Abstract
In mobile crowdsourcing, workers are financially motivated to perform as many self-selected tasks as possible to maximize their revenue. Unfortunately, the existing task scheduling approaches in mobile crowdsourcing fail to consider task execution duration and do not scale for massive tasks and large geographic areas (e.g., a whole city). In this paper, we study on the geo-task scheduling problem (GTS) under the various spatial and temporal constraints in real-world mobile crowdsourcing applications, including task execution duration and task expiration time. Given the location of a worker, the goal of our study is to find an optimal task execution sequence that maximizes the number of tasks that could be finished. Since the exact solution to the maximum task scheduling is computationally intractable, we propose two sub-optimal approaches (LCPF and NUD-IC) based on the particle filtering and the DBSCAN clustering.