Mobile crowdsensing (MCS) has emerged as a prominent trend across various domains. However, ensuring the quality of the sensing data submitted by mobile users (MUs) remains a complex and challenging problem. To address this challenge, an advanced method is required to detect low-quality sensing data and identify malicious MUs that may disrupt the normal operations of an MCS system. Therefore, this article proposes a prediction- and reputation-based truth discovery (PRBTD) framework, which can separate low-quality data from high-quality data in sensing tasks. First, we apply a correlation-focused spatial-temporal transformer network to predict the ground truth of the input sensing data. Then, we extract the sensing errors of the data as features based on the prediction results to calculate the implications among the data. Finally, we design a reputation-based truth discovery (TD) module for identifying low-quality data with their implications. Given sensing data submitted by MUs, PRBTD can eliminate the data with heavy noise and identify malicious MUs with high accuracy. Extensive experimental results demonstrate that PRBTD outperforms the existing methods in terms of identification accuracy and data quality enhancement.

移动众感知是各领域中的一个突出趋势，然而确保移动用户提交的传感数据的质量仍然是一个复杂而具有挑战性的问题。为了解决这个问题，本文提出了一种基于预测和声誉的真实性发现框架，可以在感知任务中筛选出低质量数据并识别可能破坏系统正常运行的恶意用户。通过广泛的实验结果表明，该框架在识别准确性和数据质量提升方面优于现有方法。

能否在没有地面真实数据的情况下提高移动众感知数据的质量？