Semi-supervised Learning with Network Embedding on Ambient RF Signals for Geofencing Services
Semi-supervised Learning with Network Embedding on Ambient RF Signals for Geofencing Services
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DOI：
10.1109/icde55515.2023.00208
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Weipeng Zhuo;Kaili Chiu;Jierun Chen;Jiajie Tan;Edmund Sumpena;Shueng-Han Gary Chan;Sangtae Ha
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文献类型：
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作者：
Weipeng Zhuo;Kaili Chiu;Jierun Chen;Jiajie Tan;Edmund Sumpena;Shueng-Han Gary Chan;Sangtae Ha
在老年人护理、痴呆症防游荡和流行病控制等应用中，重要的是要确保人们处于预定义的区域内，以确保他们的安全和福祉。我们提出了GEM，一个实用的，半监督的地理围栏系统与网络嵌入，这是只基于周围的射频（RF）信号。GEM将测量的RF信号记录建模为加权二分图。通过一侧的接入点和另一侧的信号记录，它能够精确地捕获信号记录之间的关系。然后，GEM通过一种名为BiSAGE的新型二分网络嵌入算法从图中学习节点嵌入，该算法基于具有新型双层SAmple和aggregatE机制以及非均匀邻域采样的二分图神经网络。使用学习的嵌入，GEM最终通过用于进出检测的增强的基于直方图的算法构建一类分类模型，即，以检测用户是否在该区域内。该模型还不断改进新收集的信号记录。我们通过在不同环境中的广泛实验证明，GEM显示出最先进的性能，F分数提高了34%。GEM中的BiSAGE导致F评分比没有BiSAGE的改善54%。
In applications such as elderly care, dementia anti-wandering and pandemic control, it is important to ensure that people are within a predefined area for their safety and well-being. We propose GEM, a practical, semi-supervised Geofencing system with network EMbedding, which is based only on ambient radio frequency (RF) signals. GEM models measured RF signal records as a weighted bipartite graph. With access points on one side and signal records on the other, it is able to precisely capture the relationships between signal records. GEM then learns node embeddings from the graph via a novel bipartite network embedding algorithm called BiSAGE, based on a Bipartite graph neural network with a novel bi-level SAmple and aggreGatE mechanism and non-uniform neighborhood sampling. Using the learned embeddings, GEM finally builds a one-class classification model via an enhanced histogram-based algorithm for in-out detection, i.e., to detect whether the user is inside the area or not. This model also keeps on improving with newly collected signal records. We demonstrate through extensive experiments in diverse environments that GEM shows state-of-the-art performance with up to 34% improvement in F-score. BiSAGE in GEM leads to a 54% improvement in F-score, as compared to the one without BiSAGE.