Abstract
Spatial crowdsourcing platforms have become increasingly popular in people's daily life. A fundamental problem in spatial crowdsourcing is task assignment, which assigns spatial tasks to the workers appropriately in order to satisfy certain objectives. Previous studies usually focus on the real-time micro-task allocation, which does not consider the dependency relationships among tasks. To address this limitation, in this paper, we define and formulate a new problem, called Online Dependent Task Assignment (ODTA) in preference aware spatial crowdsourcing. We first prove that ODTA is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal{NP}\ $</tex-math></inline-formula> -hard. Then, we design a threshold-based algorithm in the adversarial order model and obtain a near-optimal theoretical bound on the competitive ratio. More importantly, considering the random order arrival model, we further present three algorithms based on a two-stage framework, namely ODTA-Greedy, ODTA-Greedy-OP and ODTA-OPT, which are more effective with a constant competition ratio of 1/8, 1/8 and 1/4 respectively. Experimental results on both synthetic and real datasets show that our proposed ODTA-OPT approach outperforms the representative approaches in terms of overall utility
Published Version
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