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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The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.
References
Jian W, Jiaxin L, Zhongnan Z, Guosheng Z (2021) A task recommendation framework for heterogeneous mobile crowdsensing. J Supercomput 77:12121–12142
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
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
Zhang X, Xue G, Yu R, Yang D, Tang J (2015) Truthful incentive mechanisms for crowdsourcing. In: 2015 IEEE Conference on Computer Communications (INFOCOM)
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
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
Nisan N, Ronen A (2001) Algorithmic mechanism design. Games Econom Behav 35:166–196
Krishna V (2009) Auction theory, 2nd edn. Academic Press, Cambridge
Vickrey W (1961) Counterspeculation, auctions, and competitive sealed tenders. J Finance 16(1):8–37
Clarke EH (1971) Multipart pricing of public goods. Public Choice 11(1):17–33
Groves T (1973) Incentives in teams. Econom J Econom Soc 41(4):617–631
Lehmann D, O’callaghan LI, Shoham Y (2002) Truth revelation in approximately efficient combinatorial auctions. J ACM 49(5):577–602
Samimi P, Teimouri Y, Mukhtar M (2016) A combinatorial double auction resource allocation model in cloud computing. Inf Sci 357(C):201–216
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
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
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
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
Liu X, Liu J, Wu H (2021) Energy-efficient task allocation of heterogeneous resources in mobile edge computing. IEEE Access 9:119700–119711
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
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
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
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
Gong X, Shroff NB (2020) Truthful data quality elicitation for quality-aware data crowdsourcing. IEEE Trans Control Netw Syst 7(1):326–337
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
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
Zhao Y, Song W (2017) Truthful mechanisms for message dissemination via device-to-device communications. IEEE Trans Veh Technol 66(11):10307–10321
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
Parkes DC (2007) Online mechanisms. In: Nisan N, Roughgarden T, Tardos É, Vazirani VV (eds) Algorithmic game theory. Cambridge University Press, New York
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
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
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
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
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
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
Liu X, Liu J (2022) A truthful online mechanism for virtual machine provisioning and allocation in clouds. Clust Comput 25:1095–1109
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
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
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
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
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
IBM ILOG CPLEX Optimizer, 15 August 2017, Available: https://www.ibm.com/analytics/cplex-optimizer/
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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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