Skip to main content
SpringerLink
Log in

Improving Data Credibility for Mobile Crowdsensing with Clustering and Logical Reasoning

  • Conference paper
  • First Online:
Cloud Computing and Security (ICCCS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10040))

Included in the following conference series:

  • 1934 Accesses

Abstract

Mobile crowdsensing is a new paradigm that tries to collect a vast amount of data with the rich set of sensors on pervasive mobile devices. However, the unpredictable intention and various capabilities of device owners expose the application to potential dishonest and malicious contributions, bringing forth the important issues of data credibility assurance. Existed works generally attempt to increase data confidence level with the guide of reputation, which is very likely to be unavailable in reality. In this work, we propose CLOR, a general scheme to ensure data credibility for typical mobile crowdsensing application without requiring reputation knowledge. By integrating data clustering with logical reasoning, CLOR is able to formally separate false and normal data, make credibility assessment, and filter out the false ingredient. Simulation results show that improved data credibility can be achieved effectively with our scheme.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ganti, R.K., Ye, F., Lei, H.: Mobile crowdsensing: current state and future challenges. IEEE Commun. Mag. 49(11), 32–39 (2011)

    Article  Google Scholar 

  2. Gu, B., Sheng, V.S., Tay, K.T., Romano, W., Li, S.: Incremental support vector learning for ordinal regression. IEEE Trans. Neural Netw. Learn. Syst. 26(7), 1403–1416 (2015)

    Article  MathSciNet  Google Scholar 

  3. Khan, W.Z., Xiang, Y., Aalsalem, M.Y., Arshad, Q.: Mobile phone sensing systems: a survey. IEEE Commun. Surv. Tutorials 15(1), 402–427 (2013)

    Article  Google Scholar 

  4. Reddy, S., Samanta, V., Burke, J., Estrin, D., Hansen, M., Srivastava, M.: Mobisense - mobile network services for coordinated participatory sensing. In: Proceedings of International Symposium on Autonomous Decentralized Systems (ISADS), pp. 1–6. IEEE (2009)

    Google Scholar 

  5. Downs, J.S., Holbrook, M.B., Sheng, S., Cranor, L.F.: Are your participants gaming the system? Screening mechanical turk workers. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2399–2402. ACM (2010)

    Google Scholar 

  6. Wang, X., Cheng, W., Mohapatra, P., Abdelzaher, T.: Artsense: anonymous reputation and trust in participatory sensing. In: Proceedings of INFOCOM, pp. 2517–2525. IEEE (2013)

    Google Scholar 

  7. Amintoosi, H., Kanhere, S.S.: A reputation framework for social participatory sensing systems. Mob. Netw. Appl. 19(1), 88–100 (2014)

    Article  Google Scholar 

  8. Cheng, L., Kong, L., Luo, C., Niu, J., Gu, Y., He, W., Das, S.K.: False data detection and correction framework for participatory sensing. In: Proceedings of IEEE/ACM International Symposium on Quality of Service (IWQoS). IEEE/ACM (2015)

    Google Scholar 

  9. Zhou, T., Cai, Z., Xu, M., Chen, Y.: Leveraging crowd to improve data credibility for mobile crowdsensing. In: Proceedings of the 21th IEEE Symposium on Computers and Communications (ISCC). IEEE (2016)

    Google Scholar 

  10. Li, J., Li, X., Yang, B., Sun, X.: Segmentation-based image copy-move forgery detection scheme. IEEE Trans. Inf. Forensics Secur. 10(3), 507–518 (2015)

    Article  Google Scholar 

  11. Fu, Z., Ren, K., Shu, J., Sun, X., Huang, F.: Enabling personalized search over encrypted outsourced data with efficiency improvement. IEEE Trans. Parallel Distrib. Syst. (2015). doi:10.1109/TPDS.2015.2506573

    Google Scholar 

  12. Talasila, M., Curtmola, R., Borcea, C.: Improving location reliability in crowd sensed data with minimal efforts. In: Proceedings of the 6th Joint IFIP Wireless and Mobile Networking Conference (WMNC), pp. 1–8. IEEE (2013)

    Google Scholar 

  13. Wang, X., Fu, H., Xu, C., Mohapatra, P.: Provenance logic: enabling multi-event based trust in mobile sensing. In: Proceedings of IEEE International Performance Computing and Communications Conference (IPCCC), pp. 1–8. IEEE (2014)

    Google Scholar 

  14. Liu, S., Liu, Y., Ni, L., Li, M., Fan, J.: Detecting crowdedness spot in city transportation. IEEE Trans. Veh. Technol. 62(4), 1527–1539 (2013)

    Article  Google Scholar 

  15. Srivastava, M., Abdelzaher, T., Szymanski, B.: Human-centric sensing. Philos. Trans. R. Soc. Lond., Ser. A Math. Phys. Eng. Sci. 370(1958), 176–197 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  16. Alswailim, M.A., Hassanein, H.S., Zulkernine, M.: CRAWDAD dataset queensu/crowd temperature (v. 2015–11-20) (2015). http://crawdad.org/queensu/crowd_temperature/20151120

Download references

Acknowledgment

This work is supported by National Key Basic Research Program of China (No.2012CB933504), National Natural Science Foundation of China under Grant Nos. 61379144, 61379145, 61402513.

Author information

Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, China

    Tongqing Zhou, Zhiping Cai, Yueyue Chen & Ming Xu

Authors
  1. Tongqing Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiping Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yueyue Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ming Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tongqing Zhou .

Editor information

Editors and Affiliations

  1. University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Michigan State University , EAST LANSING, Michigan, USA

    Alex Liu

  3. National Dong Hwa University , Shoufeng, Taiwan

    Han-Chieh Chao

  4. Purdue University , West Lafayette, Indiana, USA

    Elisa Bertino

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Zhou, T., Cai, Z., Chen, Y., Xu, M. (2016). Improving Data Credibility for Mobile Crowdsensing with Clustering and Logical Reasoning. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48674-1_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48673-4

  • Online ISBN: 978-3-319-48674-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation