Vaccinated, What Next? An Efficient Contact and Social Distance Tracing Based on Heterogeneous Telco Data
Vaccinated, What Next? An Efficient Contact and Social Distance Tracing Based on Heterogeneous Telco Data
复制标题
DOI：
10.1109/jsen.2022.3194540
复制
发表时间：
2022-09
影响因子：
4.3
通讯作者：
Hamada Rizk;Asmaa Saeed;Hirozumi Yamaguchi
中科院分区：
文献类型：
--
作者：
Hamada Rizk;Asmaa Saeed;Hirozumi Yamaguchi
对安全提升系统的需求一直在增加，特别是为了限制COVID-19的快速传播。实时保持社交距离是遏制大流行爆发的重要应用。很少有人提出需要基础设施设置和高端电话的系统。因此，它们具有有限的普遍采用。蜂窝技术享有广泛的可用性和他们的支持，由商品手机，这表明利用它的社会距离跟踪。然而，共享相同环境的用户可能连接到不同网络配置的不同电信提供商。传统的基于蜂窝的定位系统通常为每个提供商构建单独的模型，导致社交距离性能下降。在这篇文章中，我们提出了CellTrace，一个基于深度学习的社交距离保持系统。具体来说，CellTrace使用深度学习版本的典型相关性分析来查找跨提供者表示。不同提供商的数据在这种表示中高度相关，并用于训练用于估计社交距离的本地化模型。此外，CellTrace还集成了不同的模块，可以提高深度模型对过度训练和噪声的泛化能力。我们已经在两种不同的环境中实施和评估了CellTrace，并与最先进的细胞定位和接触追踪技术进行了并排比较。结果表明，CellTrace可以准确地定位用户和估计接触发生，无论连接的供应商，亚米中位数误差和97%的准确性，分别。此外，我们表明，CellTrace在各种具有挑战性的情况下具有强大的性能。
The demand for safety-boosting systems is always increasing, especially to limit the rapid spread of COVID-19. Real-time social distance preservation is an essential application toward containing the pandemic outbreak. Few systems have been proposed which require infrastructure setup and high-end phones. Therefore, they have limited ubiquitous adoption. Cellular technology enjoys widespread availability and their support by commodity cellphones, which suggest leveraging it for social distance tracking. However, users sharing the same environment may be connected to different telecom providers of different network configurations. Traditional cellular-based localization systems usually build a separate model for each provider, leading to a drop in social distance performance. In this article, we propose CellTrace, a deep learning-based social distance preserving system. Specifically, CellTrace finds a cross-provider representation using a deep learning version of canonical correlation analysis. Different providers’ data are highly correlated in this representation and used to train a localization model for estimating the social distances. In addition, CellTrace incorporates different modules that improve the deep model’s generalization against overtraining and noise. We have implemented and evaluated CellTrace in two different environments with a side-by-side comparison with the state-of-the-art cellular localization and contact tracing techniques. The results show that CellTrace can accurately localize users and estimate the contact occurrence, regardless of the connected providers, with a submeter median error and 97% accuracy, respectively. In addition, we show that CellTrace has robust performance in various challenging scenarios.