Skip to main content
SpringerLink
Log in

People-centric collective intelligence: decentralized and enhanced privacy mobile crowd sensing based on blockchain

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Smart phones have changed the way we interact with this world and our peers. They have not only become powerful, but they have become affordable. In this current day and age, smart phones are getting smarter with every subsequent iteration. Every aspect of today’s smart phones is far more efficient and powerful than the computers we had a few years back. Mobile crowd sensing (MCS) is the recent attractive research area that follows the crowd’s basic principle wisdom. Complex tasks can be performed easily with (MCS) network using the enormous amount of human involvement and powerful mobile devices. Even though there are a plethora of options available to users in terms of services to choose from, the one thing that remains a concern is security, privacy & data breach. Mobile crowd sensing -centralized design approach can give a lot of durable services. However, the trust of centralized service providers cannot be assured within the real world, owing to the profit-driven facts of service providers, when the privacy policy goes into conflict with their profit, they’re most likely to perform dishonest work, going against the work ethics and even performing malicious activities for gaining more profit. The prime example is Mark Zuckerberg providing their Facebook User data to third parties for monetary benefits. Besides the emergence of assorted attacks, particularly for inner attacks, service providers cannot offer full protection for task information, attribution information, or collected knowledge. Therefore, a decentralized design approach for mobile crowd sensing is strongly required. This paper aims to design and implement a real-time privacy-preserving data aggregation distributed-scheme to mobile crowd sensing called SMARTEE. Our design uses elliptic curve-based Public Key Encryption and digital signature for data encryption and verification, which performs faster computations, uses less storage and employs a shorter key. The digital signature ensures integrity and privacy-preserving of the data. We incorporate blockchain technology with the traditional (MCS) to process and analyze obtained crowd sensed data that ensures automotive security and privacy. Our proposed architecture with the features of blockchain protects the user information's privacy and increases the security of the (MCS) environment.

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
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Liu CH, Zhang B, Su X, Ma J, Wang W, Leung J (2015) Energy-aware participant selection for smartphone-enabled mobile crowd sensing. IEEE Syst J. https://doi.org/10.1109/JSYST.2015.2430362

    Article  Google Scholar 

  2. Feng W, Yan Z, Zhang H, Zeng K, Xiao Y, Hou J (2017) A survey on security, privacy, and trust in mobile crowdsourcing. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2017.2765699

    Article  Google Scholar 

  3. Li C, Gong S, Wang X, Wang L, Jiang Q, Okamura K (2017) Secure and efficient content distribution in crowdsourced vehicular content-centric networking. IEEE Access 6:5727–5739. https://doi.org/10.1109/ACCESS.2017.2778502

    Article  Google Scholar 

  4. Fernando N, Loke SW, Rahayu W (2016) Computing with nearby mobile devices: a work sharing algorithm for mobile edge-clouds. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2016.2560163

    Article  Google Scholar 

  5. Wu D, Fan L, Zhang C, Wang H, Wang A (2018) Dynamical credibility assessment of privacy-preserving strategy for opportunistic mobile crowd sensing. IEEE Access 6:37430–37443. https://doi.org/10.1109/ACCESS.2018.2847251

    Article  Google Scholar 

  6. Jansi KR, Kasmir Raja SV (2016) A survey on Privacy Preserving Data Aggregation Schemes in People Centric Sensing Systems and Wireless Domains. Indian J Sci Technol. https://doi.org/10.17485/ijst/2016/v9i37/102072

    Article  Google Scholar 

  7. Jansi KR, Kasmir Raja SV (2016) Design Perspectives of People Centric Sensing Systems. Indian J Sci Technol. https://doi.org/10.17485/ijst/2016/v9i37/102076

    Article  Google Scholar 

  8. Jansi KR, Kasmir Raja SV (2010) Cluster based efficient privacy preserving data aggregation (CEPPDA) in people centric sensing networks. Int J Adv Sci Technol 29(6):1450–1465. http://sersc.org/journals/index.php/IJAST/article/view/9282/5129

  9. Jansi K, Kasmir Raja SV, Sandhia GK (2018) Efficient privacy-preserving fault tolerance aggregation for people-centric sensing system. Serv Oriented Comput Appl 12:305–315. https://doi.org/10.1007/s11761-018-0241-5

    Article  Google Scholar 

  10. Feng W, Yan Z (2019) "MCS-Chain: decentralized and trustworthy mobile crowdsourcing based on blockchain. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2019.01.036

    Article  Google Scholar 

  11. Cunha F, Villas L, Boukerche A, Maia G, Viana A, Mini RA, Loureiro AA (2016) Data communication in VANETs: Protocols, applications and challenges. Ad Hoc Netw 44:90–103. https://doi.org/10.1016/j.adhoc.2016.02.017

    Article  Google Scholar 

  12. Wang Y, Yan Z, Feng W, Liu S (2020) Privacy protection in mobile crowd sensing: a survey. WorldWideWeb 23:421–452. https://doi.org/10.1007/s11280-019-00745-2

    Article  Google Scholar 

  13. Liu R, Liang J, Gao W, Yu S (2017) Privacy-based recommendation mechanism in mobile participatory sensing systems using crowdsourced users’ preferences. Futur Gener Comput Syst 80:76–88. https://doi.org/10.1016/j.future.2017.08.055

    Article  Google Scholar 

  14. Baskar, M., Renuka Devi, R., Ramkumar, J. et al.(2021) Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems. Neural Process Lett. https://doi.org/10.1007/s11063-020-10407-4

    Article  Google Scholar 

  15. Boubichea DE, Imran M, Maqsood A, Shoaib M (2018) Mobile crowd sensing—Taxonomy, applications, challenges, and solutions. Comput Hum Behav. https://doi.org/10.1016/j.chb.2018.10.028

    Article  Google Scholar 

  16. Suchithra M, Baskar M, Ramkumar JP, Kalyanasundaram B (2020) Amutha, “Invariant packet feature with network conditions for efficient low rate attack detection in multimedia networks for improved QoS.” J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-02056-1

    Article  Google Scholar 

  17. Wan J, Liu J, Shao Z, Vasilakos A, Imran M, Zhou KJS (2016) Mobile crowd sensing for traffic prediction in internet of vehicles. Sensors 16(1):88. https://doi.org/10.3390/s16010088

    Article  Google Scholar 

  18. Bazzi A, Zanella N (2016) Position based routing in crowd sensing vehicular networks. Ad Hoc Netw 36(2):409–424. https://doi.org/10.1016/j.adhoc.2015.06.005

    Article  Google Scholar 

  19. Xu Y, Tao J, Gao Y, Zeng L (2017) Location-Aware Worker Selection for Mobile Opportunistic Crowdsensing in VANETs. In: GLOBECOM 2017 IEEE Global Communications Conference. https://doi.org/10.1109/GLOCOM.2017.8255049.

  20. Thiagarajan R, Ganesan R, Anbarasu V et al (2021) Optimised with secure approach in detecting and isolation of malicious nodes in MANET. Wireless Pers Commun. https://doi.org/10.1007/s11277-021-08092-0

    Article  Google Scholar 

  21. Nunes DF, Moreira ES, Kimura BY, Sastry N, Mahmoodi T (2017) Attraction-area based geo-clustering for LTE vehicular crowdsensing data offloading. In: Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems. pp. 323–327. https://doi.org/10.1145/3127540.3127576

  22. Liu Y, Tang S, Wu HT, Zhang N (2019) RTPT: A framework for real-time privacy-preserving truth discovery on crowdsensed data streams. Comput Netw 148:349–360. https://doi.org/10.1016/j.comnet.2018.11.018

    Article  Google Scholar 

  23. Wang K, Qi X, Shu L, Deng DJ, Rodrigues C (2016) Toward trustworthy crowdsourcing in the social internet of things. IEEE Wirel Commun. https://doi.org/10.1109/MWC.2016.7721739

    Article  Google Scholar 

  24. Lu Z, Liu W, Wang Q, Qu G, Liu A (2018) A privacy-preserving trust model based on blockchain for vanets. IEEE Access 6:07. https://doi.org/10.1109/ACCESS.2018.2864189

    Article  Google Scholar 

  25. Xie L, Ding Y, Yang H, Wang X (2019) Blockchain-based Secure and trustworthy internet of things in SDN-enabled 5G-VANETs. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2913682

    Article  Google Scholar 

  26. Yang T, Chou D, Tseng W, Tseng H, Liu A (2019) Blockchain-based traffic event validation and trust verification for VANETs. IEEE Access 7:08. https://doi.org/10.1109/ACCESS.2019.2903202

    Article  Google Scholar 

  27. Yao Y, Chang X, Mišć J, Mišć VB, Li TJ (2019) BLA: blockchain-assisted lightweight anonymous authentication for distributed vehicular fog services. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2019.2892009

    Article  Google Scholar 

  28. Capponi A, Fiandrino C, Kliazovich D, Bouvry P, Giordano C (2017) A cost-effective distributed framework for data collection in cloud-based mobile crowd sensing architectures. IEEE Trans Sustain Comput. https://doi.org/10.1109/TSUSC.2017.2666043

    Article  Google Scholar 

  29. Jiao Y, Wang P, Niyato D, Suankaewmanee P, Systems D (2019) Auction mechanisms in cloud/fog computing resource allocation for public blockchain networks. IEEE Trans Parallel Distrib Syst. https://doi.org/10.1109/TPDS.2019.2900238

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, 603203, India

    M. Arulprakash & R. Jebakumar

Authors
  1. M. Arulprakash
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Jebakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Arulprakash.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arulprakash, M., Jebakumar, R. People-centric collective intelligence: decentralized and enhanced privacy mobile crowd sensing based on blockchain. J Supercomput 77, 12582–12608 (2021). https://doi.org/10.1007/s11227-021-03756-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03756-x

Keywords

Search

Navigation