Skip to main content
Log in

A truthful mechanism for time-bound tasks in IoT-based crowdsourcing with zero budget

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Crowdsourcing is a process of engaging a ‘crowd’ or a group of common people for accomplishing the tasks. In this work, the time-bound tasks allocation problem in IoT-based crowdsourcing is investigated in strategic setting. The proposed model consists of multiple task providers (or task requesters) and several IoT devices (or task executors), and each of the task providers carries a task that have start time and completion time. Each of the participating IoT devices provide a preference ordering (order of their interest for the tasks) over a subset of tasks. Given the time bound tasks and ranking (or preference ordering) of the task executors, the objectives are: (1) to assign the tasks to different slots so that they are non-conflicting in nature, and (2) to allocate at most one task to each of the task executors from their respective preference ordering. To achieve the above objectives, a truthful mechanism is proposed namely Truthful Mechanism for Time-bound Tasks in IoT-based Crowdsourcing (TMTTC). Through theoretical analysis, it is proved that TMTTC satisfies the properties such as computational efficiency, truthfulness, Pareto optimality, and The Core. Through simulation, it is shown that TMTTC performs better than benchmark mechanism on the ground of truthfulness.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data Availability

The data sets generated and analysed during the current study are available from the corresponding author (V. K. Singh) on reasonable request.

Code Availability

The code will be publicly available upon acceptance.

References

  1. Chenxi Qiu, Anna Squicciarini, Dev Rishi Khare, Barbara Carminati, and James Caverlee. Crowdeval: A cost-efficient strategy to evaluate crowdsourced worker’s reliability. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS ’18, page 1486-1494, Richland, SC, 2018. International Foundation for Autonomous Agents and Multiagent Systems

  2. 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

    Article  Google Scholar 

  3. Z. Feng, Y. Zhu, Q. Zhang, L. M. Ni, and A. V. Vasilakos. TRAC: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing. In IEEE INFOCOM 2014 - IEEE Conference on Computer Communications, pages 1231–1239, Toronto, ON, Canada, April 2014

  4. Florian Daniel, Pavel Kucherbaev, Cinzia Cappiello, Boualem Benatallah, and Mohammad Allahbakhsh. Quality control in crowdsourcing: A survey of quality attributes, assessment techniques, and assurance actions. ACM Computing Surveys, 51(1):7:1–7:40, January 2018

  5. Gale D, Shapley LS (1962) College admissions and the stability of marriage. American Mathematical Monthly 69:9–15

    Article  MathSciNet  Google Scholar 

  6. L. Gao, F. Hou, and J. Huang. Providing long-term participation incentive in participatory sensing. In 2015 IEEE Conference on Computer Communications (INFOCOM), pages 2803–2811, Kowloon, Hong Kong, April 2015

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

  8. https://en.wikipedia.org/wiki/crowdsourcing, May 2018

  9. Jaya Mukhopadhyay, Vikash Kumar Singh, Anita Pal, and Abhishek Kumar. A truthful budget feasible mechanism for iot-based participatory sensing with incremental arrival of budget. Journal of Ambient Intelligence and Humanized Computing, Feb 2021

  10. Jurairat Phuttharak and Seng Wai Loke (2019) A review of mobile crowdsourcing architectures and challenges: Toward crowd-empowered internet-of-things. IEEE Access 7:304–324

    Article  Google Scholar 

  11. B. Klaus, D. F. Manlove, and F. Rossi. Matching under preferences. In Felix Brandt, Vincent Conitzer, Ulle Endriss, Jérôme Lang, and Ariel D. Procaccia, editors, Handbook of Computational Social Choice, pages 333–355. Cambridge University Press, Cambridge, New York, April 2016

  12. Kleinberg Jon, Tardos Eva (2005) Algorithm Design. Addison-Wesley Longman Publishing Co., Inc, Boston, MA, USA

    Google Scholar 

  13. 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 of Things Journal 6(5):8095–8113

    Article  Google Scholar 

  14. Lefeng Zhang, Ping Xiong, Wei Ren, and Tianqing Zhu. A differentially private method for crowdsourcing data submission. Concurrency and Computation: Practice and Experience, 31(19):e5100, 2019. e5100 cpe.5100

  15. Li Yang, Zhao Yunlong, Ishak Serrat, Song Hongtao, Wang Nianbin, Yao Nianmin (2018) An anonymous data reporting strategy with ensuring incentives for mobile crowd-sensing. Journal of Ambient Intelligence and Humanized Computing 9(6):2093–2107

    Article  Google Scholar 

  16. Li Jiaye, Hao Yu, Zhang Leyuan, Wen Guoqiu (2019) Double weighted k-nearest voting for label aggregation in crowdsourcing learning. Multimedia Tools and Applications 78:33357–33374

    Article  Google Scholar 

  17. T. Luo, S. K. Das, H. P. Tan, and L. Xia. Incentive mechanism design for crowdsourcing: An all-pay auction approach. ACM Transactions on Intelligent Systems and Technology, 7(3):35:1–35:26, February 2016

  18. Masaki Kobayashi, Hiromi Morita, Masaki Matsubara, Nobuyuki Shimizu, and Atsuyuki Morishima. An empirical study on short- and long-term effects of self-correction in crowdsourced microtasks. In HCOMP, pages 79–87. AAAI Press, 2018

  19. Mazlan Nurulhasanah, Ahmad Sharifah Sakinah Syed, Kamalrudin Massila (2018) Volunteer selection based on crowdsourcing approach. Journal of Ambient Intelligence and Humanized Computing 9(3):743–753

    Article  Google Scholar 

  20. Munro Robert (2013) Crowdsourcing and the crisis-affected community. Information Retrieval 16(2):210–266

    Article  MathSciNet  Google Scholar 

  21. T. Roughgarden. CS269I: Incentives in computer science (Stanford University course), 2016. Lecture 3: Strategic Voting

  22. T. Roughgarden. CS269I: Incentives in computer science, (Stanford University Course), Lecture #1: The draw and college admissions, September 2016

  23. T. Roughgarden. CS364A: Algorithmic game theory (Stanford University course), lecture #9: Beyond quasi-linearity, October 2013

  24. Ruiyun Yu, Jiannong Cao, Rui Liu, Wenyu Gao, Xingwei Wang, and Junbin Liang. Participant incentive mechanism toward quality-oriented sensing: Understanding and application. ACM Trans. Sen. Netw., 15(2):21:1–21:25, February 2019

  25. Samarjit Roy, Dhiman Sarkar, and Debashis De. Dewmusic: crowdsourcing-based internet of music things in dew computing paradigm. Journal of Ambient Intelligence and Humanized Computing, page 2103-2119, Feb 2021

  26. J. Schummer and R. V. Vohra. Mechanism design without money. In E. Tardos N. Nisan, T. Roughgarden and V. V. Vazirani, editors, Algorithmic Game Theory, pages 209–242. Cambridge University Press, New York, 2007

  27. Shahzad Sarwar Bhatti, Xiaofeng Gao, and Guihai Chen. General framework, opportunities and challenges for crowdsourcing techniques: A comprehensive survey. Journal of Systems and Software, 167:110611, 2020

  28. Shapley L, Scarf H (1974) On cores and indivisibility. Journal of Mathematical Economics 1:23–37

    Article  MathSciNet  Google Scholar 

  29. Y. Singer. Budget feasible mechanisms. In Proceedings of the 2010 IEEE \(51^{st}\) Annual Symposium on Foundations of Computer Science, FOCS ’10, pages 765–774, Washington, DC, USA, 2010. IEEE Computer Society

  30. V. K. Singh, S. Mukhopadhyay, F. Xhafa, and P. Krause. A quality-assuring, combinatorial auction based mechanism for IoT-based crowdsourcing. In Advances in Edge Computing: Massive Parallel Processing and Applications, volume 35, pages 148–177. IOS Press, 2020

  31. Slivkins A, Vaughan JW (2014) Online decision making in crowdsourcing markets: Theoretical challenges. SIGecom Exchanges 12(2):4–23

    Article  Google Scholar 

  32. Syed Thouheed Ahmed, Vinoth Kumar, and JungYoon Kim. Aitel: ehealth augmented intelligence based telemedicine resource recommendation framework for IoT devices in smart cities. IEEE Internet of Things Journal, pages 1–1, 2023

  33. Venkatraman S, Surendiran B (2020) Adaptive hybrid intrusion detection system for crowd sourced multimedia internet of things systems. Multimedia Tools and Applications 79:3993–4010

    Article  Google Scholar 

  34. Vikash Kumar Singh, Sajal Mukhopadhyay, Fatos Xhafa, and Aniruddh Sharma. A budget feasible peer graded mechanism for iot-based crowdsourcing. Journal of Ambient Intelligence and Humanized Computing, 11(4):1531–1551, Jan 2020

  35. Wang Xiumin, Tushar Wayes, Yuen Chau, Zhang Xinglin (2020) Promoting users’ participation in mobile crowdsourcing: A distributed truthful incentive mechanism (dtim) approach. IEEE Transactions on Vehicular Technology 69(5):5570–5582

  36. Wen Yutian, Shi Jinyu, Zhang Qi, Tian Xiaohua, Huang Zhengyong, Hui Yu, Cheng Yu, Shen Xuemin (2015) Quality-driven auction-based incentive mechanism for mobile crowd sensing. IEEE Transactions on Vehicular Technology 64(9):4203–4214

    Article  Google Scholar 

  37. Xiaolong Xu, Qing Cai, Guoming Zhang, Jie Zhang, Wei Tian, Xiaorui Zhang, and Alex X. Liu. An incentive mechanism for crowdsourcing markets with social welfare maximization in cloud-edge computing. Concurrency and Computation: Practice and Experience, 33(7):e4961, 2021. e4961 cpe.4961

  38. Xiaowen Gong and Ness Shroff. 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, pages 161–170, New York, NY, USA, 2018. ACM

  39. P. Xu, A. Srinivasan, K. K. Sarpatwar, and K. Wu. Budgeted online assignment in crowdsourcing markets: Theory and practice. In Proceedings of the \(16^{th}\) Conference on Autonomous Agents and MultiAgent Systems, AAMAS’17, pages 1763–1765, Richland, SC, 2017. International Foundation for Autonomous Agents and Multiagent Systems

  40. Ying Hu, Yingjie Wang, Yingshu Li, and Xiangrong Tong. An incentive mechanism in mobile crowdsourcing based on multi-attribute reverse auctions. Sensors, 18(10), 2018

Download references

Acknowledgements

We would like to thanks the faculty members of the School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati for their valuable suggestions during the course of this work.

Funding

No funds and grants was received.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vikash Kumar Singh.

Ethics declarations

Conflicts of interest

There is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar Singh, V., Mishra, S. A truthful mechanism for time-bound tasks in IoT-based crowdsourcing with zero budget. Multimed Tools Appl 83, 9873–9892 (2024). https://doi.org/10.1007/s11042-023-16015-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16015-3

Keywords

Navigation