UniTask: A Unified Task Assignment Design for Mobile Crowdsourcing-Based Urban Sensing

Zhidan Liu, Zhenjiang Li, Kaishun Wu

Abstract: Mobile crowdsourcing (MCS) becomes an emerging paradigm for various useful urban sensing application designs by assigning the crowdsourcing tasks to the participants with rich-sensor equipped mobile devices. To effectively assign MCS tasks, many research efforts have been made in the literature. However, most prior schemes mainly optimize certain performance metrics in the assignment, yet overlooking other metrics, which thus cannot guarantee the overall system performance. This also limits the applicability of the proposed solution dedicated to the targeted performance metrics only. In this paper, we present UniTask, a unified task assignment design to address these issues. UniTask jointly considers the representative MCS performance metrics, including coverage, latency, and accuracy, to optimize the overall system utility. We mathematically formulate this problem and prove its NP-hardness. To efficiently schedule tasks, we also propose a utility-aware heuristic algorithm in UniTask. Moreover, a set of optimization techniques are further designed to enhance UniTask. Extensive evaluations are performed on two real-world datasets. Experimental results demonstrate that utility is an effective indicator of the system's overall performance. With an optimization on the system utility, UniTask can outperform the baseline methods on these individual performance metrics.