Skip to main content

A Reputation Incentive Mechanism of Crowd Sensing System Based on Blockchain

  • Conference paper
  • First Online:
Artificial Intelligence and Security (ICAIS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1253))

Included in the following conference series:

Abstract

Crowd Sensing (CS) is a kind of crowdsourcing that utilizes the built-in sensors and applications in intelligent devices, and has recently become a promising solution for distributed sensing. Crowd Sensing perception is a new data acquisition mode combining crowdsourcing idea and mobile device perception ability. CS refers to the formation of interactive and participatory perception network through people’s existing mobile devices, and the release of perception tasks to individuals or groups in the network to complete, so as to help professionals or the public to collect data, analyze information and share knowledge. This paper proposes a kind of CS perception system based on blockchain network, which processes the data of CS perception, and protects the privacy and information security of CS perception data. The reputation incentive mechanism is applied to the CS data analysis, which improves the data quality effectively. Experiments show that the mechanism proposed in this paper has an effective effect on improving quality of data.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Pass, R., Seeman, L., Shelat, A.: Analysis of the blockchain protocol in asynchronous networks. In: Coron, J.-S., Nielsen, J.B. (eds.) EUROCRYPT 2017. LNCS, vol. 10211, pp. 643–673. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56614-6_22

    Chapter  MATH  Google Scholar 

  2. Garay, J., Kiayias, A., Leonardos, N.: The bitcoin backbone protocol: analysis and applications. In: Oswald, E., Fischlin, M. (eds.) EUROCRYPT 2015. LNCS, vol. 9057, pp. 281–310. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46803-6_10

    Chapter  Google Scholar 

  3. Eyal, I., Gencer, A.E., Sirer, E.G., et al.: Bitcoin-NG: a scalable blockchain protocol. In: Proceedings of the 13th USENIX Symposium on Networked Systems Design and Implementation, pp. 45–59. USENIX Association, Berkeley (2016)

    Google Scholar 

  4. Fanning, K., Centers, D.P.: Blockchain and its coming impact on financial services. J. Corp. Account. Finan. J. 27(5), 53–57 (2016)

    Article  Google Scholar 

  5. Endsley, M.R.: Design and evaluation for situation Awareness enhancement. Proc. Hum. Factors Ergon. Soc. Ann. Meet. J. 32(2), 97–101 (1988)

    Article  Google Scholar 

  6. Endsley, M.R.: Situation awareness global assessment technique. In: Naecon (ed.) Proceedings of the IEEE 1988 National Aerospace and Electronics Conference (NAECON 1988), pp. 45–59. IEEE, Dayton (1988)

    Google Scholar 

  7. Endsley, M.R., Connors, E.S.: Situation awareness: state of the art. In: Power & Energy Society General Meeting-conversion & Delivery of Electrical Energy in the Century, pp. 1–4. IEEE (2008)

    Google Scholar 

  8. Liu, L., Cao, Z., Mao, C.: A note on one outsourcing scheme for big data access control in cloud. Int. J. Electron. Inf. Eng. J. 9(1), 29–35 (2018)

    Google Scholar 

  9. Zhou, X., et al.: Mobile crowd sensing and computing: the review of an emerging human-powered sensing paradigm. ACM Comput. Surv. J. 48(1), 7 (2016)

    Google Scholar 

  10. Jian, G., Xiao-Dong, Z., Qi, S.U., et al.: Survey of network security situation awareness. J. Softw. J. 11(23), 1–17 (2017)

    Google Scholar 

  11. Islam, S., Ali, H., Habib, A., Nobi, N., Alam, M., Hossain, D.: Threat minimization by design and deployment of secured networking model. Int. J. Electron. Inf. Eng. J. 8(2), 135–144 (2018)

    Google Scholar 

  12. Hashemi, S.M., He, J.: An evolutionary multi-objective approach for modelling network security. Int. J. Netw. Secur. J. 19(4), 528–536 (2017)

    Google Scholar 

  13. Zhang, C., Yuan, D.: Fast fine-grained air quality index level prediction using random forest algorithm on cluster computing of spark. In: Ubiquitous Intelligence & Computing & IEEE International Conference on Autonomic & Trusted Computing & IEEE International Conference on Scalable Computing & Communications & Its Associated Workshops, pp. 929–934. IEEE (2016)

    Google Scholar 

  14. Abawajy, J.H., Kelarev, A., Chowdhury, M.: Large iterative multitier ensemble classifiers for security of big data. IEEE Trans. Emerg. Top. Comput. J. 2(3), 352–363 (2014)

    Article  Google Scholar 

  15. Liu, Y., Sun, Z.L., Wang, Y.P., et al.: An eigen decomposition based rank parameter selection approach for the NRSFM algorithm. Neurocomput. J. 198, 109–113 (2016)

    Article  Google Scholar 

  16. Ganti, R.K., Ye, F., Lei, H.: Mobile crowd sensing: current state and future challenges. IEEE Commun. Mag. J. 49(11), 32–39 (2013)

    Article  Google Scholar 

  17. Guo, B., Chen, C., Zhang, D., et al.: Mobile crowd sensing and computing: when participatory sensing meets participatory social media. IEEE Commun. Mag. J. 54(2), 131–137 (2016)

    Article  Google Scholar 

  18. Jia, B., Zhou, T., Li, W., Liu, Z., Zhang, J.: A Blockchain-based location privacy protection incentive mechanism in crowd sensing networks. Sensors J. 18, 3894–3907 (2018)

    Article  Google Scholar 

  19. Li, M., Weng, J., Yang, A., et al.: CrowdBC: a blockchain-based decentralized framework for crowdsourcing. IEEE Trans. Parallel Distrib. Syst. 30(6), 1251–1266 (2018)

    Article  Google Scholar 

  20. Jiang, X., Liu, M., Yang, C., Liu, Y., Wang, R.: A blockchain-based authentication protocol for WLAN mesh security access. Comput. Mater. Continua 58(1), 45–59 (2019)

    Article  Google Scholar 

  21. Song, R., Song, Y., Liu, Z., Tang, M., Zhou, K.: GaiaWorld: a novel blockchain system based on competitive PoS consensus mechanism. Comput. Mater. Continua 60(3), 973–987 (2019)

    Article  Google Scholar 

  22. Sun, G., et al.: Research on public opinion propagation model in social network based on blockchain. Comput. Mater. Continua 60(3), 1015–1027 (2019)

    Article  Google Scholar 

  23. Melanie, S.: Blockchain: Blueprint for a New Economy. O’Reilly Media Inc., Newton (2015)

    Google Scholar 

  24. Szabo, N.: Formalizing and securing relationships on public networks. https://firstmonday.org/ojs/index.php/fm/article/view/548/469. Accessed 20 Nov 2019

  25. Christin, D.: Privacy in mobile participatory sensing: current trends and future challenges. J. Syst. Softw. 116, 57–68 (2015)

    Article  Google Scholar 

  26. Laranjeiro, N., Soydemir, S.N., Bernardino, J.: A survey on data quality: classifying poor data. In: IEEE 21st Pacific Rim International Symposium on Dependable Computing (PRDC). IEEE, pp. 179–188 (2015)

    Google Scholar 

  27. Askham, N., et al.: The six primary dimensions for data quality assessment. In: DAMA UK Working Group, pp. 432–435 (2013)

    Google Scholar 

  28. Loshin, D.: Data quality assessment. In: The Practitioner’s Guide to Data Quality Improvement (2010). Elsevier, Journal 11, 191–206

    Google Scholar 

  29. Pipino, L.L., Lee, Y.W., Wang, R.Y.: Data quality assessment. Commun. ACM J. 45(4), 211–218 (2002)

    Google Scholar 

  30. Truong, N.B., Lee, G.M., Um, T.W., et al.: Trust evaluation mechanism for user recruitment in mobile crowd-sensing in the Internet of Things. IEEE Trans. Inf. Forensics Secur. 2705–2719 (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jieren Cheng .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cheng, J., Long, H., Tang, X., Li, J., Chen, M., Xiong, N. (2020). A Reputation Incentive Mechanism of Crowd Sensing System Based on Blockchain. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Communications in Computer and Information Science, vol 1253. Springer, Singapore. https://doi.org/10.1007/978-981-15-8086-4_65

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-8086-4_65

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-8085-7

  • Online ISBN: 978-981-15-8086-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics