Skip to main content
Springer Nature Link
Log in

A truthful budget feasible mechanism for IoT-based participatory sensing with incremental arrival of budget

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

We consider one of the scenarios in IoT-based mobile crowdsourcing in strategic setting, where we have single task requester (or task provider) and multiple task executors (or IoT devices). In this, a task requester have multiple heterogeneous tasks along with the publicly known budget. One constraint that is taken into consideration in terms of budget is that, a task provider is not having an entire budget available a priori, but only a part of the overall budget is available at the time of floating of the tasks. It means that, the overall budget comes incrementally in multiple phases. On the other hand, each IoT device reports valuation—the costs it charges for executing the tasks. The valuations of each of the IoT devices are private (only known to it and not known to others). Given this scenario, the goal is to determine a set of quality task executors for each of the tasks in such a way that the overall payment made to the task executors is less than or equal to the available budget. In this paper, a mechanism is designed that is both truthful and budget feasible. Along with truthfulness and budget feasibility, the mechanism helps in selecting the quality task executors for each task. For measuring the performance of the proposed mechanism on the basis of truthfulness and budget feasibility, the simulations are done.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. Here, budget means that the maximum amount a task requester can invest as the payment of the selected quality task executors.

  2. https://capcp.la.psu.edu/data-and-software/fcc-spectrum-auction-data/FCC_Auction82/FCC_Auction82d.zip/view.

References

  • Alfaro LD, Shavlovsky M, Polychronopoulos V (2016) Incentives for truthful peer grading. CoRR. arXiv:abs/1604.03178 (Eprint1604.03178)

  • Coulson S, Woods M, Scott M, Hemment D (2018) Making sense: empowering participatory sensing with transformation design. Des J 21(6):813–833. https://doi.org/10.1080/14606925.2018.1518111

    Article  Google Scholar 

  • Daniel F, Kucherbaev P, Cappiello C, Benatallah B, Allahbakhsh M (2018) Quality control in crowdsourcing: a survey of quality attributes, assessment techniques, and assurance actions. ACM Comput Surv 51(1):7:1–7:40. https://doi.org/10.1145/3148148

    Article  Google Scholar 

  • Duan Z, Tian L, Yan M, Cai Z, Han Q, Yin G (2017) Practical incentive mechanisms for iot-based mobile crowdsensing systems. IEEE Access 5:20383–20392. https://doi.org/10.1109/ACCESS.2017.2751304

    Article  Google Scholar 

  • Edelman B, Ostrovsky M, Schwarz M (2007) Internet advertising and the generalized second price auction: Selling billions of dollars worth of keywords. Am Econ Rev 97(1):242–259. https://doi.org/10.1257/aer.97.1.242

  • Feng Z, Zhu Y, Zhang Q, Zhu H, Yu J, Cao J, Ni LM (2014) Towards truthful mechanisms for mobile crowdsourcing with dynamic smartphones. In: 2014 IEEE 34th international conference on distributed computing systems. Madrid, pp 11–20. https://doi.org/10.1109/ICDCS.2014.10

  • Goel G, Nikzad A, Singla A (2014) Mechanism design for crowdsourcing markets with heterogeneous tasks. In: Proceedings of the Second AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2014(November), pp 2–4, Pittsburgh. Pennsylvania, USA

  • Gong X, Shroff N (2018) Incentivizing truthful data quality for quality-aware mobile data crowdsourcing. In: Proceedings of the eighteenth ACM international symposium on mobile ad hoc networking and computing (Mobihoc '18). Association for Computing Machinery, New York, NY, USA, pp 161–170. https://doi.org/10.1145/3209582.3209599

  • Gong X, Shroff NB (2019) Truthful mobile crowdsensing for strategic users with private data quality. IEEE/ACM Trans Netw 27(5):1959–1972

    Article  Google Scholar 

  • Hamrouni A, Ghazzai H, Frikha M, Massoud Y (2020) A spatial mobile crowdsourcing framework for event reporting. IEEE Trans Comput Soc Syst 7(2):477–491

    Article  Google Scholar 

  • Jaimes LG, Idalides VL, Labrador MA (2012) A location-based incentive mechanism for participatory sensing systems with budget constraints. In: IEEE International Conference on Pervasive Computing and Communications, Lugano, pp 103–108. https://doi.org/10.1109/PerCom.2012.6199855

  • Jain S, Gujar S, Bhat Zoeter O, Narahari Y (2018) A quality assuring, cost optimal multi-armed bandit mechanism for expertsourcing. Artif Intell 254:44–63. https://doi.org/10.1016/j.artint.2017.10.001

    Article  MathSciNet  MATH  Google Scholar 

  • Jiang L, Niu X, Xu J, Yang D, Xu L (2019) Incentivizing the workers for truth discovery in crowdsourcing with copiers. In: 2019 IEEE 39th international conference on distributed computing systems (ICDCS). Dallas, TX, USA, pp 1286–1295. https://doi.org/10.1109/ICDCS.2019.00129

  • Kobayashi M, Morita H, Matsubara M, Shimizu N, Morishima A (2018) An empirical study on short- and long-term effects of self-correction in crowdsourced microtasks. In: HCOMP, AAAI Press, pp 79–87

  • Kong X, Liu X, Jedari B, Li M, Wan L, Xia F (2019) Mobile crowdsourcing in smart cities: technologies, applications, and future challenges. IEEE Internet Things J 6(5):8095–8113

    Article  Google Scholar 

  • Lee JS, Hoh B (2010) Dynamic pricing incentive for participatory sensing. Elsevier J Pervasive Mob Comput 6(6):693–708. https://doi.org/10.1016/j.pmcj.2010.08.006

    Article  Google Scholar 

  • Li Y, Zhao Y, Ishak S, Song H, Wang N, Yao N (2018) An anonymous data reporting strategy with ensuring incentives for mobile crowd-sensing. J Ambient Intell Humaniz Comput 9(6):2093–2107. https://doi.org/10.1007/s12652-017-0529-x

    Article  Google Scholar 

  • Luo T, Das SK, Tan HP, Xia L (2016) Incentive mechanism design for crowdsourcing: an all-pay auction approach. ACM Trans Intell Syst Technol 7(3):351–3526. https://doi.org/10.1145/2837029

    Article  Google Scholar 

  • Mazlan N, Syed Ahmad SS, Kamalrudin M (2018) Volunteer selection based on crowdsourcing approach. J Ambient Intell Humaniz Comput 9(3):743–753. https://doi.org/10.1007/s12652-017-0490-8

    Article  Google Scholar 

  • Mitsopoulou E, Boutsis I, Kalogeraki V, Yu JY (2018) A cost-aware incentive mechanism in mobile crowdsourcing systems. In: 2018 19th IEEE international conference on mobile data management (MDM). Aalborg, pp 239–244. https://doi.org/10.1109/MDM.2018.00042

  • Nisan N, Roughgarden T, Tardos E, Vazirani VV (2007) Algorithmic game theory. Cambridge University Press, New York

    Book  MATH  Google Scholar 

  • Phuttharak J, Loke SW (2019) A review of mobile crowdsourcing architectures and challenges: toward crowd-empowered internet-of-things. IEEE Access 7:304–324

    Article  Google Scholar 

  • Reddy S, Estrin D, Hansen M, Srivastava M (2010) Examining micro-payments for participatory sensing data collections. In: Proceedings of the 12th ACM international conference on Ubiquitous computing (UbiComp '10). Association for Computing Machinery, New York, USA, pp 33–36. https://doi.org/10.1145/1864349.1864355

  • Restuccia F, Das SK, Payton J (2016) Incentive mechanisms for participatory sensing: survey and research challenges. ACM Trans Sen Netw 12(2):40. https://doi.org/10.1145/2888398

  • Roughgarden T (2016a) CS269I: incentives in computer science (Stanford University course). Lecture #17: Scoring Rules and Peer Prediction (Incentivizing Honest Forecasts and Feedback)

  • Roughgarden T (2016b) CS269I: incentives in computer science (Stanford University course). Lecture 3: Strategic Voting

  • Roughgarden T (2016c) CS269I: incentives in computer science (Stanford University course). Lecture #14: More on Auctions

  • Roughgarden T (2016d) Twenty lectures on algorithmic game theory. Stanford University, Stanford

    Book  MATH  Google Scholar 

  • Singer Y (2010) Budget feasible mechanisms. In: IEEE 51st Annual Symposium on Foundations of Computer Science, Las Vegas, NV, pp 765–774. https://doi.org/10.1109/FOCS.2010.78

  • Singh VK, Mukhopadhyay S, Xhafa F, Krause P (2020a) A quality-assuring, combinatorial auction based mechanism for IoT-based crowdsourcing. In: Advances in edge computing: massive parallel processing and applications. Advances in Parallel Computing, vol 35. IOS Press, pp 148–177. ISBN 978-1-64368-063-7

  • Singh VK, Mukhopadhyay S, Xhafa F, Sharma A (2020b) A budget feasible peer graded mechanism for iot-based crowdsourcing. J Ambient Intell Humaniz Comput 11(4):1531–1551. https://doi.org/10.1007/s12652-019-01219-z

    Article  Google Scholar 

  • Singla A, Krause A (2013) Truthful incentives in crowdsourcing tasks using regret minimization mechanisms. In: Proceedings of the 22nd international conference on world wide web. ACM (WWW ’13), Association for Computing Machinery, New York, USA, pp 1167–1178. https://doi.org/10.1145/2488388.2488490

  • Sukhwani V, Shaw R (2020) Operationalizing crowdsourcing through mobile applications for disaster management in India. Progr Disaster Sci 5:100052. https://doi.org/10.1016/j.pdisas.2019.100052

    Article  Google Scholar 

  • Tan W, Jiang Z (2019) A novel experience-based incentive mechanism for mobile crowdsensing system. In: Proceedings of the international conference on artificial intelligence, information processing and cloud computing (AIIPCC '19). Association for Computing Machinery, New York, USA. https://doi.org/10.1145/3371425.3371459

  • Wang H, Guo S, Cao J, Guo M (2018a) Melody: a long-term dynamic quality-aware incentive mechanism for crowdsourcing. IEEE Trans Parallel Distrib Syst 29(4):901–914. https://doi.org/10.1109/TPDS.2017.2775232

    Article  Google Scholar 

  • Wang Y, Cai Z, Tong X, Gao Y, Yin G (2018b) Truthful incentive mechanism with location privacy-preserving for mobile crowdsourcing systems. Comput Netw 135:32–43. https://doi.org/10.1016/j.comnet.2018.02.008

    Article  Google Scholar 

  • Wang Y, Gao Y, Li Y, Tong X (2020) A worker-selection incentive mechanism for optimizing platform-centric mobile crowdsourcing systems. Comput Netw. https://doi.org/10.1016/j.comnet.2020.107144

    Article  Google Scholar 

  • 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):4203–4214

    Article  Google Scholar 

  • Xu P, Srinivasan A, Sarpatwar KK, Wu K (2017) Budgeted online assignment in crowdsourcing markets: Theory and practice. In: Proceedings of the 16th conference on autonomous agents and multiagent systems, international foundation for autonomous agents and multiagent systems, Richland, SC, AAMAS’17, pp 1763–1765. http://dl.acm.org/citation.cfm?id=3091125.3091431

  • Yu R, Cao J, Liu R, Gao W, Wang X, Liang J (2019) Participant incentive mechanism toward quality-oriented sensing: understanding and application. ACM Trans Sens Netw 15(2):211–2125. https://doi.org/10.1145/3303703

    Article  Google Scholar 

  • Zhao D, Li XY, Ma H (2016) Budget-feasible online incentive mechanisms for crowdsourcing tasks truthfully. IEEE/ACM Trans Netw 24(2):647–661. https://doi.org/10.1109/TNET.2014.2379281

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, National Institute of Technology, Durgapur, West Bengal, India

    Jaya Mukhopadhyay & Anita Pal

  2. School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India

    Vikash Kumar Singh

  3. Department of Computer Science and Engineering, National Institute of Technology, Durgapur, West Bengal, India

    Sajal Mukhopadhyay & Abhishek Kumar

Authors
  1. Jaya Mukhopadhyay
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Vikash Kumar Singh
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Sajal Mukhopadhyay
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Anita Pal
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Abhishek Kumar
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Vikash Kumar Singh.

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

Mukhopadhyay, J., Singh, V.K., Mukhopadhyay, S. et al. A truthful budget feasible mechanism for IoT-based participatory sensing with incremental arrival of budget. J Ambient Intell Human Comput 13, 1107–1124 (2022). https://doi.org/10.1007/s12652-020-02844-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-02844-9

Keywords