Kavli Affiliate: Bo Gu
| First 5 Authors: Jiajie Li, Bo Gu, Shimin Gong, Zhou Su, Mohsen Guizani
| Summary:
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.
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