Mobile crowdsourcing has emerged as a promising collaboration paradigm in which each spatial task requires a set of mobile workers in near vicinity to the target location. Considering the desired privacy of the participating mobile devices, trust is considered to be an important factor to enable effective collaboration in mobile crowdsourcing. The main impediment to the success of mobile crowdsourcing is the allocation of trustworthy mobile workers to nearby spatial tasks for collaboration. This process becomes substantially more challenging for large-scale online spatial task allocations in uncertain mobile crowdsourcing systems. The uncertainty can mislead the task allocation, resulting in performance degradation. Moreover, the large-scale nature of real-world crowdsourcing poses a considerable challenge to spatial task allocation in uncertain environments. To address the aforementioned challenges, first, an optimization problem of mobile crowdsourcing task allocation is formulated to maximize the trustworthiness of workers and minimize movement distance costs. Second, for the uncertain crowdsourcing scenario, a Markov decision process-based mobile crowdsourcing model (MCMDP) is formulated to illustrate the dynamic trust-aware task allocation problem. Third, to solve large-scale MCMDP problems in a stable manner, this study proposes an improved deep Q-learning-based trust-aware task allocation (ImprovedDQL-TTA) algorithm that combines trust-aware task allocation and deep Q-learning as an improvement over the uncertain mobile crowdsourcing systems. Finally, experimental results illustrate that the ImprovedDQL-TTA algorithm can stably converge in a number of training iterations. Compared with the reference algorithm, our proposed algorithm achieves effective solutions on the experimental data sets.

中文翻译：

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基于深度q学习的不确定移动众包信任感知任务分配方法

移动众包已经成为一种很有前途的协作范例，其中每个空间任务都需要在目标位置附近的一组移动工作人员。考虑到参与移动设备的所需隐私，信任被认为是在移动众包中实现有效协作的重要因素。移动众包成功的主要障碍是将可信赖的移动工作人员分配给附近的空间任务以进行协作。对于不确定的移动众包系统中的大规模在线空间任务分配，此过程变得更具挑战性。这种不确定性可能会误导任务分配，从而导致性能下降。而且，现实世界中众包的大规模性质对不确定环境中的空间任务分配提出了相当大的挑战。为了解决上述挑战，首先，提出了移动众包任务分配的优化问题，以最大化工人的可信赖度并最小化移动距离成本。其次，针对不确定的众包场景，提出了一种基于马尔可夫决策过程的移动众包模型（MCMDP）来说明动态的信任感知任务分配问题。第三，为了稳定地解决大规模MCMDP问题，本研究提出了一种改进的基于深度Q学习的信任感知任务分配（ImprovedDQL-TTA）算法，该算法结合了信任感知任务分配和深度Q学习作为对不确定的移动众包系统的改进。最后，实验结果表明，改进的DQL-TTA算法可以在许多训练迭代中稳定收敛。与参考算法相比，我们提出的算法在实验数据集上获得了有效的解决方案。