Summary of Can We Enhance the Quality Of Mobile Crowdsensing Data Without Ground Truth?, by Jiajie Li et al.
Can We Enhance the Quality of Mobile Crowdsensing Data Without Ground Truth?
by Jiajie Li, Bo Gu, Shimin Gong, Zhou Su, Mohsen Guizani
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Mobile crowdsensing (MCS) requires a reliable method to detect low-quality sensing data and malicious users disrupting system operations. This paper proposes a prediction- and reputation-based truth discovery (PRBTD) framework, which separates high-quality from low-quality data in sensing tasks. The PRBTD framework employs a correlation-focused spatio-temporal Transformer network that learns from historical sensing data to predict ground truth. However, due to noise and bursty values, predictions can be inaccurate. To address this, implications among sensing data are learned to evaluate quality. A reputation-based TD module identifies low-quality data with implications. Given sensing data, PRBTD eliminates noisy data and detects malicious users with high accuracy. Experimental results show that PRBTD outperforms existing methods in terms of identification accuracy and data quality enhancement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MCS is a way for people to contribute data using their mobile devices. But some of this data might be bad or fake, which can mess up the system. This paper suggests a new way to solve this problem called PRBTD. It uses two main parts: one that predicts what’s true and one that figures out who’s doing the wrong thing. The prediction part looks at past data to make good guesses, but it can be tricky because of noise and weird patterns in the data. So they use another way to look at the data and see if it makes sense. Finally, they put all these pieces together to find the bad data and identify who’s causing problems. This new method works better than old methods for keeping the data good and finding troublemakers. |
Keywords
» Artificial intelligence » Transformer
