Turbo-GTS: Scaling Mobile Crowdsourcing using Workload-Balancing Bisection Tree

In mobile crowdsourcing, workers are financially motivated to perform self-selected tasks to maximize their revenue. Unfortunately, the existing task scheduling approaches in mobile crowdsourcing fail to scale for massive tasks and large geographic areas. We present Turbo-GTS, a system that assigns tasks to each worker to maximize the total number of the tasks that can be completed for an entire worker group while taking into account various spatial and temporal constraints, such as task execution duration, task expiration time, and worker/task geographic locations. The core of Turbo-GTS is WBT-NNH and WBT-NUD, our two newly developed scheduling algorithms, which build on the algorithms, QT-NNH and QT-NUD, proposed in our prior work [5]. The key idea is that Turbo-GTS performs dynamic workload balancing among all workers using the proposed Workload-balancing Bisection Tree (WBT) in support of large-scale Geo-Task Scheduling (GTS). Turbo-GTS includes an interactive interface for users to load the current task/worker distributions and compare the task assignment of each worker returned by different algorithms in a real-time fashion. Using the Foursquare mobile user check-in data in New York City and Tokyo, we show the superiority of Turbo-GTS over the state of the art in terms of the total number of the tasks that can be accomplished by the entire worker group and the corresponding running time. We also demonstrate the front-end interface of Turbo-GTS with two exploratory use cases in New York City.