IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v12y2020i3p56-d334831.html
   My bibliography  Save this article

Development of User-Participatory Crowdsensing System for Improved Privacy Preservation

Author

Listed:
  • Mihui Kim

    (School of Computer Engineering & Applied Mathematics, Computer System Institute, Hankyong National University, Anseong 17579, Korea)

  • Junhyeok Yun

    (School of Computer Engineering & Applied Mathematics, Computer System Institute, Hankyong National University, Anseong 17579, Korea)

Abstract

Recently, crowdsensing, which can provide various sensing services using consumer mobile devices, is attracting considerable attention. The success of these services depends on active user participation and, thus, a proper incentive mechanism is essential. However, if the sensing information provided by a user includes personal information, and an attacker compromises the service provider, participation will be less active. Accordingly, personal information protection is an important element in crowdsensing services. In this study, we resolve this problem by separating the steps of sensing data processing and the reward payment process. An arbitrary node in a sensing data processing pool consisting of user nodes is selected for sensing data processing, and only the processing results are sent to the service provider server to reward the data providing node. The proposed user-participatory crowdsensing system is implemented on the Kaa Internet of things (IoT) platform to evaluate its performance and demonstrate its feasibility.

Suggested Citation

  • Mihui Kim & Junhyeok Yun, 2020. "Development of User-Participatory Crowdsensing System for Improved Privacy Preservation," Future Internet, MDPI, vol. 12(3), pages 1-19, March.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:3:p:56-:d:334831
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/12/3/56/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/12/3/56/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Virginia Pilloni, 2018. "How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0," Future Internet, MDPI, vol. 10(3), pages 1-14, March.
    2. Marianne Silva & Gabriel Signoretti & Julio Oliveira & Ivanovitch Silva & Daniel G. Costa, 2019. "A Crowdsensing Platform for Monitoring of Vehicular Emissions: A Smart City Perspective," Future Internet, MDPI, vol. 11(1), pages 1-20, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giovanni Pau & Alessandro Severino & Antonino Canale, 2019. "Special Issue “New Perspectives in Intelligent Transportation Systems and Mobile Communications towards a Smart Cities Context”," Future Internet, MDPI, vol. 11(11), pages 1-3, October.
    2. Damianos P. Sakas & Nikolaos Th. Giannakopoulos, 2021. "Harvesting Crowdsourcing Platforms’ Traffic in Favour of Air Forwarders’ Brand Name and Sustainability," Sustainability, MDPI, vol. 13(15), pages 1-25, July.
    3. Radosław Drozd & Radosław Wolniak, 2021. "Metrisable assessment of the course of stream-systemic processes in vector form in industry 4.0," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(6), pages 2161-2176, December.
    4. Iñigo Pombo & Leire Godino & Jose Antonio Sánchez & Rafael Lizarralde, 2020. "Expectations and limitations of Cyber-Physical Systems (CPS) for Advanced Manufacturing: A View from the Grinding Industry," Future Internet, MDPI, vol. 12(9), pages 1-15, September.
    5. Vitor Hugo dos Santos Filho & Luis Maurício Martins de Resende & Joseane Pontes, 2024. "Development of a Theoretical Model for Digital Risks Arising from the Implementation of Industry 4.0 (TMR-I4.0)," Future Internet, MDPI, vol. 16(6), pages 1-32, June.
    6. Lijun Zhang & Kai Liu & Jian Liu, 2018. "Multidiscipline Integrated Platform Based on Probabilistic Analysis for Manufacturing Engineering Processes," Future Internet, MDPI, vol. 10(8), pages 1-10, July.
    7. Anna Kwiotkowska & Magdalena Gębczyńska, 2022. "Job Satisfaction and Work Characteristics Combinations in Industry 4.0 Environment—Insight from the Polish SMEs in the Post–Pandemic Era," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    8. Isam Mashhour Al Jawarneh & Luca Foschini & Paolo Bellavista, 2023. "Efficient Integration of Heterogeneous Mobility-Pollution Big Data for Joint Analytics at Scale with QoS Guarantees," Future Internet, MDPI, vol. 15(8), pages 1-28, August.
    9. Domaszewicz, Jaroslaw & Parzych, Dariusz, 2022. "Intra-Company Crowdsensing: Datafication with Human-in-the-Loop," MPRA Paper 112608, University Library of Munich, Germany.
    10. Aldona Kluczek & Patrycja Żegleń & Daniela Matušíková, 2021. "The Use of Prospect Theory for Energy Sustainable Industry 4.0," Energies, MDPI, vol. 14(22), pages 1-29, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:12:y:2020:i:3:p:56-:d:334831. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.