Can We Enhance the Quality of Mobile Crowdsensing Data Without Ground Truth?

TL;DR

This paper introduces a novel prediction- and reputation-based framework for improving mobile crowdsensing data quality without relying on ground truth, effectively identifying low-quality and malicious data through advanced prediction and reputation mechanisms.

Contribution

It proposes the PRBTD framework combining a spatio-temporal Transformer and reputation-based truth discovery to enhance data quality and detect malicious users without ground truth.

Findings

PRBTD outperforms existing methods in accuracy

Effectively filters noisy data and identifies malicious users

Improves overall sensing data quality

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.