquitous Sensor Network(USN) data process. Multi-sensors data fusion for acquiring context information in the sensor network has become big data process in the data streaming conditions. The studies in the data streaming conditions first consider searching, classifying, and clustering the characteristics of continuous data that are not saved in a server. However, it is necessary to infer the changing context in real time through the data patterns acquired from the real time data. For example, we need to infer situations urgently and efficiently without holding to analyze when a blaze in a large area occurs, objects move around the road, or some risk factors threat the security in our residential environment. To do so, it is necessary to infer the context based on the rapid analysis of data patterns by time intervals in the data stream. We could obtain a clue of the simple and early context inference from the changing patterns with time of the sensing data detected and reported by sensors. The study presents a way to infer context information by discriminating event information reported to a host through sink nodes in the data streaming environment. It is also suggested that the results from discriminating event information are applied to BPA determination with using DST. It is reasonable to determine BPA based on the detection of patterns appeared in