Incentive mechanisms for mobile crowd sensing based on supply-demand relationship

Article
  • 4 Downloads
Part of the following topical collections:
  1. Special Issue on Network Coverage

Abstract

Mobile crowd sensing has become an efficient paradigm for performing large scale sensing tasks. An incentive mechanism is important for the mobile crowd sensing system to stimulate participants, and to achieve good service quality. In this paper, we design the incentive mechanisms for mobile crowd sensing, where the price and supply of the resource contributed by the smartphone users are determined by the supply-demand relationship of market. We present two models of mobile crowd sensing: the resource model and the budget model. In the resource model, each sensing task has the least resource demand. In the budget model, each task has a budget constraint. We design an incentive mechanism for each of the two models. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed incentive mechanisms achieve computational efficiency, profitability, individual rationality, and truthfulness. Moreover, the designed mechanisms can satisfy the properties of non-monopoly and constant discount under certain conditions.

Keywords

Mobile crowd sensing Incentive mechanism Supply-demand relationship 

Notes

Acknowledgments

This work was supported in part by the NSFC (No. 61472193, 61502251), and NSF (No. 1444059, 1717315).

References

  1. 1.
    Ganti R, Ye F, Lei H (2011) Mobile crowdsensing: current state and future challenges. IEEE Commun Mag 49(11):32–39CrossRefGoogle Scholar
  2. 2.
    Eisenman S, Miluzzo E, Lane N, Peterson R, Ahn G, Campbell A (2007) The Bikenet Mobile Sensing System for Cyclist Experience Mapping. Proc SenSys 2007, Sydney, p 87–101Google Scholar
  3. 3.
    Miluzzo E, Lane N, Fodor K, Peterson R, Eisenman S, Lu H, Musolesi M, Zheng X, Campbell A (2008) Sensing Meets Mobile Social Networks: The Design, Implementation and Evaluation of the CenceMe Application. Proc SenSys 2008, Raleigh p 37–51Google Scholar
  4. 4.
    Mun M, Reddy S, Shilton K, Yau N, Burke J, Estrin D, Hansen M, Howard E, West R, Boda P (2009) PIER, the personal environmental impact report, as a platform for participatory sensing systems research. Proc MobiSys 2009, Krakow, p 55–68Google Scholar
  5. 5.
    Carrapetta J, Youdale N, Chow A, Sivaraman V Haze Watch Project, Online: http://www.pollution.ee.unsw.edu.au
  6. 6.
    Rana R, Chou C, Kanhere S, Bulusu N, Hu W (2010) Earphone: An end-to-end participatory urban noise mapping. Proc IPSN 2010, Stockholm, p 105–116Google Scholar
  7. 7.
    Koukoumidis E, Peh L, Martonosi M (2011) SignalGuru: Leveraging Mobile Phones for Collaborative Traffic Signal Schedule Advisory. Proc MobiSys 2011, Bethesda, p 127–140Google Scholar
  8. 8.
    Liu Y, Zhao Y, Chen L, Pei J, Han J (2012) Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays. IEEE TPDS 23(11):2138–2149Google Scholar
  9. 9.
    Yang Z, Wu C, and Liu Y (2012) Locating in Fingerprint Space: Wireless Indoor Localization with Little Human Intervention. Proc MobiCom 2012, Istanbul, p 269–280Google Scholar
  10. 10.
    Zhou P, Zheng Y, Li M (2010) How Long to Wait?: Predicting Bus Arrival Time with Mobile Phone Based Participatory Sensing. Proc MobiSys 2012, Ambleside, p 1228–1240Google Scholar
  11. 11.
    Wang K, Gu L, Sun Y, Chen H, Leung VCM (2017) Crowdsourcing-based content-centric network: a social perspective. IEEE Netw 31(5):12–18Google Scholar
  12. 12.
    Singer Y Budget feasible mechanisms. Proc IEEE FOCS 2010, 2010:765–774Google Scholar
  13. 13.
    Singer Y, Mittal M (2013) Pricing mechanisms for crowdsourcing markets. Proc WWW 2013, Rio de Janeiro, p 1157–1166Google Scholar
  14. 14.
    Yang D, Xue G, Fang X, Tang J (2012) Crowdsourcing to Smartphones: Incentive Mechanism Design for Mobile Phone Sensing. Proc MobiCom, 2012, Istanbul, p 173–184Google Scholar
  15. 15.
    Zhang X, Xue G, Yu R, Yang D, Tang J (2015) Truthful Incentive Mechanisms for Crowdsourcing. Proc INFOCOM 2015, Hong Kong, p 2830–2838Google Scholar
  16. 16.
    Feng Z, Zhu Y, Zhang Q, Ni L, Vasilakos A (2014) TRAC: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing. Proc INFOCOM 2014, Toronto, p 1231–1239Google Scholar
  17. 17.
    Zhao D, Li X, Ma H (2014) How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint. Proc INFOCOM 2014, Toronto, p 1213–1221Google Scholar
  18. 18.
    Luo T, Kanhere S, Tan H, Wu F, Wu H (2015) Crowdsourcing with Tullock Contests: A New Perspective. Proc INFOCOM 2015, Hong Kong, p 2515–2523Google Scholar
  19. 19.
    Peng D, Wu F, Chen G (2015) Pay as How Well You Do: A Quality Based Incentive Mechanism for Crowdsensing. Proc MobiHoc 2015, Hang Zhou, p 177–186Google Scholar
  20. 20.
    Lorenzo B, Gomez-Cuba F, Garcia-Rois J (2015) A Microeconomic Approach to Data Trading in User Provided Networks. Proc Globecom 2015, San Diego, p 1–7Google Scholar
  21. 21.
    Niyato D, Hossain E (2010) A microeconomic model for hierarchical bandwidth sharing in dynamic spectrum access networks. IEEE Trans Comput 59(7):865–877MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Xu J, Xiang J, Li Y (2017) Incentivize maximum continuous time interval coverage under budget constraint in mobile crowd sensing. Wirel Netw 23(5):1549–1562CrossRefGoogle Scholar
  23. 23.
    Xu J, Xiang J, Yang D (2015) Incentive mechanisms for time window dependent tasks in mobile Crowdsensing. IEEE TWC 14(11):6353–6364Google Scholar
  24. 24.
    Xu J, Fu J, Yang D, Xu L, Wang L, Li T (2017) FIMI:a constant frugal incentive mechanism for time window coverage in mobile Crowdsensing. J Comput Sci Technol 32(5):919–935CrossRefGoogle Scholar
  25. 25.
    Xu J, Rao Z, Xu L, Yang D, Li T (2017) Mobile crowd sensing via online communities: incentive mechanisms for multiple cooperative tasks, Proc IEEE MASS, p 171–179Google Scholar
  26. 26.
    Xu J, Xiang J, Chen X (2016) ODMBP: behavior forwarding for multiple property destinations in mobile social networks. Mob Inf Syst 2016:1–11Google Scholar
  27. 27.
    Gillis M Basic Supply and Demand. Wolfram Demonstrations Project, http://demonstrations.wolfram.com/BasicSupplyAndDemand/
  28. 28.
    Blumrosen L, Nisan N (2007) Combinatorial auctions (a survey). In Nisan N, Roughgarden T, Tardos E, Vazirani V (2007) Algorithmic Game Theory. Cambridge University PressGoogle Scholar
  29. 29.
    Wang R, Chen F, Chen Z, Li T, Harari G, Tignor S, Zhou X, Zeev D, Campbell A (2014) StudentLife: Assessing Mental Health, Academic Performance and Behavioral Trends of College Students using Smartphones. Proc UbiComp 2014, Seattle, p 3–14Google Scholar
  30. 30.
    Koutsopoulos I Optimal incentive-driven design of participatory sensing systems. Proc INFOCOM 2013, 2013:1402–1410Google Scholar
  31. 31.
    Wen Y, Shi J, Zhang Q, Tian X, Huang Z, Yu H, Cheng Y, Shen X (2015) Quality-driven auction-based incentive mechanism for mobile crowd sensing. IEEE Trans Veh Technol 64(9):4203–4214CrossRefGoogle Scholar
  32. 32.
    Wang K, Qi X, Shu L, Deng DJ, Rodrigues JJPC (2016) Toward trustworthy crowdsourcing in social internet of things. IEEE Wirel Commun:30(5):30–30(5):36Google Scholar
  33. 33.
    Xu J, Li H, Li Y, Yang D and Li T (2017) Incentivizing the biased requesters: truthful task assignment mechanisms in crowdsourcing, Proc IEEE SECON, p 1–9Google Scholar
  34. 34.
    Wang Z, Tan R, Hu J, Zhao J, Wang Q, Xia F, Niu X (2018) Heterogeneous incentive mechanism for time-sensitive and location-dependent Crowdsensing networks with random arrivals. Comput Netw 131(2):96–109CrossRefGoogle Scholar
  35. 35.
    Chen H, Lou W, Wang Z, Wang Q (2016) A secure credit-based incentive mechanism for message forwarding in noncooperative DTNs. IEEE Trans Veh Technol 65(8):6377–6388CrossRefGoogle Scholar
  36. 36.
    He S, Shin D, Zhang J, Chen J (2014) Toward Optimal Allocation of Location Dependent Tasks in Crowdsensing. Proc INFOCOM 2014, Toronto, p 745–753Google Scholar
  37. 37.
    He S, Shin D, Zhang J, Chen J (2017) Near-optimal allocation algorithms for location-dependent tasks in Crowdsensing. IEEE Trans Veh Technol 66(4):3392–3405CrossRefGoogle Scholar
  38. 38.
    Tham C, Luo T (2015) Quality of contributed service and market equilibrium for participatory sensing. IEEE Trans Mob Comput 14(4):29–42CrossRefGoogle Scholar
  39. 39.
    He S, Shin D, Zhang J, Chen J, Lin P (2017) An exchange market approach to mobile Crowdsensing: pricing, task allocation, and Walrasian equilibrium. IEEE Selected Areas in Commun 35(4):921–934CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Jiangsu Key Laboratory of Big Data Security & Intelligent ProcessingUniversity of Posts and TelecommunicationsNanjingChina
  2. 2.Department of Computer ScienceColorado School of MinesGoldenUSA

Personalised recommendations