Online Dependent Task Assignment in Preference Aware Spatial Crowdsourcing
Jiajun Yao, Lei Yang, Xiaohua Xu
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 article, 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><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 <inline-formula><tex-math notation="LaTeX">$\frac{1}{8}$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$\frac{1}{8}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\frac{1}{4}$</tex-math></inline-formula> , 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.