Journal
IEEE TRANSACTIONS ON MOBILE COMPUTING
Volume 18, Issue 7, Pages 1661-1673Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2018.2865355
Keywords
Worker recruitment; mobile crowd sensing; social network; smart city
Funding
- NSFC [61702017]
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Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper leverages the influence propagation on the social network to assist the MCS worker recruitment. We first select a subset of users on the social network as initial seeds and push MCS tasks to them. Then, influenced users who accept tasks are recruited as workers, and the ultimate goal is to maximize the coverage. Specifically, to select a near-optimal set of seeds, we propose two algorithms, named Basic-Selector and Fast-Selector, respectively. Basic-Selector adopts an iterative greedy process based on the predicted mobility, which has good performance but suffers from inefficiency concerns. To accelerate the selection, Fast-Selectoris proposed, which is based on the interdependency of geographical positions among friends. Empirical studies on two real-world datasets verify that Fast-Selector achieves higher coverage than baseline methods under various settings, meanwhile, it is much more efficient than Basic-Selectorwhile only sacrificing a slight fraction of the coverage.
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