Abstract
Mobile Crowdsensing (MCS) has become a new paradigm of collecting and merging a large number of sensory data by using rich sensor-equipped mobile terminals. Existing studies focusing on multi-task allocation with the objective of maximizing the social utility may result in the problem of unbalanced allocation due to the limited resources of workers, which may damage the social fairness, and requesters who suffer unfairness will choose to leave the system, thereby destroying the long-term stability of the system. To address this issue, we introduce max-min fairness into the design of a novel fairness-aware incentive mechanism for MCS. We first formalize the max-min fairness-aware multi-task allocation problem by using the sensing time threshold of tasks as a constraint. By modeling the max-min fairness-aware multi-task allocation problem as a Stackelberg game consisting of multi-leader and multi-follower, we next compute the unique Stackelberg equilibrium at which the utilities of both requesters and workers are maximized. Then, we design a greedy algorithm to achieve max-min fairness while meeting the sensing time threshold required by the task. Finally, simulation results further demonstrate the impact of intrinsic parameters on social utility and price of fairness, as well as the feasibility and effectiveness of our proposed max-min fairness-aware incentive mechanism.
Keywords
This work was supported in part by the National Natural Science Foundation of China (No. 62072411, 61872323, 61751303), in part by the Social Development Project of Zhejiang Provincial Public Technology Research (No. 2017C33054), in part by the Natural Science Foundation of Guangdong Province (No. 2018A030313061), and in part by the Guangdong Science and Technology Plan (no. 2017B010124001, 201902020016, 2019B010139001).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zhang, X.: Incentives for mobile crowd sensing: a survey. IEEE Commun. Surv. Tutor. 18(1), 1 (2015)
Liu, Y.: Data-oriented mobile crowdsensing: a comprehensive survey. IEEE Commun. Surv. Tutor. 21(3), 2849–2885 (2019)
Guo, B., Yu, Z., Zhou, X., et al.: From participatory sensing to mobile crowd sensing. In: International Conference on Pervasive Computing, pp. 593–598 (2014)
Wu, F., Luo, T.: WiFiScout: a crowdsensing WiFi advisory system with gamification-based incentive. In: Mobile Ad Hoc and Sensor Systems, pp. 533–534 (2014)
Liu, L., Liu, W., Zheng, Y., et al.: Third-eye: a mobilephone-enabled crowdsensing system for air quality monitoring. Mobile Wearable Ubiquit. Technol. 2(1), 1–26 (2018)
Kim, S., Robson, C., Zimmerman, T., et al.: Creek watch: pairing usefulness and usability for successful citizen science. In: Human Factors Computing Systems, pp. 2125–2134 (2011)
Thiagarajan, A., Ravindranath, L., Lacurts, K., et al.: VTrack: accurate, energy-aware road traffic delay estimation using mobile phones. In: International Conference on Embedded Networked Sensor Systems, pp. 85–98 (2009)
Lau, J.K., Tham, C., Luo, T., et al.: Participatory cyber physical system in public transport application. In: Utility and Cloud Computing, pp. 355–360 (2011)
Gao, C., Kong, F., Tan, J., et al.: HealthAware: tackling obesity with health aware smart phone systems. In: Robotics and Biomimetics, pp. 1549–1554 (2009)
Capponi, A., Fiandrino, C., Kantarci, B., et al.: A survey on mobile crowdsensing systems: challenges, solutions, and opportunities. IEEE Commun. Surv. Tutor. 21(3), 2419–2465 (2019)
Huang, H., Xin, Y., Sun, Y., et al.: A truthful double auction mechanism for crowdsensing systems with max-min fairness. In: Wireless Communications and Networking Conference, pp. 1–6 (2017)
Li, D., Yang, L., Liu, J., et al.: Considering decoy effect and fairness preference: an incentive mechanism for crowdsensing. IEEE Internet Things J. 6(5), 8835–8852 (2019)
Zhu, X., An, J., Yang, M., et al.: A fair incentive mechanism for crowdsourcing in crowd sensing. IEEE Internet Things J. 3(6), 1364–1372 (2016)
Tao, X., Song, W.: Location-dependent task allocation for mobile crowdsensing with clustering effect. IEEE Internet Things J. 6(1), 1029–1045 (2019)
Sooksatra, K., Li, R., Li, Y., Guan, X., Li, W.: Fairness-aware auction mechanism for sustainable mobile crowdsensing. In: Biagioni, E.S., Zheng, Y., Cheng, S. (eds.) WASA 2019. LNCS, vol. 11604, pp. 310–321. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23597-0_25
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, S., Jiang, W., Duan, J., Huang, Z., Lu, J. (2020). Max-Min Fairness Multi-task Allocation in Mobile Crowdsensing. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12487. Springer, Cham. https://doi.org/10.1007/978-3-030-62460-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-62460-6_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62459-0
Online ISBN: 978-3-030-62460-6
eBook Packages: Computer ScienceComputer Science (R0)