Skip to main content

Advertisement

Springer Nature Link
Log in

Truthful double auction based incentive mechanism for participatory sensing systems

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

The sensors available in the smartphones are useful to explore a diverse range of city dynamics (e.g. noise pollution, road condition, traffic condition, etc.). The potential of the smartphone sensors coupled with their widespread availability help to emerge a new paradigm of sensing known as participatory sensing. It uses the power of smartphone equipped sensors to collect, store, and analyze data with high spatiotemporal granularity. In a participatory sensing based system, a task provider (also known as a crowdsourcer) may have a set of sensing tasks regarding different dynamics of a city. Here, adequate users’ participation is necessary to acquire a sufficient amount of data which is a key factor for the participatory sensing based systems to provide good service quality. The task providers appoint a set of task executors (smartphone users i.e. participants of crowdsensing tasks) to execute those sensing tasks. But, existing works on sensing task allocation suffer from lack of good incentive mechanisms that are attractive for the task executors. In order to address this issue, in this paper, a double auction based incentive mechanism called TATA (Truthful Double Auction for Task Allocation) is proposed for participatory sensing. TATA performs fair allocation of tasks which is leading to efficient incentive mechanism. In the case of TATA, the fair allocation of sensing tasks of the task providers to the task executers indicates that the proposed double auction mechanism is able to satisfy the truthfulness property in order to resist market manipulation (i.e., untruthful bidding and asking). Specifically, TATA achieves all the desirable properties like individual rationality, truthfulness (i.e. incentive compatibility), budget balance, etc. TATA is also computationally efficient and yields high system efficiency. Additionally, the performance of the proposed incentive mechanism is evaluated and compared with the existing mechanisms through extensive simulations based on the real-world data from Amazon Mechanical Turk. TATA yields high utility and satisfaction for the task providers and executors as compared to the existing mechanisms.

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
Algorithm 1
Algorithm 2
Algorithm 3
Algorithm 4
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data Availability

No datasets were generated or analysed during the current study.

References

  1. Middya AI, Dey P, Roy S (2022) Iot-based crowdsensing for smart environments. In: Internet of Things for Smart Environments. Springer, pp. 33–58

  2. Kanhere SS (2013) Participatory sensing: Crowdsourcing data from mobile smartphones in urban spaces. In: International Conference on Distributed Computing and Internet Technology. Springer, pp. 19–26

  3. Patra S, Middya AI, Roy S (2021) “PotSpot: Participatory sensing based monitoring system for pothole detection using deep learning,” Multimedia Tools and Applications, vol. 80, no. 16, pp. 25 171–25 195,. [Online]. Available: https://doi.org/10.1007/s11042-021-10874-4

  4. Shirzad-Ghaleroudkhani N, Mei Q, Gül M (2022) A crowdsensing-based platform for transportation infrastructure monitoring and management in smart cities. In: The Rise of Smart Cities. Elsevier, pp. 609–624. [Online]. Available: https://doi.org/10.1016/b978-0-12-817784-6.00005-9

  5. Kar D, Middya AI, Roy S (2019) An approach to detect travel patterns using smartphone sensing. In: 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). IEEE, [Online]. Available: https://doi.org/10.1109/ants47819.2019.9118073

  6. Nath P, Saha P, Middya AI, Roy S (2021) Long-term time-series pollution forecast using statistical and deep learning methods. Neural Computing and Applications, Apr. 2021. [Online]. Available: https://doi.org/10.1007/s00521-021-05901-2

  7. Das R, Middya AI, Roy S (2021) High granular and short term time series forecasting of pm2.5 air pollutant - a comparative review. Artif Intell Rev. [Online]. Available: https://doi.org/10.1007/s10462-021-09991-1

  8. Middya AI, Roy S (2021) Spatial interpolation techniques on participatory sensing data. ACM Transactions on Spatial Algorithms and Systems 7(3):1–32

  9. Chandra B, Middya AI, Roy S (2021) Spatio-temporal prediction of noise pollution using participatory sensing. In: Emerging Technologies in Data Mining and Information Security. Springer, pp. 597–607

  10. Liu Z, Jiang S, Zhou P, Li M (2017) A participatory urban traffic monitoring system: The power of bus riders. IEEE Transactions on Intelligent Transportation Systems 18(10):2851–2864

    Article  Google Scholar 

  11. Yan H, Hua Q, Zhang D, Wan J, Rho S, Song H (2017) Cloud-assisted mobile crowd sensing for traffic congestion control. Mobile Networks and Applications 22(6):1212–1218

    Article  Google Scholar 

  12. Takahashi J, Kobana Y, Isoyama N, Tobe Y, Lopez G (2018) Ykob: Participatory sensing-based road condition monitoring using smartphones worn by cyclist. Electronics and Communications in Japan 101(4):3–14

    Article  Google Scholar 

  13. Nunes DE, Mota VF (2019) A participatory sensing framework to classify road surface quality. Journal of Internet Services and Applications 10(1):13

    Article  Google Scholar 

  14. Hu K, Sivaraman V, Luxan BG, Rahman A (2015) Design and evaluation of a metropolitan air pollution sensing system. IEEE Sensors J 16(5):1448–1459

    Article  Google Scholar 

  15. Middya AI, Roy S, Dutta J, Das R (2020) Jusense: A unified framework for participatory-based urban sensing system. Mobile Networks and Applications, pp. 1–26

  16. Nie J, Luo J, Xiong Z, Niyato D (2018) Wang P (2018) A stackelberg game approach toward socially-aware incentive mechanisms for mobile crowdsensing. IEEE Trans Wirel Commun 18(1):724–738

    Article  Google Scholar 

  17. Wang Z, Li J, Hu J, Ren J, Wang Q, Li Z, Li Y (2021) Towards privacy-driven truthful incentives for mobile crowdsensing under untrusted platform. IEEE Trans Mob Comput

  18. Yang D, Xue G, Fang X, Tang J (2015) Incentive mechanisms for crowdsensing: Crowdsourcing with smartphones. IEEE/ACM Trans Networking 24(3):1732–1744

    Article  Google Scholar 

  19. Jiang N, Xu D, Zhou J, Yan H, Wan T, Zheng J (2020) Toward optimal participant decisions with voting-based incentive model for crowd sensing. Inf Sci 512:1–17

    Article  Google Scholar 

  20. Cai H, Zhu Y, Feng Z, Zhu H, Yu J, Cao J (2018) Truthful incentive mechanisms for mobile crowd sensing with dynamic smartphones. Comput Netw 141:1–16

    Article  Google Scholar 

  21. Jiang L-Y, He F, Wang Y, Sun L-J (2017) Huang H-P (2017). Quality-aware incentive mechanism for mobile crowd sensing, J Sensors

    Google Scholar 

  22. Jin W, Li M, Guoy L, Yang L (2018) Dpda: A differentially private double auction scheme for mobile crowd sensing. In: 2018 IEEE Conference on Communications and Network Security (CNS). IEEE, pp. 1–9

  23. Chen S, x Liu M, Chen X (2016) A truthful double auction for two-sided heterogeneous mobile crowdsensing markets. Comput Commun 81:31–42

  24. Deng X, Goldberg P, Tang B, Zhang J (2014) Revenue maximization in a bayesian double auction market. Theor Comput Sci 539:1–12

    Article  MathSciNet  Google Scholar 

  25. Rana R, Chou CT, Bulusu N, Kanhere S, Hu W (2015) Ear-phone: A context-aware noise mapping using smart phones. Pervasive and Mobile Computing 17:1–22

    Article  Google Scholar 

  26. Maisonneuve N, Stevens M, Niessen ME, Steels L (2009) Noisetube: Measuring and mapping noise pollution with mobile phones. In: Information technologies in environmental engineering. Springer, pp. 215–228

  27. Dutta J, Pramanick P, Roy S (2017) Noisesense: Crowdsourced context aware sensing for real time noise pollution monitoring of the city. In: 2017 IEEE ANTS. IEEE, 2017, pp. 1–6

  28. Bales E, Nikzad N, Quick N, Ziftci C, Patrick K, Griswold WG (2019) Personal pollution monitoring: mobile real-time air quality in daily life. Pers Ubiquit Comput 23(2):309–328

    Article  Google Scholar 

  29. Dutta J, Chowdhury C, Roy S, Middya AI, Gazi F (2017) Towards smart city: sensing air quality in city based on opportunistic crowd-sensing. In: Proceedings of the 18th international conference on distributed computing and networking pp. 1–6

  30. Dey MR, Satapathy U, Bhanse P, Mohanta BK, Jena D (2019) Magtrack: detecting road surface condition using smartphone sensors and machine learning. In: IEEE TENCON. IEEE, pp. 2485–2489

  31. Yi C-W, Chuang Y-T, Nian C-S (2015) Toward crowdsourcing-based road pavement monitoring by mobile sensing technologies. IEEE Trans Intell Transp Syst 16(4):1905–1917

    Article  Google Scholar 

  32. Allouch A, Koubâa A, Abbes T, Ammar A (2017) Roadsense: Smartphone application to estimate road conditions using accelerometer and gyroscope. IEEE Sensors J 17(13):4231–4238

    Article  Google Scholar 

  33. Bose B, Dutta J, Ghosh S, Pramanick P, Roy S (2018) D &rsense: Detection of driving patterns and road anomalies. In: 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU). IEEE, pp. 1–7

  34. Vij D, Aggarwal N (2018) Smartphone based traffic state detection using acoustic analysis and crowdsourcing. Appl Acoust 138:80–91

    Article  Google Scholar 

  35. Alam MY, Imam S, Anurag H, Saha S, Nandi S, Saha M (2018) Lisense: Monitoring city street lighting during night using smartphone sensors. In: 2018 IEEE ICDMW. IEEE, pp. 596–603

  36. Sun J, Wu D (2024) Contextual features based budget-limited online pricing for heterogeneous sensing tasks. IEEE Internet of Things Journal, p. 1-1. [Online]. Available: https://doi.org/10.1109/JIOT.2023.3348506

  37. Suhag D, Jha V (2023) A comprehensive survey on mobile crowdsensing systems. J Syst Archit 102952

  38. Sun J, Wu D (2023) Ellipsoid-pricing-based context-feature mechanisms for noisy sensing tasks. IEEE Internet of Things Journal, vol. 10, no. 22, p. 19972-19980. [Online]. Available: https://doi.org/10.1109/JIOT.2023.3282193

  39. Zhang X, Xue G, Yu R, Yang D, Tang J (2015) Truthful incentive mechanisms for crowdsourcing.In: 2015 IEEE Conference on Computer Communications (INFOCOM). IEEE, Apr. 2015. [Online]. Available: https://doi.org/10.1109/infocom.2015.7218676

  40. Li D, Wang S, Liu J, Liu H, Wen S (2019) Crowdsensing from the perspective of behavioral economics: An incentive mechanism based on mental accounting. IEEE Internet of Things Journal 6(5):9123–9139. https://doi.org/10.1109/jiot.2019.2928035

    Article  Google Scholar 

  41. Wang L, Yu Z, Zhang D, Guo B, Liu CH (2019) Heterogeneous multi-task assignment in mobile crowdsensing using spatiotemporal correlation. IEEE Transactions on Mobile Computing 18(1):84–97. https://doi.org/10.1109/tmc.2018.2827375

    Article  Google Scholar 

  42. Jin H, Su L, Chen D, Nahrstedt K, Xu J (2015) Quality of information aware incentive mechanisms for mobile crowd sensing systems. In: Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing. [Online]. Available: https://doi.org/10.1145/2746285.2746310

  43. Wen Y, Shi J, Zhang Q, Tian X, Huang Z, Yu H, Cheng Y, Shen X (2014) Quality-driven auction-based incentive mechanism for mobile crowd sensing. IEEE Trans Veh Technol 64(9):4203–4214

    Article  Google Scholar 

  44. Zhang G, Hou F, Gao L, Yang G, Cai LX (2022) Nondeterministic-mobility-based incentive mechanism for efficient data collection in crowdsensing. IEEE Internet of Things Journal 9(23):23 626–23 638. [Online]. Available: https://doi.org/10.1109/jiot.2022.3190565

  45. Esmaeilyfard R, Moghisi M (2022) An incentive mechanism design for multitask and multipublisher mobile crowdsensing environment. The Journal of Supercomputing. [Online]. Available: https://doi.org/10.1007/s11227-022-04852-2

  46. Li Y, Li F, Zhu L, Chen H, Li T, Wang Y (2022) Fair incentive mechanism with imperfect quality in privacy-preserving crowdsensing. IEEE Internet of Things Journal 9(19):19 188–19 200. https://doi.org/10.1109/jiot.2022.3164664

  47. Ni T, Chen Z, Xu G, Zhang S, Zhong H (2021) Differentially private double auction with reliability-aware in mobile crowd sensing. Ad Hoc Networks 114:102450. https://doi.org/10.1016/j.adhoc.2021.102450

    Article  Google Scholar 

  48. Chen X, Zhang L, Pang Y, Lin B, Fang Y (2022) Timeliness-aware incentive mechanism for vehicular crowdsourcing in smart cities. IEEE Transactions on Mobile Computing 21(9):3373–3387. https://doi.org/10.1109/tmc.2021.3052963

    Article  Google Scholar 

  49. Chen S, Liu M, Chen X (2016) A truthful double auction for two-sided heterogeneous mobile crowdsensing markets. Comput Commun 81:31–42. https://doi.org/10.1016/j.comcom.2015.11.010

    Article  Google Scholar 

  50. Chen C, Wang Y (2013) Sparc: Strategy-proof double auction for mobile participatory sensing. In: 2013 International Conference on Cloud Computing and Big Data. IEEE, pp. 133–140

  51. Zhang Y, Zhang X (2021) Price learning-based incentive mechanism for mobile crowd sensing. ACM Transactions on Sensor Networks 17(2):1–24. https://doi.org/10.1145/3447622

    Article  Google Scholar 

  52. Jin H, Su L, Nahrstedt K (2017) Centurion: Incentivizing multi-requester mobile crowd sensing. In: IEEE INFOCOM 2017-IEEE Conference on Computer Communications. IEEE, pp. 1–9

  53. Peng D, x Wu F, Chen G (2018) Data quality guided incentive mechanism design for crowdsensing. IEEE Transactions on Mobile Computing 17(2):307–319. https://doi.org/10.1109/tmc.2017.2714668

  54. Zhan Y, Liu CH, Zhao Y, Zhang J, Tang J (2020) Free market of multi-leader multi-follower mobile crowdsensing: An incentive mechanism design by deep reinforcement learning. IEEE Transactions on Mobile Computing 19(10):2316–2329. https://doi.org/10.1109/tmc.2019.2927314

    Article  Google Scholar 

  55. Zhan Y, Xia Y, Zhang J, Li T, Wang Y (2020) An incentive mechanism design for mobile crowdsensing with demand uncertainties. Inf Sci 528:1–16. https://doi.org/10.1016/j.ins.2020.03.109

    Article  MathSciNet  Google Scholar 

  56. Jia B, Gong H, Zong Z, Zhou T, Baker T, Al-Shamma’a A, Jia Y (2022) An incentive mechanism in expert-decision-based crowdsensing networks. Appl Soft Comput 122:108834

    Article  Google Scholar 

  57. Song Z, Ngai E, Ma J, Gong X, Liu Y, Wang W (2014) Incentive mechanism for participatory sensing under budget constraints. In: 2014 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, pp. 3361–3366

  58. Chou CT, Bulusu N, Kanhere S et al (2007) Sensing data market. Proceedings of Poster Papers p. 13

  59. Reddy S, Estrin D, Srivastava M (2010) Recruitment framework for participatory sensing data collections. In: International Conference on Pervasive Computing. Springer, pp. 138–155

  60. Lee J-S, Hoh B (2010) Sell your experiences: a market mechanism based incentive for participatory sensing. In: 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, pp. 60–68

  61. Wang E, Wang H, Yang Y, Liu W (2022) Truthful incentive mechanism for budget-constrained online user selection in mobile crowdsensing. IEEE Transactions on Mobile Computing 21(12):4642–4655. https://doi.org/10.1109/TMC.2021.3083920

    Article  Google Scholar 

  62. Jin H, Su L, Xiao H, Nahrstedt K (2018) Incentive mechanism for privacy-aware data aggregation in mobile crowd sensing systems. IEEE/ACM Transactions on Networking 26(5):2019–2032. https://doi.org/10.1109/TNET.2018.2840098

    Article  Google Scholar 

  63. Xu Y, Xiao M, Wu J, Zhang S, Gao G (2023) Incentive mechanism for spatial crowdsourcing with unknown social-aware workers: A three-stage stackelberg game approach. IEEE Transactions on Mobile Computing 22(8):4698–4713. https://doi.org/10.1109/TMC.2022.3157687

    Article  Google Scholar 

  64. Liu Y, Li H, Zhao G, Duan J (2018) Reverse auction based incentive mechanism for location-aware sensing in mobile crowd sensing. In: 2018 IEEE International Conference on Communications (ICC). IEEE, pp. 1–6

  65. Li Q, Cao G (2013) Providing privacy-aware incentives for mobile sensing. In 2013 IEEE international conference on pervasive computing and communications (PerCom). IEEE, pp. 76–84

  66. Sun J, Wu D (2022) Online truthful incentives for heterogeneous and \(k\)-submodular crowdsensing. IEEE Systems Journal 1–10:2022. https://doi.org/10.1109/jsyst.2022.3217803

    Article  Google Scholar 

  67. Zhou R, Zhang R, Wang Y, Tan H, He K (2022) Online incentive mechanism for task offloading with privacy-preserving in UAV-assisted mobile edge computing. In: Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing. ACM. [Online]. Available: https://doi.org/10.1145/3492866.3549715

  68. Luo T, Tan H-P, Xia L (2014) Profit-maximizing incentive for participatory sensing. In: IEEE INFOCOM 2014-IEEE Conference on Computer Communications. IEEE, pp. 127–135

  69. Koutsopoulos I (2013) Optimal incentive-driven design of participatory sensing systems. In: 2013 Proceedings IEEE INFOCOM. IEEE, pp. 1402–1410

  70. Zhao B, Tang S, Liu X, Zhang X (2020) Pace: Privacy-preserving and quality-aware incentive mechanism for mobile crowdsensing. IEEE Transactions on Mobile Computing 20(5):1924–1939

    Article  Google Scholar 

  71. Zhang Y, Zhang X (2023) Incentive mechanism with task bundling for mobile crowd sensing. ACM Transactions on Sensor Networks. [Online]. Available: https://doi.org/10.1145/3581788

  72. Zhang J, Li X,x Shi Z, Zhu C (2022) A reputation-based and privacy-preserving incentive scheme for mobile crowd sensing: a deep reinforcement learning approach. Wireless Networks. [Online]. Available: https://doi.org/10.1007/s11276-022-03111-9

  73. Chu LY, Shen Z-JM (2008) Truthful double auction mechanisms. Oper Res 56(1):102–120

    Article  MathSciNet  Google Scholar 

  74. Parsons S, Rodriguez-Aguilar JA, Klein M (2011) Auctions and bidding: A guide for computer scientists. ACM Computing Surveys (CSUR) 43(2):1–59

    Article  Google Scholar 

  75. Zhang X, Yang Z, Sun W, Liu Y, Tang S, Xing K, Mao X (2015) Incentives for mobile crowd sensing: A survey. IEEE Communications Surveys & Tutorials 18(1):54–67

    Article  Google Scholar 

  76. Liu S, Yu Y, Guo L, Yeoh PL, Ni Q, Vucetic B, Li Y (2022) Truthful online double auctions for mobile crowdsourcing: An on-demand service strategy. IEEE Internet of Things Journal 9(17):16,096–16,112

  77. Amazon mechanical turk (mturk). https://www.mturk.com/. Accessed: 2020-05-06

  78. Zhang Y, Zhang X (2023) Incentive mechanism with task bundling for mobile crowd sensing. ACM Transactions on Sensor Networks 19(3):1–23

  79. Liu Z, Li K, Zhou X, Zhu N, Li K (2020) Incentive mechanisms for crowdsensing: Motivating users to preprocess data for the crowdsourcer. ACM Transactions on Sensor Networks (TOSN) 16(4):1–24

Download references

Funding

This research did not receive any specific grant.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Asif Iqbal Middya & Sarbani Roy

Authors
  1. Asif Iqbal Middya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sarbani Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.I.M implemented the algorithms and prepared all figures. S.R supervised the research work. A.I.M and S.R wrote the main manuscript. Both the authors reviewed the manuscript.

Corresponding author

Correspondence to Sarbani Roy.

Ethics declarations

Ethics Approval

Not applicable.

Conflict of Interest

The authors declare that they have no conflict of interest.

Consent to publish

All the authors agreed to publish the manuscript.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

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

This article belongs to the Topical Collection: 1- Track on Networking and Applications

Guest Editor: Vojislav B. Misic

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

Middya, A.I., Roy, S. Truthful double auction based incentive mechanism for participatory sensing systems. Peer-to-Peer Netw. Appl. 17, 2137–2166 (2024). https://doi.org/10.1007/s12083-024-01681-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-024-01681-3

Keywords