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.
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Abid Z, Chabridon S (2011) A fine-grain approach for evaluating the quality of context. In: Proceedings of IEEE international conference on pervasive computing and communications workshops, pp 444–449
Al-Shargabi AAQ (2015) A multilayer framework for quality of context in context-aware systems. Dissertation, De Montfort University
Al-Shargabi AA, Siewe F (2013) Resolving context conflicts using association rules (RCCAR) to improve quality of context-aware systems. In: Proceedings of the 8th international conference on computer science and education, pp 1450–1455
Al-Shargabi AA, Siewe F, Zahary AT (2017) Quality of context in context-aware systems. EAI Endorsed Trans Context-aware Syst Appl 4(12):1–25
Brgulja N, Kusber R, David K, Baumgarten M (2009) Measuring the probability of correctness of contextual information in context aware systems. In: Proceedings of the 8th IEEE international conference on dependable, autonomic and secure computing, pp 246–253
Buchholz T, Kupper A, Schiffers M (2003) Quality of context information: what it is and why we need it. In: Proceedings of the 10th international workshop of the HP open view university association, pp 1–14
Chen CH, Ye CY, Jacobsen HA (2011) Hybrid context inconsistency resolution for context-aware services. In: Proceedings of IEEE international conference on pervasive computing and communications, pp 10–19
Dey AK, Salber D, Abowd GD (2001) A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Hum–Comput Interact J 16(2–4):97–166
Filho JB, Agoulmine N (2011) A quality-aware approach for resolving context conflicts in context-aware systems. In: Proceedings of IEEE IFIP 9th international conference on embedded and ubiquitous computing, pp 229–236
Ji MY, Xu HJ, Wang LT, Dang J, Xu ZZ, Fang HT (2016) Approach of measuring PoC of context using limited self-feedback in context-aware systems. IET Wireless Sensor System 6(5):158–165
Jiang W, Zhuang M, Xie C, Wu J (2017) Sensing attribute weights: a novel basic belief assignment method. Sensors 17(4):721–741
Kim Y, Lee KA (2006) Quality measurement method of context information in ubiquitous environments. In: Proceedings of international conference on hybrid information technology, pp 576–581
Krause M, Hochstatter I (2005) Challenges in modelling and using quality of context (QoC). In: Proceedings of international workshop on mobile agents for telecommunication applications, pp 324–333
Lee BH, Kim DH (2012) Efficient context-aware selection based on user feedback. IEEE Trans Consumer Electron 58(3):978–984
Manzoor A, Truong HL, Dustdar S (2008) On the evaluation of quality of context. In: Proceedings of the 3rd European conference on smart sensing and context, pp 140–153
Manzoor A, Truong HL, Dustdar S (2009a) Quality aware context information aggregation system for pervasive environments. In: Proceedings of international conference on advanced information networking and applications workshops, pp 266–271
Manzoor A, Truong HL, Dustdar S (2009b) Using quality of context to resolve conflicts in context-aware systems. In: Proceedings of the 1st international conference on quality of context, pp 144–155
Manzoor A, Truong HL, Dustdar S (2014) Quality of context: models and applications for context-aware systems in pervasive environments. Knowl Eng Rev 29(2):154–170
McAllister D, Sun CE, Vouk M (1990) Reliability of voting in fault tolerant software systems for small output-spaces. IEEE Trans Reliab 39(5):524–534
Nazario DC, Campos PJ, Inacio EC, Dantas MAR (2017) Quality of context evaluating approach in AAL environment using IoT technology. In: Proceedings of IEEE 30th international symposium on computer-based medical systems, pp 558–563
Nazario DC, Tromel IVB, Dantas MAR, Todesco JL (2014) Toward assessing quality of context parameters in a ubiquitous assisted environment. In: Proceedings of international symposium on computers and communication, pp 1–6
Neisse R, Wegdam M, Sinderen MV (2008) Trustworthiness and quality of context information. In: Proceedings of international conference for young computer scientists, pp 1925–1931
Redman TC, Blanton A (1997) Data quality for the information age. Artech House, Norwood
Salah NB, Saadi IB (2016) Fuzzy AHP for learning service selection in context-aware ubiquitous learning systems. In: Proceedings of international conference on ubiquitous intelligence and computing, advanced and trusted computing, scalable computing and communications, cloud and big data computing, internet of people, and smart world congress, pp 171–179
Stvilia B, Gasser L, Twidale MB, Smith LC (2007) A framework for information quality assessment. J Assoc Inf Sci Technol 58(12):1720–1733
Shannon CE (1948) A mathematical theory of communication. Bell System Tech J 27(3):379–423
Su HZ, Ren J, Wen Z (2018) An approach using Dempster-Shafer evidence theory to fuse multi-source observations for dam safety estimation. Soft Comput 23(14):5633–5644
Tang Y, Zhou D, Xu S, He Z (2017) A weighted belief entropy-based uncertainty measure for multi-sensor data fusion. Sensors 17(4):928–943
Weiser M (1999) The computer for the 21st century. Mobile Comput Commun Rev 3(3):3–11
Xu HJ, Chen M, Zhou YM, Du BZ, Pan LL (2018) A novel comprehensive quality index QoX and the corresponding context-aware system framework. In: Proceedings of IEEE 4th international conference on computer and communications, pp 1–5
Xu HJ, Wang LT, Xiong HL, Du ZF, Xie ZG (2014) Effective context inconsistency elimination algorithm based on feedback and reliability distribution for IOV. China Commun 11(10):16–27
Yang X (2012) An adaptive mechanism for inconsistent context resolution in ubiquitous computing. In: Proceedings of international conference on control engineering and communication technology, pp 703–706
You I, Choi J, Choi C, Kim P (2014) Intelligent healthcare service based on context inference using smart device. Soft Comput 18(12):2577–2586
Zhang Y, Sun Y, Xie B (2015) Quality of health information for consumers on the web: a systematic review of indicators, criteria, tools, and evaluation results. J Assoc Inf Sci Technol 66(10):2071–2084
Zheng D, Wang J, Kerong B (2012) Evaluation of quality measure factors for the middleware based context-aware applications. In: Proceedings of international conference on computer and information science, pp 403–408
Zheng D, Wang J, Kerong B (2013) A QoC based method for reliable fusion of uncertain pervasive contexts. In: Proceedings of IEEE international conference on high performance computing and communications, embedded and ubiquitous computing, pp 2311–2316
Zheng D, Wang J, Kerong B (2014) Research of QoC based management for complex sensor networks applications. In: Proceedings of IEEE 12th international conference on dependable, autonomic and secure computing, pp 435–440
This work was financially supported by the National Natural Science Foundation of China (61771292, 61401253), the National Key Research and Development Program of China (2017YFC0803403, 2018YFC0831001) and the Natural Science Foundation of Shandong Province of China (ZR2016FM29, ZR2019MF038), the Key Research and Development Program of Shandong Province of China (2017GGX201003).
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Chen, M., Xu, H., Xiong, H. et al. A new overall quality indicator OQoC and the corresponding context inconsistency elimination algorithm based on OQoC and Dempster–Shafer theory. Soft Comput 24, 10829–10841 (2020). https://doi.org/10.1007/s00500-019-04585-0
- Context-aware systems
- Overall quality of context indicator
- Dempster–Shafer theory
- Personal identity verification