Can We Enhance the Quality of Mobile Crowdsensing Data Without Ground Truth?
Li, Jiajie Gu, Bo Gong, Shimin Su, Zhou Guizani, Mohsen
Li, Jiajie
Gu, Bo
Gong, Shimin
Su, Zhou
Guizani, Mohsen
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
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Research Projects
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Journal Issue
Abstract
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.
Citation
J. Li, B. Gu, S. Gong, Z. Su and M. Guizani, "Can We Enhance the Quality of Mobile Crowdsensing Data Without Ground Truth?," in IEEE Transactions on Mobile Computing, doi: 10.1109/TMC.2025.3526277
Source
IEEE Transactions on Mobile Computing
Conference
Keywords
Sensors, Mobile computing, Data integrity, Correlation, Accuracy, Data aggregation, Crowdsensing, Sparse matrices, Transformers, Noise
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Source
Publisher
IEEE