Skip to main content

Advertisement

SpringerLink
Log in

A novel context inconsistency elimination algorithm based on the optimized Dempster-Shafer evidence theory for context-awareness systems

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

With the fast advancement of the Internet of things (IoT), context-awareness systems (CASs) have been widely used in many different fields, such as digital home and smart healthcare. However, low quality of context (QoC) usually causes the CASs to make inappropriate decisions, therefore, the context inconsistency has become an urgent problem that needs to be resolved. Although many researchers have adopted some QoC parameters to solve this problem, they do not take sufficient account of the relationships between context information sources and the impacts of the uncertainty of context information on the credibility of context information sources. In this paper, different distance measurement methods are utilized to divide context information sources into credible context information sources and incredible context information sources, on this basis, the Deng entropy is introduced to construct a new discounting factor in order to assign different discounting factors for different kinds of context information sources and a novel context inconsistency elimination algorithm based on the optimized Dempster-Shafer (D-S) evidence theory is proposed. The experimental results demonstrate that the proposed algorithm based on the Cosine distance can obtain 94.33% context-judge rate under high precision configuration of sensors. Besides, under low precision configuration of sensors, compared to the correlation coefficient based on generalized information quality (CIQ)-weighted algorithm which has the highest context-judge rate among other inconsistency elimination algorithms, the proposed algorithm based on the Cosine distance can solve more inconsistent context information.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Pimple M, Villarreal V, McChesney I (2019) Ubiquitous computing and ambient intelligence-UCAmI. Sensors 19(18):4034– 4037

    Article  Google Scholar 

  2. Chahuara P, Portet F, Vacher M (2017) Context-aware decision making under uncertainty for voice-based control of smart home. Expert Syst Appl 75(1):63–79

    Article  Google Scholar 

  3. Li H (2014) A novel design for a comprehensive smart automation system for the office environment. In: Proceedings of the 19th IEEE emerging technology and factory automation, pp 1–4

  4. 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

    Article  Google Scholar 

  5. Rogova G, Snidaro L (2019) Quality, context, and information fusion. In: Information quality in information fusion and decision making, pp 219–242

  6. Nazario DC, Tromel IVB, Dantas MAR, Todesco JL (2014) Toward assessing quality of context parameters in a ubiquitous assisted environment. In: Proceedings of IEEE symposium on computers and communications, pp 1–6

  7. Zheng D, Wang J, Kerong B (2013) A QoC based method for reliable fusion of uncertain pervasive contexts. In: Proceedings of the 10th IEEE international conference on high performance computing and communications, pp 2311–2316

  8. Zheng Y (2015) Methodologies for cross-domain data fusion: an overview. IEEE Trans Big Data 1(1):16–34

    Article  Google Scholar 

  9. McAllister DF, Sun C-E, Vouk MA (1990) Reliability of voting in fault-tolerant software systems for small outputspaces. IEEE Trans Reliab 39(5):524–534

    Article  MATH  Google Scholar 

  10. Manzoor A, Truong H-L, 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

  11. Lee B-H, Kim D-H (2012) Efficient context-aware selection based on user feedback. IEEE Trans Consum Electron 58(3):978–984

    Article  Google Scholar 

  12. Laghari AA, He H, Khan A, Kumar N, Kharel R (2018) Quality of experience framework for cloud computing (QoC). IEEE Access 6:64876–64890

    Article  Google Scholar 

  13. 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

    Article  Google Scholar 

  14. 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 Wirel Sens Syst 6(5):158–165

    Article  Google Scholar 

  15. 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 & education, pp 1450–1455

  16. Al-Shargabi AA, Siewe F, Thabit ZA (2016) A comparison study between RCCAR and conventional prediction techniques for resolving context conflicts in pervasive context-aware systems. In: Proceedings of the 17th international arab conference on information technology, pp 1–6

  17. Wang ZY, Xiao FY (2019) An improved multisensor data fusion method and its application in fault diagnosis. IEEE Access 7:3928–3937

    Article  Google Scholar 

  18. Manzoor A, Truong H-L, Dustdar S (2009) 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

  19. Boulkaboul S, Djenouri D (2020) DFIOT: data fusion for Internet of things. J Netw Syst Manag 28(15):1136–1160

    Article  Google Scholar 

  20. Fan SD, Xu HJ, Xiong HL, Chen M, Liu Q, Xing QH, Li TK (2022) A new QoC parameter and corresponding context inconsistency elimination algorithms for sensed contexts and non-sensed contexts. Appl Intell 52(1):681–698

    Article  Google Scholar 

  21. Dey AK, Abowd GD, Salber D (2001) A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Hum-Comput Interact 16(2):97–166

    Article  Google Scholar 

  22. Buchholz T, Kupper A, Schiffers M (2003) Quality of context: what it is and why we need it. In: Proceedings of the 10th workshop of the hp openview university association, pp 1–15

  23. Krause M, Hochstatter I (2005) Challenges in modelling and using quality of context (QoC). In: Proceedings of the 2nd international conference on mobility aware technologies and applications, pp 324–333

  24. Manzoor A, Truong H-L, Dustdar S (2014) Quality of context: models and applications for context-aware systems in pervasive environments. Knowl Eng Rev 29(2):154–170

    Article  Google Scholar 

  25. Al-Shargabi AA, Siewe F (2018) A multi-layer framework for quality of context in ubiquitous context-aware systems. Int J Pervasive Computing Commun 14(2):165–196

    Article  Google Scholar 

  26. Filho JB, Miron AD, Satoh I, Gensel J, Martin H (2010) Modeling and measuring quality of context information in pervasive environments. In: Proceedings of the 24th IEEE international conference on advanced information networking and applications, pp 690–697

  27. Chen M, Xu HJ, Xiong HL, Pan LL, Du BZ, Li FF (2020) A new overall quality indicator OQoC and the corresponding context inconsistency elimination algorithm based on OQoC and Dempster-Shafer theory. Soft Comput 24(14):10829–10841

    Article  Google Scholar 

  28. Jiang W, Zhan J (2017) A modified combination rule in generalized evidence theory. Appl Intell 46(3):630–640

    Article  MathSciNet  Google Scholar 

  29. Dubois D, Prade H (1984) Upper and lower possibilities induced by a multivalued mapping. In: Proceedings of the 9th world congress the international federation of automatic control, pp 147–152

  30. Shafer GA (1976) A mathematical theory of evidence. Princeton University Press

  31. Li P, Wei CP (2019) An emergency decision-making method based on D-S evidence theory for probabilistic linguistic term sets. Int J Disaster Risk Reduct 37:1–10

    Article  Google Scholar 

  32. Sun RL, Deng Y (2019) A new method to identify incomplete frame of discernment in evidence theory. IEEE Access 7:15547–15555

    Article  Google Scholar 

  33. Luo ZY, Deng Y (2020) A matrix method of basic belief massignment’s negation in Dempster-Shafer theory. IEEE Trans Fuzzy Syst 28(9):2270–2276

    Article  Google Scholar 

  34. Wang LW, Zhang Y, Feng JF (2005) On the Euclidean distance of images. IEEE Trans Pattern Anal Mach Intell 27(8):1334–1339

    Article  Google Scholar 

  35. Valsesia D, Fosson SM, Ravazzi C, Bianchi T, Magli E (2016) SparseHash: embedding Jaccard distance between supports of signals. In: Proceedings of IEEE international conference on multimedia & expo workshops, pp 1–6

  36. Manochandar S, Punniyamoorthy M (2021) A new user similarity measure in a new prediction model for collaborative filtering. Appl Intell 51(1):586–615

    Article  Google Scholar 

  37. Chen L, Xia MM (2021) A context-aware recommendation approach based on feature selection. Appl Intell 51(2):865–875

    Article  Google Scholar 

  38. Kang BY, Deng Y (2019) The Maximum Deng Entropy. IEEE Access 7:120758–120765

    Article  Google Scholar 

  39. Jiang W, Zhuang MY, Qin XY, Tang YC (2016) Conflicting evidence combination based on uncertainty measure and distance of evidence. SpringerPlus 5:1–11

    Article  Google Scholar 

  40. Yuan KJ, Xiao FY, Fei LG, Kang BY, Deng Y (2016) Modeling sensor reliability in fault diagnosis based on evidence theory. Sensors 16(1):1–13

    Article  Google Scholar 

  41. Li DB, Deng Y (2019) A new correlation coefficient based on generalized information quality. IEEE Access 7:1754110–175419

    Google Scholar 

Download references

Acknowledgements

This work was financially supported by the Natural Science Foundation of Shandong Province of China (ZR2020MF139), the National Key Research and Development Program of China (2020YFC0833200), the Key Research and Development Program of Shandong Province of China (2021SFGC0701), the Natural Science Foundation of China (61871342) and the Shandong Provincial Natural Science Foundation Intelligent Computing Joint Foundation (ZR2020LZH013).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong University, Qingdao, 266237, China

    Qiang Liu, Hongji Xu, Bo He, Zhi Liu, Shidi Fan, Jie Xu, Tiankuo Li, Juan Li, Mengmeng Wang & Shijie Li

  2. School of Control Science and Engineering, Shandong University, Jinan, 250061, China

    Hui Yuan

Authors
  1. Qiang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongji Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bo He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hui Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shidi Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jie Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Tiankuo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Juan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Mengmeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Shijie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Hongji Xu or Zhi Liu.

Ethics declarations

Conflict of Interests

All authors declare that they have no conflict of interest.

Additional information

Human and animals rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Q., Xu, H., He, B. et al. A novel context inconsistency elimination algorithm based on the optimized Dempster-Shafer evidence theory for context-awareness systems. Appl Intell 53, 15261–15277 (2023). https://doi.org/10.1007/s10489-022-04223-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-04223-7

Keywords

Search

Navigation