Skip to main content
Log in

An incentive mechanism based on endowment effect facing social welfare in Crowdsensing

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

Abstract

Mobile CrowdSensing(MCS) is a new type of network which needs a large number of users to collect data to complete the sensing task and requires users to have a high level of participation. In the current research, the incentive purpose is mainly achieved by paying certain rewards to service providers. However, due to the demand for data quality and quantity, platforms often have high consumption. Most of the current studies assume that the evaluation of an item by a node is not affected by its ownership status. But behavioral economics points out that because of the existence of endowment effect, the evaluation of the value of an item by a node is greater when it is owned than when it is not owned, thereby affecting the value evaluation strategy of the node. Therefore, inspired by the phenomenon that enterprises use share dividends to stimulate endowment effect of employees in daily life and thus motivate employees to maintain loyalty, the paper constructs a mapping relationship between it and MCS and designs a Reverse Combinatorial Auction Based Endowment Effect (RCBEE) incentive mechanism. The paper designs endowment assets and assigns them to selected nodes according to the relationship between multiple coefficients of nodes, so as to introduce the endowment effect. The paper analyzes the change of the node on the evaluation value of endowment assets, changes the node income, and reconstructs the income matrix. On the one hand, the RCBEE mechanism continuously strengthens the node’s endowment intensity by setting the holding period; on the other hand, it introduces the contribution threshold to trigger the node’s endowment effect, so that users can maximize the incentive goal of social welfare by reducing the bid price or completing more tasks. Simulation experiments show that, compared with the traditional incentive mechanism, RCBEE reduces the payment cost and increases the social welfare.

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.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Capponi A, Fiandrino C, Kantarci B, Foschini L, Bouvry P (2019) A survey on mobile crowdsensing systems: challenges, solutions and opportunities. IEEE Commun Surveys Tutor 21(3):2419–2465

    Article  Google Scholar 

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

  3. Ji G, Yao Z, Zhang B, Li C (2020) A reverse auction-based incentive mechanism for mobile crowdsensing. IEEE Internet Things J 7(9):8238–8248

    Article  Google Scholar 

  4. Tong LA, Zhu YB, Huang LB (2019) Tgba: A two-phase group buying based auction mechanism for recruiting workers in mobile crowd sensing. Comput Netw 149:56–75

    Article  Google Scholar 

  5. Gendy ME, Al-Kabbany A, Badran EF (2019) Maximizing clearance rate of reputation-aware auctions in mobile crowdsensing,“in 2019 16th IEEE Annual Consumer Communications Networking Conference (CCNC), pp. 1–2

  6. Jin H, Su L, Chen D, Guo H, Nahrstedt K, Xu J (2019) Thanos: incentive mechanism with quality awareness for mobile crowd sensing. IEEE Trans Mob Comput 18(8):1951–1964

    Article  Google Scholar 

  7. Zhang X, Yang Z, Sun W, Liu Y, Tang S, Xing K, Mao X (2017) Incentives for mobile crowd sensing: a survey. IEEE Commun Surveys Tutor 18(1):54–67

    Article  Google Scholar 

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

  9. Huang H, Xin Y, Sun Y, Yang W (2017) A truthful double auction mechanism for crowdsensing systems with max-min fairness,” in 2017 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6

  10. Zhang X, Gao L, Cao B, Li Z, Wang M (2017) A double auction mechanism for mobile crowd sensing with data reuse,“in GLOBECOM 2017–2017 IEEE Global Commun Conference, pp 1–6

  11. Li T, Jung T, Qiu Z, Li H, Cao L, Wang Y (2020) Scalable privacy-preserving participant selection for mobile crowdsensing systems: participant grouping and secure group bidding. IEEE Trans Network Sci Eng 7(2):855–868

    Article  MathSciNet  Google Scholar 

  12. Nie J, Luo J, Xiong Z, Niyato D, Wang P (2019) A stackelberg game approach toward socially-aware incentive mechanisms for mobile crowdsensing. IEEE Trans Wirel Commun 18(1):724–738

    Article  Google Scholar 

  13. Wang Z, Zhou H, Zhao Y, Wu Y, Deng S, Huang H (2019) Stackelberg game based dynamic admission and scheduling in mobile crowdsensing. IEEE Access 7:101689–101703

    Article  Google Scholar 

  14. Hao L, Jia B, Liu J, Huang B, Li W (2020) Vcg-qcp: A reverse pricing mechanism based on vcg and quality all-pay for collaborative crowdsourcing,” in 2020 IEEE Wire-less Communications and Networking Conference (WCNC), pp. 1–6

  15. Li Q, Cao H, Wang S, Zhao X (2020) A reputation-based multi-user task selection incentive mechanism for crowdsensing. IEEE Access 99:1–1

    Google Scholar 

  16. Valerio L, Bruno R, Passarella A (2015) Cellular traffic offloading via opportunistic networking with reinforcement learning. Comput Commun 71:129–141

    Article  Google Scholar 

  17. Mao G, Zhang Z, Anderson BDO (2016) Cooperative content dissemination and offloading in heterogeneous mobile networks. IEEE Trans Veh Technol 65(8):6573–6587

    Article  Google Scholar 

  18. “Medwatcher.” http://www.fda.gov/MedicalDevices/Safety/portaProblem/ucm385880.htm

  19. Cheng Y, Li X, Li Z, Jiang S, Li Y, Jia J, Jiang X (2014) Aircloud: a cloud-based air-quality monitoring system for everyone,” in Proceedings of the 12th ACM Conference on embedded network sensor systems, pp. 251–265

  20. 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, vol. PP, no. 99, pp. 1–1

  21. Restuccia F, Ferraro P, Sanders TS, Silvestri S, Das SK, Lo Re G (2019) First: a framework for optimizing information quality in mobile crowdsensing systems. Acm Trans Sens Networks 15(1):5.1–5.35

    Google Scholar 

  22. He S, Shin D, Zhang J, Chen J (2017) Near-optimal allocation algorithms for location-dependent tasks in crowdsensing. IEEE Trans Veh Technol 66(4):3392–3405

    Article  Google Scholar 

  23. Gong W, Zhang B, Li C (2019) Location-based online task assignment and path planning for mobile crowdsensing. IEEE Trans Veh Technol 68(2):1772–1783

    Article  Google Scholar 

  24. Depoorter B , Hoeppner S (2014) Endowment Effect[J]. Encyclopedia of Law & Economics, pp. 1–9

  25. Horowitz JK, McConnell KE (2002) A review of wta/wtp studies. J Environ Econ Manag 44(3):426–447

    Article  Google Scholar 

  26. Cell TT, Hammitt JK (2014) A new meta-analysis on the wtp/wta disparity. J Environ Econ Manag 68(1):175–187

    Article  Google Scholar 

  27. Shafer GR (1984) Judgment under uncertainty: Heuristics and biases. J Am Statistic Assoc 79(385):223–224

    Article  Google Scholar 

  28. Thaler RH (1999) Mental accounting matters. J Behav Decis Mak 12(3):183–206

    Article  Google Scholar 

  29. Morewedge CK, Giblin CE (2015) Explanations of the endowment effect: an integra-tive review. Trends Cognitive Ences 19(6):339–348

    Article  Google Scholar 

  30. Ariely D, Huber J, Wertenbroch K (2018) When do losses loom larger than gains? J Market Res 42(2):134–138 2005

    Article  Google Scholar 

  31. Peters E, Slovic P, Gregory R (2003) The role of affect in the wta/wtp disparity. J Behav Decis Mak 16(4):309–330

    Article  Google Scholar 

  32. Zhang Y, Fishbach A (2005) The role of anticipated emotions in the endowment effect. J Consum Psychol 15(4):316–324

    Article  Google Scholar 

  33. Bolton GE, Ockenfels A (1998) Strategy and equity: an erc-analysis of the güth–van Damme game. J Math Psychol 42(2–3):215–226

    Article  Google Scholar 

  34. Murata A, Matsushita Y, Kubo S, Moriwaka M (2014) Analysis of human behavior by experimental game-theoretic approach-cooperative behavior, risk averse and seeking tendencies, and risk diversification,” in 2014 Proceedings of the SICE Annual Conference (SICE), pp. 947–954

  35. Kahneman D, Knetsch JL, Thaler RH (1990) Experimental tests of the endowment effect and the coase theorem. J Polit Econ 98(6):1325–1348

    Article  Google Scholar 

  36. Li D, Qiu L, Liu J, Xiao C (2018) Analysis of behavioral economics in crowdsensing: a loss aversion cooperation model. Sci Program 2018:1–18

    Google Scholar 

  37. Jaimes LG, Calderon JM (2018) Gaussian mixture model for crowdsensing incentivization,” in 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pp. 685–689

  38. Rui S and Yong Z (2017) Modelling and simulation for rumor propagation on complex networks with repast simulation platform,“in 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 1014–1018

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Deng Li.

Additional information

Publisher’s note

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

This article is part of the Topical Collection: Special Issue on Convergence of Edge Computing and Next Generation Networking

Guest Editors: Deze Zeng, Geyong Min, Qiang He, and Song Guo

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, J., Huang, S., Wang, W. et al. An incentive mechanism based on endowment effect facing social welfare in Crowdsensing. Peer-to-Peer Netw. Appl. 14, 3929–3945 (2021). https://doi.org/10.1007/s12083-021-01142-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-021-01142-1

Keywords

Navigation