Mixed Priority Queue Scheduling Based on Spectral Clustering in Spatial Crowdsourcing

With the ubiquity of GPS-enabled smart devices equipped with high-fidelity sensors and increased availability of the wireless network, spatial crowdsourcing has been recently proposed as a general framework to employ smart device carriers as workers to provide services and perform location-sensitive tasks. In this paper, we focus on the task assignment in spatial crowdsourcing, which aims to find the optimal strategy to assign each task to a proper worker such that the total number of completed tasks is maximized and the traveling time cost is minimized, while the workers can return to their initial locations before deadlines after performing the assigned tasks. Finding the optimal global assignment turns out to be intractable since it does not simply imply optimality for an individual worker, as a typical nearest-neighbor heuristic does not render a satisfactory result in general. In spatial crowdsourcing, we model the task assignment problem as a multiple objective joint optimization problem, which focuses on maximizing accomplished task rate and minimizing travel time cost rate simultaneously, and solves it with a mixed priority queue scheduling algorithm. We also introduce a spectral clustering algorithm in spatial crowdsourcing for the first time to divide the task network into different subdomains, considering the task clustering phenomena in real scenarios. Experiments on synthetic and real-world networks demonstrate the efficiency and effectiveness of our method in the task assignment of spatial crowdsourcing and provide insights into its application in practice.