Skip to main content
SpringerLink
Log in

A Pricing Approach Toward Incentive Mechanisms for Participant Mobile Crowdsensing in Edge Computing

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Owing to the acceleration of urbanization and the rapid development of mobile Internet, mobile crowd sensing (MCS) has been recognized as a promising method to acquire massive volume of data. However, due to the massive perception data in participatory MCS system, the data privacy of mobile users and the response speed of data processing in cloud platform are hard to guarantee. Stimulating the enthusiasm of participants could be challenging at the same time. In this paper, we first propose a three-layer MCS architecture which introduces edge servers to process raw data, protects users’ privacy and improve response time. In order to maximize social welfare, we consider two-stage game in three-layer MCS architecture. Then, we formulate a Markov decision process (MDP)-based social welfare maximization model and investigate a convex optimization pricing problem in the proposed three-layer architecture. Combined with the market economy model, the problem could be considered as a Walrasian equilibrium problem according to market exchange theory. We propose a pricing approach toward incentive mechanisms based on Lagrange multiplier method, dual decomposition and subgradient iterative method. Finally, we derive the experimental data from real-world dataset and extensive simulations demonstrate the performance of our proposed method.

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

Similar content being viewed by others

References

  1. IDC (2020) should bring the market back to growth as we expect many regions to rebound, according to IDC. https://www.idc.com/getdoc.jsp?containerId=US44916519

  2. Gao H, Huang W, Yang X (2019) Applying probabilistic model checking to path planning in an intelligent transportation system using mobility trajectories and their statistical data. Intell Autom Soft Comput 25(3):547–559

    Google Scholar 

  3. Li W, Liao K, He Q, Xia Y* (2019) Performance-aware Cost-effective Resource Provisioning for Future Grid IoT-Cloud System. J Energy Eng 145(5):1–13

    Article  Google Scholar 

  4. Qi L, He Q, Chen F, et al (2019) Finding All You Need: Web API s Recommendation in Web of Things through Keywords Search. IEEE Trans Comput Social Syst 6(5):1063–1072

    Article  Google Scholar 

  5. Akyildiz IF, Su W, Sankarasubramaniam Y, et al (2002) A survey on sensor networks. IEEE Commun Magazine 40(8):102–114

    Article  Google Scholar 

  6. Guo B, Wang Z, Yu Z, et al (2015) Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm. ACM Comput Surveys (CSUR) 48(1):7

    Article  Google Scholar 

  7. Wu Y, Zeng J, Peng H, Chen H, Li C (2016) Survey on Incentive Mechanisms for Crowd Sensing. J Software 27(8):2025–2047

    MathSciNet  Google Scholar 

  8. Nie J, Luo J, Xiong Z, et al (2019) A Stackelberg Game Approach Toward Socially-Aware Incentive Mechanisms for Mobile Crowdsensing. IEEE Trans Wireless Commun 18(1):724–738

    Article  Google Scholar 

  9. Pouryazdan M, Kantarci B, Soyata T, et al (2017) Quantifying User Reputation Scores, Data Trustworthiness, and User Incentives in Mobile Crowd-Sensing. IEEE Access 5(99):1382–1397

    Article  Google Scholar 

  10. Yin Y, Chen L, Xu Y, Wan J (2018) Location-Aware Service recommendation with enhanced probabilistic matrix factorization. IEEE Access 6:62815–62825

    Article  Google Scholar 

  11. Chen Y, Zhang N, Zhang Y, et al (2019) TOFFEE: task offloading and frequency scaling for energy efficiency of mobile devices in mobile edge computing. IEEE Trans Cloud Comput https://doi.org/10.1109/TCC.2019.2923692

  12. Qi L, Chen Y, Yuan Y, et al (2019) A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems World Wide Web 1–23

  13. Sun J (2013) Current states and challenges of work, arXiv:1310.8364

  14. Yang D, Xue G, Fang X, et al (2016) Incentive mechanisms for crowdsensing: Crowdsourcing with smartphones. IEEE/ACM Trans Netw (TON) 24(3):1732–1744

    Article  Google Scholar 

  15. Jin H, Su L, Chen D, et al (2018) Thanos: Incentive mechanism with quality awareness for mobile crowd sensing. IEEE Trans Mob Comput 1–14

  16. Peng D, Wu F, Chen G (2017) Data quality guided incentive mechanism design for crowdsensing. IEEE trans Mob Comput 17(2):307–319

    Article  Google Scholar 

  17. Gao G, Xiao M, Wu J, et al (2018) Truthful incentive mechanism for nondeterministic crowdsensing with vehicles. IEEE Trans Mob Comput 17(12):2982–2997

    Article  Google Scholar 

  18. Zheng Z, Peng Y, Wu F, et al (2017) An online pricing mechanism for mobile crowdsensing data markets. Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing 26:1–10

    Google Scholar 

  19. Chen Y, Huang J, Lin C, et al (2015) A Partial Selection Methodology for Efficient QoS-Aware Service Composition. IEEE Trans Serv Comput 8(3):384–397

    Article  Google Scholar 

  20. Gao H, Huang W, Duan Y, Yang X, Zou Q (2019) Research on Cost-Driven services composition in an uncertain environment. J Internet Technol (JIT) 20(3):755–769

    Google Scholar 

  21. Duan X, Zhao C, He S, et al (2016) Distributed algorithms to compute Walrasian equilibrium in mobile crowdsensing. IEEE Trans Ind Electron 64(5):4048–4057

    Article  Google Scholar 

  22. He S, Shin DH, Zhang J, et al (2017) An exchange market approach to mobile crowdsensing: pricing, task allocation, and walrasian equilibrium. IEEE J Selected Areas Commun 35(4):921– 934

    Article  Google Scholar 

  23. Jin H, Su L, Chen D, et al (2018) Thanos: Incentive mechanism with quality awareness for mobile crowd sensing. IEEE Trans Mob Comput

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

    Article  Google Scholar 

  25. Xia Y, Zhou M, Luo X, et al (2015) Stochastic Modeling and Quality Evaluation of Infrastructure-as-a-Service Clouds. IEEE Trans Autom Sci Eng 12(1):162–170

    Article  Google Scholar 

  26. Chen Y, Zhang N, Zhang Y, et al (2019) Dynamic computation offloading in edge computing for internet of things. IEEE Internet Things J 6(3):4242–4251

    Article  Google Scholar 

  27. Wang Y, Liu H, Zheng W, et al (2019) Multi-Objective Workflow scheduling with Deep-Q-Network-Based Multi-Agent reinforcement learning. IEEE Access 7:39974–39982

    Article  Google Scholar 

  28. Chen X, Jiao L, Li W, et al (2015) Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans Netw 24(5):2795–2808

    Article  Google Scholar 

  29. Liu Y, Xu C, Zhan Y, et al (2017) Incentive mechanism for computation offloading using edge computing: A Stackelberg game approach. Comput Netw 129:399–409

    Article  Google Scholar 

  30. Yu H, Cheung MH, Gao L, et al (2016) Economics of public Wi-Fi monetization and advertising, IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, IEEE 1–9

  31. Gong W, Qi L, Xu Y (2018) Privacy-aware multidimensional mobile service quality prediction and recommendation in distributed fog environment Wireless Communications and Mobile Computing

  32. Zhou Z, Liao H, Gu B, et al (2018) Robust mobile crowd sensing: When deep learning meets edge computing. IEEE Netw 32(4):54–60

    Article  Google Scholar 

  33. Duan X, Zhao C, He S, et al (2017) Distributed algorithms to compute walrasian equilibrium in mobile crowdsensing. IEEE Trans Ind Electron 64(5):4048–4057

    Article  Google Scholar 

  34. Esser E, Zhang X, Chan TF (2010) A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science. SIAM J Imaging Sci 3(4):1015–1046

    Article  MathSciNet  MATH  Google Scholar 

  35. Chen Y, Zhang N, Zhang Y, et al (2019) Energy Efficient Dynamic Offloading in Mobile Edge Computing for Internet of Things. IEEE Trans Cloud Comput, https://doi.org/10.1109/TCC.2019.2898657

  36. Leskovec J , Krevl A Snap Datasets: Stanford Large Network Dataset Collection. [Online]. Available: http://snap.stanford.edu/data

  37. Li J, Cai Z, Wang J, et al (2018) Truthful Incentive Mechanisms for Geographical Position Conflicting Mobile Crowdsensing Systems, IEEE Transactions on Computational Social Systems, 1–11

  38. Duan X, Zhao C, He S, et al (2016) Distributed algorithms to compute Walrasian equilibrium in mobile crowdsensing. IEEE Trans Ind Electron 64(5):4048–4057

    Article  Google Scholar 

  39. Yin Y, Chen L, Xu Y, et al (2019) Qos prediction for service recommendation with deep feature learning in edge computing environment. Mob Netw Appl 1–11

Download references

Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (No. 61872044, 61872219, 61902029), Beijing Municipal Program for Top Talent Cultivation (CIT&TCD201804055), and the Natural Science Foundation of Shandong Province (ZR2019MF001).

Author information

Authors and Affiliations

  1. Computer School, Beijing Information Science and Technology University, Beijing, China

    Xin Chen, Chao Tang, Zhuo Li, Ying Chen & Shuang Chen

  2. School of Information Science and Engineering, Qufu Normal University, Jining, China

    Lianyong Qi

Authors
  1. Xin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chao Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhuo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lianyong Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ying Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shuang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lianyong Qi.

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

Chen, X., Tang, C., Li, Z. et al. A Pricing Approach Toward Incentive Mechanisms for Participant Mobile Crowdsensing in Edge Computing. Mobile Netw Appl 25, 1220–1232 (2020). https://doi.org/10.1007/s11036-020-01538-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-020-01538-y

Keywords

Search

Navigation