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

Mingyang Ji; Hongji Xu; Leitao Wang; Juan Dang; Zhengzheng Xu; Haiteng Fang

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

View Fulltext

Author(s): Mingyang Ji ¹ ; Hongji Xu ^{1, 2} ; Leitao Wang ¹ ; Juan Dang ¹ ; Zhengzheng Xu ¹ ; Haiteng Fang ¹
- Affiliations: 1: School of Information Science and Engineering, Shandong University, Jinan, People's Republic of China ;
  2: Department of Cognitive Science, University of California, San Diego, La Jolla, USA
Source: Volume 6, Issue 5, October 2016, p. 158 – 165
DOI: 10.1049/iet-wss.2015.0132 , Print ISSN 2043-6386, Online ISSN 2043-6394

Received 23/11/2015, Accepted 18/04/2016, Revised 25/03/2016, Published 01/10/2016

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.

References

1. 1)
  - 8. Huebscher, M.C., McCann, J.A.: ‘A learning model for trustworthiness of context-awareness services’. Proc. of the Third IEEE Int. Conf. on Pervasive Computing and Communications Workshops, Koloa, March 2005, pp. 120–124.
2. 2)
  - 23. Bu, Y.Y., Gu, T., Tao, X.P., et al: ‘Managing quality of context in pervasive computing’. Proc. of the Sixth IEEE Int. Conf. on Quality Software, Beijing, China, October 2006, pp. 193–200.
3. 3)
  - 25. Lee, B.H., Kim, D.H.: ‘Efficient context-aware selection based on user feedback’, IEEE Trans. Consum. Electron., 2012, 58, (3), pp. 978–984 (doi: 10.1109/TCE.2012.6311345).
4. 4)
  - 5. Javadi, S.H., Peiravi, A.: ‘Fusion of weighted decisions in wireless sensor networks’, IET Wirel. Sens. Syst., 2015, 5, (2), pp. 97–105 (doi: 10.1049/iet-wss.2013.0116).
5. 5)
  - 69. Redman, T.C.: ‘The impact of poor data quality on the typical enterprise’, Commun. ACM, 1998, 41, (2), pp. 79–82 (doi: 10.1145/269012.269025).
6. 6)
  - 6. Brgulja, N., Kusber, R., David, K., et al: ‘Measuring the probability of correctness of contextual information in context aware systems’. Proc. Eighth IEEE Int. Conf. on Dependable, Autonomic and Secure Computing, Chengdu, China, December 2009, pp. 246–253.
7. 7)
  - 7. Filho, J.B., Agoulmine, N.: ‘A quality-aware approach for resolving context conflicts in context-aware systems’. Proc. of IEEE/IFIP 9th Int. Conf. on Embedded and Ubiquitous Computing, Melbourne, Australia, October 2011, pp. 229–236.
8. 8)
  - 3. Yao, X.Y., Gao, Q., Li, F.: ‘Impact of outdated channel state information on energy efficiency of co-operative hybrid automatic repeat reQuest in wireless sensor networks’, IET Wirel. Sens. Syst., 2014, 4, (4), pp. 170–175 (doi: 10.1049/iet-wss.2014.0045).
9. 9)
  - 24. Xu, H.J., Wang, L.T., Xiong, H.L., et al: ‘Effective context inconsistency elimination algorithm based on feedback and reliability distribution for IOV’, China Commun., 2014, 11, (10), pp. 16–27 (doi: 10.1109/CC.2014.6969790).
10. 10)
  - 14. Huebscher, M.C., McCann, J.A., Dulay, N.: ‘Fusing multiple sources of context data of the same context type’. Proc. of the Int. Conf. on Hybrid Information Technology, Cheju Island, Korea, November 2006, pp. 406–415.
11. 11)
  - 12. Zheng, D., Wang, J., Kerong, B.: ‘A QoC based method for reliable fusion of uncertain pervasive contexts’. Proc. of 2013 IEEE Int. Conf. on High Performance Computing and Communications & 2013 IEEE Int. Conf. on Embedded and Ubiquitous Computing, Hunan, China, November 2013, pp. 2311–2316.
12. 12)
  - 1. Buchholz, T., Küpper, A., Schiffers, M.: ‘Quality of context information: what it is and why we need it’. Proc. of the Tenth Int. Workshop of the HP Open View University Association, Geneva, Switzerland, July 2003, pp. 1–14.
13. 13)
  - 21. Bellavista, P., Corradi, A., Fanelli, M., et al: ‘A survey of context data distribution for mobile ubiquitous systems’, ACM Comput. Surv., 2012, 44, (4), pp. 1–45 (doi: 10.1145/2333112.2333119).
14. 14)
  - 4. Jokhio, H.S., Jokhio, I.A., Kemp, A.H.: ‘Light-weight framework for security-sensitive wireless sensor networks applications’, IET Wirel. Sens. Syst., 2013, 3, (4), pp. 298–306 (doi: 10.1049/iet-wss.2012.0127).
15. 15)
  - 17. Wu, H., Siegel, M., Ablay, S.: ‘Sensor fusion using Dempster–Shafer theory II: static weighting and Kalman filter-like dynamic weighting’. Proc. 20th IEEE Instrumentation and Measurement Technology Conf., Vaii, USA, May 2003, pp. 907–912.
16. 16)
  - 15. Manzoor, A., Truong, H.L., Dustdar, S.: ‘Quality of context: models and applications for context-aware systems in pervasive environments’, Knowl. Eng. Rev., 2014, 29, (2), pp. 154–170 (doi: 10.1017/S0269888914000034).
17. 17)
  - 22. Neisse, R., Wegdam, M., Sinderen, M.V.: ‘Trustworthiness and quality of context information’. Proc. of the Ninth Int. Conf. for Young Computer Scientists, Hunan, China, November 2008, pp. 1925–1931.
18. 18)
  - 5. Nazario, D.C., Dantas, M.A.R., Todesco, J.L.: ‘Taxonomy of publications on quality of context’, Sustain. Bus. Int. J., 2012, 2012, (20), pp. 1–28.
19. 19)
  - 13. Manzoor, A., Truong, H.L., Dustdar, S.: ‘Using quality of context to resolve conflicts in context-aware systems’. Proc. of the First Int. Workshop, Quacon 2009, Stuttgart, Germany, June 2009, pp. 144–155.
20. 20)
  - 9. Nazario, D.C., Tromel, I.V.B., Dantas, M.A.R., et al: ‘Toward assessing quality of context parameters in a ubiquitous assisted environment’. Proc. of Int. Symp. on Computers and Communication, Madeira, Portugal, June 2014, pp. 1–6.
21. 21)
  - 18. Manzoor, A., Truong, H.L., Dustdar, S.: ‘Quality aware context information aggregation system for pervasive environments’. Proc. of IEEE Int. Conf. on Advanced Information Networking and Applications Workshops, Bradford, UK, May 2009, pp. 266–271.
22. 22)
  - 16. Wu, H., Siegel, M., Stiefelhagen, R., et al: ‘Sensor fusion using Dempster–Shafer theory [for context-aware HCI]’. Proc. of the 19th IEEE Instrumentation and Measurement Technology Conf., Anchorage, AK, USA, 2002, pp. 7–12.
23. 23)
  - 11. Kim, Y., Lee, K.: ‘A quality measurement method of context information in ubiquitous environments’. Proc. of IEEE Int. Conf. on Hybrid Information Technology, Cheju Island, Korea, November 2006, pp. 576–581.
24. 24)
  - 10. Manzoor, A., Truong, H.L., Dustdar, S.: ‘On the evaluation of quality of context’. Smart Sensing and Context, Proc. of the Third European Conf. on Smart Sensing and Context, Zurich, Switzerland, October 2008, pp. 140–153.
25. 25)
  - 19. Agoulmine, N.: ‘A quality-aware approach for selecting context information from redundant context sources’. Proc. 7th Latin American Network Operations and Management Symp., Quito, Ecuador, October 2011, pp. 1–8.

Login

Not registered yet?

Share

Tools

Login to add to favourites

Key

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

References

Related content