Crowdsensing has emerged as a promising data collection paradigm for utilizing embedded sensors in mobile devices to monitor the real world. However, due to the existence of malicious users, data quality problem has become a critical issue in the crowdsensing system. To address this problem, many mechanisms have been proposed to improve the quality of submitted observations, which are either not cost-efficient enough to be widely applied or only compatible with limited applications. In this paper, we propose an efficient malicious user detection method based on the Hidden Markov Model. It takes users’ observations as input and reports malicious users with the assistance of a pre-detection phase. We further incorporate the proposed detection method into task allocation, presenting an anti-malicious task allocation mechanism. The experimental results reveal that the proposed detection algorithm can identify malicious users with high accuracy and F1-Score. The proposed allocation algorithm also can significantly prevent malicious users from taking assignments, which eventually improves data quality.

Crowdsensing 已成为利用移动设备中的嵌入式传感器来监控现实世界的有前途的数据收集范式。然而，由于恶意用户的存在，数据质量问题成为众包感知系统中的一个关键问题。为了解决这个问题，已经提出了许多机制来提高提交观察的质量，这些机制要么成本效率低，无法广泛应用，要么仅与有限的应用兼容。在本文中，我们提出了一种基于隐马尔可夫模型的高效恶意用户检测方法。它以用户的观察作为输入，并在预检测阶段的帮助下报告恶意用户。我们进一步将所提出的检测方法结合到任务分配中，提出了一种反恶意的任务分配机制。实验结果表明，所提出的检测算法能够以较高的准确率和F1-Score识别恶意用户。所提出的分配算法还可以显着防止恶意用户接受分配，最终提高数据质量。