Abstract
With the popularity of smart devices and the development of high-speed wireless networks, the spatial crowdsourcing has attracted much attention from both academia and industry (e.g., Uber and TaskRabbit). Specifically, a spatial crowdsourcing platform assigns workers to location-based tasks according to their current positions, then the workers need to physically move to the specified locations to conduct the assigned tasks. In this paper, we consider an important spatial crowdsourcing problem, namely cooperation-aware spatial crowdsourcing (CA-SC), where spatial tasks (e.g., collecting the Wi-Fi signal strength in one building) are time-constrained and require more than one worker to complete thus the cooperation among assigned workers is essential to the result. Our CA-SC problem is to assign workers to spatial tasks such that the overall cooperation quality is maximized. We prove that the CA-SC problem is NP-hard by reducing from the k-set packing problem, thus intractable. To tackle the CA-SC problem, we propose task-priority greedy (TPG) approach and game theoretic (GT) approach with two optimization methods to quickly solve the CA-SC problem and achieve high total cooperation quality scores. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches over both real and synthetic datasets.