Journal
CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT
Volume -, Issue -, Pages 297-306Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3132847.3132894
Keywords
spatial crowdsourcing; spatial ask assignment; user mobility
Funding
- National Natural Science Foundation of China [61502324, 61532018, 61572335]
- Natural Science Foundation of Jiangsu Province of China [BK20151223]
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With the proliferation of GPS-enabled smart devices and increased availability of wireless network, spatial crowdsourcing (SC) has been recently proposed as a framework to automatically request workers (i.e., smart device carriers) to perform location-sensitive tasks (e.g., taking scenic photos, reporting events). In this paper we study a destination-aware task assignment problem that concerns the optimal strategy of assigning each task to proper worker such that the total number of completed tasks can be maximized whilst all workers can reach their destinations before deadlines after performing assigned tasks. Finding the global optimal assignment turns out to be an intractable problem since it does not imply optimal assignment for individual worker. Observing that the task assignment dependency only exists amongst subsets of workers, we utilize tree-decomposition technique to separate workers into independent clusters and develop an efficient depth-first search algorithm with progressive hounds to prune non-promising assignments. Our empirical studies demonstrate that our proposed technique is quite effective and settle the problem nicely.
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