Causal-Enhanced Deep Spatio-Temporal Truth Discovery for Mobile Crowdsensing

To address the problem of quality misattribution caused by prediction error propagation and environmental confounding in no-ground-truth Mobile Crowdsensing (MCS) scenarios, a Causal-Enhanced Deep Spatio-Temporal Truth Discovery framework (CE-DSTTD) was proposed. The framework consisted of two compo...

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מידע ביבליוגרפי

Principais autores:	Jiang Weijin, Shi Shunjiu, Ouyang Yuming, Xu Yusheng, Liu Xixi, Cui Xinyu, Liu Zhihua
פורמט:	Artigo
שפה:	Chinês
יצא לאור:	Editorial Department of Journal on Communications 2026-01-01
סדרה:	Tongxin xuebao
נושאים:	Mobile Crowdsensing Truth Discovery Spatio-Temporal Prediction Deconfounding Correction Structural Causal Model
גישה מקוונת:	http://www.joconline.com.cn/thesisDetails#10.11959/j.issn.1000-436x.TXXB260206
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