Abstract
Mobile crowdsourcing (MCS) is gaining real attention in recent years as it has found widespread applications such as traffic monitoring, pollution control surveillance, locating endangered species, and many others. This paradigm of research is showing an interesting power of smart devices that are held by intelligent agents (such as human beings). In MCS, the tasks which are outsourced are executed by the task executors (intelligent agents carrying smart devices). In this paper, how overlapping tasks (with a deadline) can be disseminated in slots and leveraged as evenly as possible to the stakeholders (task executors or sellers) is addressed through a scalable scheduling (interval partitioning) and economic mechanism (double auction). It is proved that our mechanism is truthful and also shown via simulation that our proposed mechanism will perform better when the agents are manipulative in nature.
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Notes
- 1.
In literature, mobile crowdsourcing is also termed as participatory sensing.
References
Hasenfratz, D., Saukh, O., Sturzenegger, S., Thiele, L.: Participatory air pollution monitoring using smartphones. In: Mobile Sensing: From Smartphones and Wearables to Big Data, Beijing, China, April 2012. ACM (2012)
J, W., et al.: Fine-grained multitask allocation for participatory sensing with a shared budget. IEEE Internet Things J. 3(6), 1395–1405 (2016)
Phuttharak, J., Loke, S.W.: A review of mobile crowdsourcing architectures and challenges: toward crowd-empowered Internet-of-Things. IEEE Access 7, 304–324 (2019)
Yu, R., Cao, J., Liu, R., Gao, W., Wang, X., Liang, J.: Participant incentive mechanism toward quality-oriented sensing: understanding and application. ACM Trans. Sen. Netw. 15(2), 21:1–21:25 (2019)
Duan, Z., Tian, L., Yan, M., Cai, Z., Han, Q., Yin, G.: Practical incentive mechanisms for IoT-based mobile crowdsensing systems. IEEE Access 5, 20383–20392 (2017)
Singh, V.K., Mukhopadhyay, S., Xhafa, F., Krause, P.: A quality-assuring, combinatorial auction based mechanism for IoT-based crowdsourcing. In: Advances in Edge Computing: Massive Parallel Processing and Applications, vol. 35, pp. 148–177. IOS Press (2020)
Mukhopadhyay, J., Singh, V.K., Mukhopadhyay, S., Pal, A.: Clustering and auction in sequence: a two fold mechanism for participatory sensing. In: Yadav, N., Yadav, A., Bansal, J.C., Deep, K., Kim, J.H. (eds.) Harmony Search and Nature Inspired Optimization Algorithms, pp. 347–356. Springer, Singapore (2019)
Singh, V.K., Mukhopadhyay, S., Xhafa, F., Sharma, A.: A budget feasible peer graded mechanism for IoT-based crowdsourcing. J. Ambient Intell. Humanized Comput. 11(4), 1531–1551 (2020)
Restuccia, F., Das, S.K., Payton, J.: Incentive mechanisms for participatory sensing: survey and research challenges. Trans. Sensor Netw. 12(2), 13:1–13:40 (2016)
Lee, J.S., Hoh, B.: Dynamic pricing incentive for participatory sensing. Elsevier J. Pervasive Mob. Comput. 6(6), 693–708 (2010)
Zhao, D., Li, X.Y., Ma, H.: How to crowdsource tasks truthfully without sacrificing utility: online incentive mechanisms with budget constraint. In: Annual IEEE International Conference on Computer Communications (INFOCOM), pp. 1213–1221 (2014)
Feng, Z., Zhu, Y., Zhang, Q., Ni, L.M., Vasilakos, A.V.: Trac: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing, pp. 1231–1239 (2014)
Gao, L., Fen, H., Jianwei, H.: Providing long-term participation incentive in participatory sensing (2015). arXiv preprint arXiv:1501.02480
Yu, R., Liu, R., Wang, X., Cao, J.: Improving data quality with an accumulated reputation model in participatory sensing systems. Sensors 14(3), 5573–5594 (2014)
Bhattacharjee, J., Pal, A., Mukhopadhyay, S., Bharasa, A.: Incentive and quality aware participatory sensing system. In: \(12^{th}\) International Conference on Dependable, Autonomic and Secure Computing (DASC), pp. 382–387. IEEE Computer Society (2014)
Xu, W., Huang, H., Sun, Y. E., Li, F., Zhu, Y.: Data: a double auction based task assignment mechanism in crowdsourcing systems. In: 2013 8th International Conference on Communications and Networking in China (CHINACOM), pp. 172–177 (2013)
Wei, Y., Zhu, Y., Zhu, H., Zhang, Q., Xue, G.: Truthful online double auctions for dynamic mobile crowdsourcing. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 2074–2082 (2015)
Huang, H., Xin, Y., Sun, Y., Yang, W.: A truthful double auction mechanism for crowdsensing systems with max-min fairness. In: 2017 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6 (2017)
Kleinberg, J., Tardos, É.: Algorithm Design. Addison-Wesley, Boston (2006)
Bredin, J., Parkes, D.C.: Models for truthful online double auctions. In: \(21^{st}\) International Conference on Uncertainty in Artificial Intelligence, pp. 50–59. AUAI press (2005)
Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms. MIT Press, Cambridge (2009)
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Mukhopadhyay, J., Singh, V.K., Mukhopadhyay, S., Pal, A. (2021). A Balanced Dissemination of Time Constraint Tasks in Mobile Crowdsourcing: A Double Auction Perspective. In: Barolli, L., Takizawa, M., Yoshihisa, T., Amato, F., Ikeda, M. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2020. Lecture Notes in Networks and Systems, vol 158. Springer, Cham. https://doi.org/10.1007/978-3-030-61105-7_8
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