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An online mechanism for task allocation and pricing in crowd sensing systems

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Abstract

In crowd sensing systems, mobile users provide requesters with access to the resources of their mobile devices, such as the core processor, memory, and camera, to execute tasks in return for monetary payment. Existing works consider the offline setting where information about mobile users and requesters is publicly known. However, this assumption does not hold for crowd sensing systems in the real world, where mobile users and requesters can arrive and leave at any time. We address the problem of online task allocation and pricing in crowd sensing systems without making any assumptions about the future information of mobile users and requesters. We formulate this problem in an auction-based online setting and propose a feasible and online double auction mechanism. The proposed online mechanism considers one-to-many mapping, which permits multiple mobile devices to work together to complete the same task at different times in order to improve resource utilization. In addition, we show that the proposed mechanism maintains individual rationality, budget-balance, and computational tractability. Furthermore, we analyze the approximation ratio of the proposed approximation algorithm. The experimental results show that a user cannot increase her utility by untruthful declaration, and the proposed mechanism brings more economic benefit for the auctioneer and stimulates users to join the system.

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Data availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Jian W, Jiaxin L, Zhongnan Z, Guosheng Z (2021) A task recommendation framework for heterogeneous mobile crowdsensing. J Supercomput 77:12121–12142

    Article  Google Scholar 

  2. Chen L, Chen T, Chen H, Tian M (2019) Crowdsourced children monitoring and finding with holding up detection based on internet-of-things technologies. IEEE Sens J 19(24):12407–12417

    Article  Google Scholar 

  3. Li P, Guo S, Stojmenovic I (2016) A truthful double auction for device-to-device communications in cellular networks. IEEE J Sel Areas Commun 32(1):71–81

    Article  Google Scholar 

  4. Zhang X, Xue G, Yu R, Yang D, Tang J (2015) Truthful incentive mechanisms for crowdsourcing. In: 2015 IEEE Conference on Computer Communications (INFOCOM)

  5. Jin A, Song W, Wang P, Niyato D, Ju P (2016) Auction mechanisms toward efficient resource sharing for cloudlets in mobile cloud computing. IEEE Trans Serv Comput 9(6):895–909

    Article  Google Scholar 

  6. Liu X, Liu J (2021) A truthful double auction mechanism for multi-resource allocation in crowd sensing systems. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2021.3075541

    Article  Google Scholar 

  7. Nisan N, Ronen A (2001) Algorithmic mechanism design. Games Econom Behav 35:166–196

    Article  MathSciNet  MATH  Google Scholar 

  8. Krishna V (2009) Auction theory, 2nd edn. Academic Press, Cambridge

    Google Scholar 

  9. Vickrey W (1961) Counterspeculation, auctions, and competitive sealed tenders. J Finance 16(1):8–37

    Article  MathSciNet  Google Scholar 

  10. Clarke EH (1971) Multipart pricing of public goods. Public Choice 11(1):17–33

    Article  Google Scholar 

  11. Groves T (1973) Incentives in teams. Econom J Econom Soc 41(4):617–631

    MathSciNet  MATH  Google Scholar 

  12. Lehmann D, O’callaghan LI, Shoham Y (2002) Truth revelation in approximately efficient combinatorial auctions. J ACM 49(5):577–602

    Article  MathSciNet  MATH  Google Scholar 

  13. Samimi P, Teimouri Y, Mukhtar M (2016) A combinatorial double auction resource allocation model in cloud computing. Inf Sci 357(C):201–216

    Article  MATH  Google Scholar 

  14. Kumar D, Baranwal G, Raza Z, Vidyarthj DP (2017) A systematic study of double auction mechanisms in cloud computing. J Syst Softw 125(C):234–255

    Article  Google Scholar 

  15. Alqerm I, Shihada B (2018) Sophisticated online learning scheme for green resource allocation in 5G heterogeneous cloud radio access networks. IEEE Trans Mob Comput 17:2423–2437

    Article  Google Scholar 

  16. Li X, Zhao L, Yu K, Aloqaily M, Jararweh Y (2021) A cooperative resource allocation model for IoT applications in mobile edge computing. Comput Commun 173:183–191

    Article  Google Scholar 

  17. Zhao L, Zhao W, Hawbani A, Al-Dubai AY, Min G, Zomaya AY, Gong C (2020) Novel online sequential learning-based adaptive routing for edge software-defined vehicular networks. IEEE Trans Wireless Commun 20(5):2991–3004

    Article  Google Scholar 

  18. Liu X, Liu J, Wu H (2021) Energy-efficient task allocation of heterogeneous resources in mobile edge computing. IEEE Access 9:119700–119711

    Article  Google Scholar 

  19. Xiong H, Zhang D, Chen G, Wang L, Gauthier V, Barnes LE (2016) iCrowd: near-optimal task allocation for piggyback crowdsensing. IEEE Trans Mob Comput 15(8):2010–2022

    Article  Google Scholar 

  20. Sun P, Wang Z, Feng Y, Wu L, Li Y, Qi H, Wang Z (2020) Towards Personalized privacy-preserving incentive for truth discovery in crowdsourced binary-choice question answering. In: 2020 IEEE Conference on Computer Communications

  21. Wang X, Chen X, Wu W (2017) Towards truthful auction mechanisms for task assignment in mobile device clouds. In: 2017 IEEE Conference on Computer Communications

  22. Jin H, Su L, Xiao H, Nahrstedt K (2020) Incentive mechanism for privacy-aware data aggregation in mobile crowd sensing systems. IEEE/ACM Trans Netw 26(5):2019–2032

    Article  Google Scholar 

  23. Gong X, Shroff NB (2020) Truthful data quality elicitation for quality-aware data crowdsourcing. IEEE Trans Control Netw Syst 7(1):326–337

    Article  MathSciNet  MATH  Google Scholar 

  24. Chichin S, Bao V, Kowalczyk R (2017) Towards efficient and truthful market mechanisms for double-sided cloud markets. IEEE Trans Serv Comput 10(1):37–51

    Article  Google Scholar 

  25. Hajiesmaili MH, Deng L, Chen M, Li Z (2017) Incentivizing device-to-device load balancing for cellular networks: an online auction design. IEEE J Sel Areas Commun 35(2):265–279

    Article  Google Scholar 

  26. Zhao Y, Song W (2017) Truthful mechanisms for message dissemination via device-to-device communications. IEEE Trans Veh Technol 66(11):10307–10321

    Article  Google Scholar 

  27. Ray KB, Saha A, Khatua S, Roy S (2021) Quality and profit assured trusted cloud federation formation: game theory based approach. IEEE Trans Serv Comput 14(3):805–819

    Article  Google Scholar 

  28. Parkes DC (2007) Online mechanisms. In: Nisan N, Roughgarden T, Tardos É, Vazirani VV (eds) Algorithmic game theory. Cambridge University Press, New York

    Google Scholar 

  29. Zhang H, Jiang H, Li B, Liu F, Vasilakos AV, Liu J (2016) A framework for truthful online auctions in cloud computing with heterogeneous user demands. IEEE Trans Comput 65(3):805–818

    Article  MathSciNet  MATH  Google Scholar 

  30. Shi W, Zhang L, Wu C, Li Z (2017) An online auction mechanism for dynamic virtual cluster provisioning in geo-distributed clouds. IEEE Trans Parallel Distrib Syst 28(3):677–688

    Article  Google Scholar 

  31. Shi W, Zhang L, Wu C, Li Z, Lau FCM (2016) An online auction framework for dynamic resource provisioning in cloud computing. IEEE/ACM Trans Netw 24(4):2060–2073

    Article  Google Scholar 

  32. Mashayekhy L, Nejad MM, Grosu D, Vasilakos VA (2015) An online mechanism for resource allocation and pricing in clouds. IEEE Trans Comput 65(4):1172–1184

    Article  MathSciNet  MATH  Google Scholar 

  33. Zhou R, Li Z, Wu C, Huang Z (2017) An efficient cloud market mechanism for computing jobs with soft deadlines. IEEE/ACM Trans Netw 25(2):793–805

    Article  Google Scholar 

  34. Chen X, Hu X, Liu YT, Ma W, Qin T, Tang P, Wang C, Zheng B (2016) Efficient mechanism design for online scheduling. J Artif Intell Res 56:429–461

    Article  MathSciNet  MATH  Google Scholar 

  35. Liu X, Liu J (2022) A truthful online mechanism for virtual machine provisioning and allocation in clouds. Clust Comput 25:1095–1109

    Article  Google Scholar 

  36. Patel YS, Malwi Z, Nighojkar A, Misra R (2021) Truthful online double auction based dynamic resource provisioning for multi-objective trade-offs in IaaS clouds. Clust Comput 24:1855–1879

    Article  Google Scholar 

  37. Luo G, Yan K, Zheng X, Tian L, Cai Z (2020) Preserving adjustable path privacy for task acquisition in mobile crowdsensing systems. Inf Sci 527:602–619

    Article  MathSciNet  MATH  Google Scholar 

  38. Kucuk K, Bayilmis C, Sonmez AF, Kacar S (2020) Crowd sensing aware disaster framework design with IoT technologies. J Ambient Intell Humaniz Comput 11:1709–1725

    Article  Google Scholar 

  39. Song L, Niyato D, Han Z, Hossian E (2014) Game-theoretic resource allocation methods for device-to-device communication. IEEE Wirel Commun 21(3):136–144

    Article  Google Scholar 

  40. Wang J, Jiang C, Bie Z, Quek TQS, Ren Y (2017) Mobile data transactions in device-to-device communication networks: pricing and auction. IEEE Wireless Commun Lett 5(3):300–303

    Article  Google Scholar 

  41. IBM ILOG CPLEX Optimizer, 15 August 2017, Available: https://www.ibm.com/analytics/cplex-optimizer/

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Acknowledgements

This work was supported in part by the Chinese Natural Science Foundation under Grant 11361048, in part by the Yunnan Natural Science Foundation under Grant 2017FH001-014, in part by the Yunnan Science Foundation under Grant 2019J0613, and in part by the Qujing Normal University Science Foundation under Grant ZDKC2016002.

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Authors and Affiliations

  1. School of Information Engineering, Qujing Normal University, Qujing, 655000, People’s Republic of China

    Xi Liu

  2. Institute of Applied Mathematics, Qujing Normal University, Qujing, 655000, People’s Republic of China

    Jun Liu

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  1. Xi Liu
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  2. Jun Liu
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Correspondence to Xi Liu.

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Liu, X., Liu, J. An online mechanism for task allocation and pricing in crowd sensing systems. J Supercomput 78, 17594–17618 (2022). https://doi.org/10.1007/s11227-022-04564-7

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  • DOI: https://doi.org/10.1007/s11227-022-04564-7

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