Mobile crowdsensing leverages the power of a vast group of participants to collect sensory data, thus presenting an economical solution for data collection. However, due to the variability among participants, the quality of sensory data varies significantly, making it crucial to extract truthful information from sensory data of differing quality. Additionally, given the fixed time and monetary costs for the participants, they typically only perform a subset of tasks. As a result, the datasets collected in real-world scenarios are usually sparse. Current truth discovery methods struggle to adapt to datasets with varying sparsity, especially when dealing with sparse datasets. In this article, we propose an adaptive Hypergraph-based EM truth discovery method, HGEM. The HGEM algorithm leverages the topological characteristics of hypergraphs to model sparse datasets, thereby improving its performance in evaluating the reliability of participants and the true value of the event to be observed. Experiments based on simulated and real-world scenarios demonstrate that HGEM consistently achieves higher predictive accuracy.

移动群智感知利用大量参与者的力量来收集感知数据，从而为数据收集提供了一种经济的解决方案。然而，由于参与者之间存在差异，感知数据的质量差异很大，因此从不同质量的感知数据中提取真实信息至关重要。此外，考虑到参与者的固定时间和金钱成本，他们通常只执行一部分任务。因此，在现实场景中收集的数据集通常是稀疏的。当前的真相发现方法很难适应稀疏度不同的数据集，尤其是在处理稀疏数据集时。在本文中，我们提出了一种基于自适应超图的 EM 真值发现方法，HGEM。 HGEM算法利用超图的拓扑特征对稀疏数据集进行建模，从而提高其评估参与者的可靠性和待观察事件的真实价值的性能。基于模拟和现实场景的实验表明，HGEM 始终能够实现更高的预测准确性。