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 needed 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
spatio-temporal Transformer network that learns from the historical sensing
data and predicts the ground truth of the data submitted by MUs. However, due
to the noise in historical data for training and the bursty values within
sensing data, the prediction results can be inaccurate. To address this issue,
we use the implications among the sensing data, which are learned from the
prediction results but are stable and less affected by inaccurate predictions,
to evaluate the quality of the data. Finally, we design a reputation-based
truth discovery (TD) module for identifying low-quality data with their
implications. Given the 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 the PRBTD method outperforms existing
methods in terms of identification accuracy and data quality enhancement.
| Search Query: ArXiv Query: search_query=au:”Bo Gu”&id_list=&start=0&max_results=3