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Approach of measuring PoC of context using limited self-feedback in context-aware systems

In context-aware systems (CASs), due to various reasons such as faulty sensors, the context information received from a context provider does not always represent accurately the reality, and incorrect contexts often lead to conflicts in multi-source conditions. The need of accurate data fusion results in research on the parameter — probability of correctness (PoC), which denotes the probability that a piece of information is correct. This study focuses on precise evaluation of PoC and utilises this parameter to accomplish more accurate data fusion ultimately. The authors propose a novel approach based on limited self-feedback by taking the output of data fusion process as criterion to evaluate PoC automatically and dynamically. Supervisory mechanism is used to ensure the credibility of self-feedback data. The updated PoC will affect the accuracy of the following rounds of data fusion based on Dempster–Shafer (DS) theory. Experiments testify from multiple perspectives that compared with the traditional user feedback-based method, this self-assessment-based approach can achieve more precise evaluation of PoC with less human interaction and effectively improve the accuracy of data fusion through PoC-based DS theory. Thus, the information or services provided by CASs will be more accurate and efficient.