Coalition-based task assignment with priority-aware fairness in spatial crowdsourcing

Author:

Zhao YanORCID,Zheng Kai,Wang Ziwei,Deng Liwei,Yang Bin,Pedersen Torben Bach,Jensen Christian S.,Zhou Xiaofang

Abstract

AbstractWith the widespread use of networked and geo-positioned mobile devices, e.g., smartphones, Spatial Crowdsourcing (SC), which refers to the assignment of location-based tasks to moving workers, is drawing increasing attention. One of the critical issues in SC is task assignment that allocates tasks to appropriate workers. We propose and study a novel SC problem, namely Coalition-based Task Assignment (CTA), where the spatial tasks (e.g., home improvement and furniture installation) may require more than one worker (forming a coalition) to cooperate to maximize the overall rewards of workers. We design a greedy and an equilibrium-based CTA approach. The greedy approach forms a set of worker coalitions greedily for performing tasks and uses an acceptance probability to identify high-value task assignments. In the equilibrium-based approach, workers form coalitions in sequence and update their strategies (i.e., selecting a best-response task), to maximize their own utility (i.e., the reward of the coalition they belong to) until a Nash equilibrium is reached. Since the equilibrium obtained is not unique and optimal in terms of total rewards, we further propose a simulated annealing scheme to find a better Nash equilibrium. To achieve fair task assignments, we optimize the framework to distribute rewards fairly among workers in a coalition based on their marginal contributions and give workers who arrive first at the SC platform highest priority. Extensive experiments demonstrate the efficiency and effectiveness of the proposed methods on real and synthetic data.

Funder

Aalborg University Library

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems

Reference47 articles.

1. Chen, Z., Fu, R., Zhao, Z., Liu, Z., Xia, L., Chen, L., Cheng, P., Cao, C.C., Tong, Y., Zhang, C.J.: gmission: a general spatial crowdsourcing platform. PVLDB 7(13), 1629–1632 (2014)

2. Cheng, P., Chen, L., Ye, J.: Cooperation-aware task assignment in spatial crowdsourcing. In: ICDE, pp. 1442–1453 (2019)

3. Cheng, P., Lian, X., Chen, L., Han, J., Zhao, J.: Task assignment on multi-skill oriented spatial crowdsourcing. TKDE 28(8), 2201–2215 (2015)

4. Cheng, P., Lian, X., Chen, Z., Fu, R., Chen, L., Han, J., Zhao, J.: Reliable diversity-based spatial crowdsourcing by moving workers. PVLDB 8(10), 1022–1033 (2015)

5. Cui, Y., Deng, L., Zhao, Y., Yao, B., Zheng, V.W., Zheng, K.: Hidden poi ranking with spatial crowdsourcing. In: SIGKDD, pp. 814–824 (2019)

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