Inconsistency Elimination of Multi-Source Information Fusion in Smart Home Using the Dempster-Shafer Evidence Theory

In the smart home, inconsistencies in multi-source information often occur due to the vast quantities and diverse varieties of context information, and the Dempster-Shafer (D-S) evidence theory exhibits remarkable efficacy in addressing this issue. However, the use of basic possibility assignment (BPA) to quantify the credibility of information may introduce the uncertainty contained in the original data. In order to obtain a more reliable BPA and improve the accuracy of multi-source information fusion, an index that combines the relationships of sources and the information volume of context (RSIVC) is defined to comprehensively calculate the uncertainty of context information. In addition, interval distance is introduced to calculate the credibility of the proposition (CoP), which quantifies the correspondence among uncertain context information and all propositions in the frame of discernment (FoD). Finally, based on the d-S evidence theory, a multi-discount context information inconsistency elimination (MDCIE) algorithm using RSIVC and CoP is proposed, which is specifically applied to address the problem of inconsistency in multi-source light intensity information in smart homes. In the experimental part, we compare the traditional and state-of-the-art inconsistency elimination algorithms to verify the effectiveness of the proposed algorithm. Besides, we also test the efficiency of various components of the MDCIE algorithm as well as several RSIVC indicator-building strategies to ensure that the proposed algorithm is optimal. Under the conditions of high precision, medium precision, and low precision of sensors, the context-judge rate reaches 99.24 %, 98.58 %, and 98.09 %, respectively, which corroborates that the MDCIE algorithm is highly effective.