Latency-oriented task completion via spatial crowdsourcing

Spatial crowdsourcing brings in a new approach for social media and location-based services (LBS) to collect location specific information via mobile users. For example, when a user checks in at a shop on Facebook, he will immediately receive and is asked to complete a set of tasks such as 'what is the opening hour of the shop'. It is non-Trivial to complete a set of tasks timely and accurately via spatial crowdsourcing. Since workers in spatial crowdsourcing are often transient and limited in number, these social media platforms need to properly allocate workers within the set of tasks such that all tasks are completed (i) with high quality and (ii) with a minimal latency (estimated by the arriving index of the last recruited worker). Solutions to quality and latency control in traditional crowdsourcing are inapplicable in this problem because they either assume sufficient workers or ignore the spatiotemporal factors. In this work, we define the Latency-oriented Task Completion (LTC) problem, which trades off quality and latency (number of workers) of task completion in spatial crowdsourcing. We prove that the LTC problem is NP-hard. We first devise a minimum-cost-flow based algorithm with a constant approximation ratio for the LTC problem in the offline scenario, where all information is known a prior. Then we study the more practical online scenario of the LTC problem, where workers appear dynamically and the platform needs to arrange tasks for each worker immediately based on partial information. We design two greedy-based algorithms with competitive ratio guarantees to solve the LTC problem in the online scenario. Finally, we validate the effectiveness and efficiency of the proposed solutions through extensive evaluations on both synthetic and real-world datasets.