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DPIM: Dynamic Pricing Incentive Mechanism for Mobile Crowd Sensing

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2023)

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Abstract

As an emerging paradigm for collecting sensory data, Mobile Crowd Sensing (MCS) technology has found widespread application. The successful application of MCS technology relies not only on the active participation of participants but also on the continuous demand for sensing task from data requestors. However, existing researchers predominantly focus on designing participant incentive mechanisms to attract participant to engage in the sensing activities, while the incentive mechanisms for data requestors are rarely addressed. To address the gap, we conceptualize the interactions between data requestors and participants as a queueing process. Building upon utility theory, we propose Dynamic Pricing Incentive Mechanism (DPIM) that dynamically offers optimal incentive guidance to the sensing platform. Moreover, we devise two distinct utility optimization modes for data requestors: one for maximizing their utility and the other for achieving utility equilibrium. These modes are tailored to meet the distinct utility requirement of the sensing platform and data requestors. Through simulations and theoretical analysis, we demonstrate that DPIM effectively provides incentives for the sensing platform across different utility modes.

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References

  1. Perez, A.J., Zeadally, S.: Secure and privacy-preserving crowdsensing using smart contracts: issues and solutions. Comput. Sci. Rev. 43, 100450 (2022)

    Article  Google Scholar 

  2. Middya, A.I., Dey, P., Roy, S.: IoT-based crowdsensing for smart environments. In: Internet of Things for Smart Environments, pp. 33–58 (2022)

    Google Scholar 

  3. Capponi, A., Fiandrino, C., Kantarci, B., et al.: A survey on mobile crowdsensing systems: challenges, solutions, and opportunities. IEEE Commun. Surv. Tutor. 21(3), 2419–2465 (2019)

    Article  Google Scholar 

  4. Wu, Y., Zeng, J.R., Peng, H., et al.: Survey on incentive mechanisms for crowd sensing. J. Softw. 27(8), 2025–2047 (2016)

    MathSciNet  Google Scholar 

  5. Sun, Y., Ding, W., Shu, L., et al.: On enabling mobile crowd sensing for data collection in smart agriculture: a vision. IEEE Syst. J. 16(1), 132–143 (2021)

    Article  Google Scholar 

  6. Sun, Y., Nurellari, E., Ding, W., et al.: A partition-based mobile-crowdsensing-enabled task allocation for solar insecticidal lamp internet of things maintenance. IEEE Internet Things J. 9(20), 20547–20560 (2022)

    Article  Google Scholar 

  7. Fascista, A.: Toward integrated large-scale environmental monitoring using WSN/UAV/crowdsensing: a review of applications, signal processing, and future perspectives. Sensors 22(5), 1824 (2022)

    Article  Google Scholar 

  8. Shang, L., Zhang, Y., Ye, Q., et al.: Smartwatersens: a crowdsensing-based approach to groundwater contamination estimation. In: IEEE International Conference on Smart Computing (SMARTCOMP), pp. 48–55 (2022)

    Google Scholar 

  9. Jiang, Z., Zhu, H., Zhou, B., et al.: CrowdPatrol: a mobile crowdsensing framework for traffic violation hotspot patrolling. IEEE Trans. Mob. Comput. (2021)

    Google Scholar 

  10. Plašilová, A., Procházka, J.: Crowdsensing technologies for optimizing passenger flows in public transport. In: 1st International Conference on Advanced Innovations in Smart Cities (ICAISC), pp. 1–6 (2023)

    Google Scholar 

  11. She, R.: Survey on incentive strategies for mobile crowdsensing system. In: IEEE 11th International Conference on Software Engineering and Service Science (ICSESS), pp. 511–514. IEEE (2020)

    Google Scholar 

  12. Wang, K., Chen, Z., Zhang, L., Liu, J., Li, B.: Incentive mechanism for improving task completion quality in mobile crowdsensing. Electronics 12(4), 1037 (2023)

    Article  Google Scholar 

  13. Wang, J., Liu, H., Dong, X., et al.: Personalized location privacy trading in double auction for mobile crowdsensing. IEEE Internet Things J. 10(10), 8971–8983 (2022)

    Article  Google Scholar 

  14. Ng, J.S., Lim, W.Y.B., Garg, S., et al.: Collaborative coded computation offloading: an all-pay auction approach. In: ICC 2021-IEEE International Conference on Communications, pp. 1–6 (2021)

    Google Scholar 

  15. Gao, H.: Mean-field-game-based dynamic task pricing in mobile crowdsensing. IEEE Internet Things J. 9(18), 18098–18112 (2022)

    Article  Google Scholar 

  16. Liu, Y., Liu, F., Wu, H.T., et al.: PriDPM: privacy-preserving dynamic pricing mechanism for robust crowdsensing. Comput. Netw. 183, 107582 (2020)

    Article  Google Scholar 

  17. Han, K., Huang, H., Luo, J.: Quality-aware pricing for mobile crowdsensing. IEEE/ACM Trans. Networking 26(4), 1728–1741 (2018)

    Article  Google Scholar 

  18. Ma, G., Chen, H., Huang, Y., et al.: Utility-based heterogeneous user recruitment of multi-task in mobile crowdsensing. IEEE Internet Things J. (2023)

    Google Scholar 

  19. Yucel, F., Bulut, E.: User satisfaction aware maximum utility task assignment in mobile crowdsensing. Comput. Netw. 172, 107156 (2020)

    Article  Google Scholar 

  20. Liu, J., Huang, S., Li, D., Wen, S., Liu, H.: Addictive incentive mechanism in crowdsensing from the perspective of behavioral economics. IEEE Trans. Parallel Distrib. Syst. 33(5), 1109–1127 (2021)

    Article  Google Scholar 

  21. Sarker, S., Razzaque, M.A., Hassan, M.M., et al.: Optimal selection of crowdsourcing workers balancing their utilities and platform profit. IEEE Internet Things J. 6(5), 8602–8614 (2019)

    Article  Google Scholar 

  22. Banerjee, S., Riquelme, C., Johari, R.: Pricing in ride-share platforms: a queueing-theoretic approach. Available at SSRN 2568258 (2015)

    Google Scholar 

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Acknowledgments

This research was supported by “Leading Goose” R &D Program of Zhejiang under Grant No. 2023C03154, and “Pioneer” R &D Program of Zhejiang under Grant No. 2023C01029.

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China

    Weiwei Xing, Xinwei Yao & Chufeng Qi

Authors
  1. Weiwei Xing
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  2. Xinwei Yao
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  3. Chufeng Qi
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Corresponding author

Correspondence to Xinwei Yao .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool, Suzhou, China

    Xinheng Wang

  3. University of Peloponnese, Patra, Greece

    Nikolaos Voros

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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Xing, W., Yao, X., Qi, C. (2024). DPIM: Dynamic Pricing Incentive Mechanism for Mobile Crowd Sensing. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 561. Springer, Cham. https://doi.org/10.1007/978-3-031-54521-4_9

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  • DOI: https://doi.org/10.1007/978-3-031-54521-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-54520-7

  • Online ISBN: 978-3-031-54521-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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