A new overall quality indicator OQoC and the corresponding context inconsistency elimination algorithm based on OQoC and Dempster–Shafer theory
| Autor: | Hongji Xu, Baozhen Du, Min Chen, Pan Lingling, Hailiang Xiong, Feifei Li |
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| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Correctness business.industry Computer science media_common.quotation_subject Context (language use) 02 engineering and technology Theoretical Computer Science 020901 industrial engineering & automation User experience design Dempster–Shafer theory 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Geometry and Topology business Algorithm Software Reliability (statistics) media_common |
| Zdroj: | Soft Computing. 24:10829-10841 |
| ISSN: | 1433-7479 1432-7643 |
| Popis: | With the rapid development of Internet of things technology, context-aware systems (CASs) are being gradually improved and widely applied to many fields such as digital home, smart health and so on. However, context information from sensor-rich CASs usually has inconsistency, which leads to wrong decisions made by systems, and even lowers user experience. Therefore, a new overall quality of context (OQoC) indicator is defined, which is the effective fusion of the parameters of reliability, up-to-dateness and modified correctness. Its accurate measurement is of great importance in inconsistency elimination. Moreover, we put forward a new context inconsistency elimination algorithm based on OQoC and Dempster–Shafer theory. The performance of the proposed algorithm is verified in personal identity verification scenario. Experimental results from multiple dimensions fully show the superiority of the proposed algorithm in solving context inconsistency problem, and quality of context information using the proposed algorithm has been greatly improved. |
| Databáze: | OpenAIRE |
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