FIS-ONE: Floor Identification System with One Label for Crowdsourced RF Signals
FIS-ONE: Floor Identification System with One Label for Crowdsourced RF Signals
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DOI：
10.1109/icdcs57875.2023.00039
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发表时间：
2023-07
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影响因子：
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通讯作者：
Weipeng Zhuo;Kaili Chiu;Jierun Chen;Ziqi Zhao;Shueng-Han Gary Chan;Sangtae Ha;Chul-Ho Lee
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文献类型：
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作者：
Weipeng Zhuo;Kaili Chiu;Jierun Chen;Ziqi Zhao;Shueng-Han Gary Chan;Sangtae Ha;Chul-Ho Lee
众包射频信号的地板标签对于许多智慧城市应用至关重要，例如多层室内定位，地理围栏和机器人监控。为了建立预测模型，在测量时识别新的射频信号的层数，使用众包射频信号的传统方法假设每层至少有很少的标记信号样本可用。在这项工作中，我们进一步推动了信封，并证明了在技术上可行的，在底层只有一个楼层标记的信号样本，而其余的信号样本未标记。我们提出了一种新颖的地板识别系统，只有一个标记样本。fi - one包括两个步骤，即信号聚类和聚类索引。我们首先建立一个二部图来建模射频信号样本，并使用我们的基于注意力的图神经网络模型获得每个节点（每个信号样本）的潜在表示，以便射频信号样本可以更准确地聚类。然后，我们通过利用来自接入点的信号可以在不同楼层检测到的观察，即信号溢出，解决了用适当的楼层标签索引集群的问题。具体来说，我们将聚类索引问题表述为一个组合优化问题，并证明它等同于求解一个旅行商问题，其（近）最优解可以有效地找到。我们已经实现了fisone，并在微软数据集和三个大型购物中心验证了它的有效性。我们的研究结果表明，FIS- ONE显著优于其他基线算法，仅使用一个地板标记信号样本，调整后的兰德指数提高了23%，规范化互信息提高了25%。
Floor labels of crowdsourced RF signals are crucial for many smart-city applications, such as multi-floor indoor localization, geofencing, and robot surveillance. To build a prediction model to identify the floor number of a new RF signal upon its measurement, conventional approaches using the crowdsourced RF signals assume that at least few labeled signal samples are available on each floor. In this work, we push the envelope further and demonstrate that it is technically feasible to enable such floor identification with only one floor-labeled signal sample on the bottom floor while having the rest of signal samples unlabeled. We propose FIS-ONE, a novel floor identification system with only one labeled sample. FIS-ONE consists of two steps, namely signal clustering and cluster indexing. We first build a bipartite graph to model the RF signal samples and obtain a latent representation of each node (each signal sample) using our attention-based graph neural network model so that the RF signal samples can be clustered more accurately. Then, we tackle the problem of indexing the clusters with proper floor labels, by leveraging the observation that signals from an access point can be detected on different floors, i.e., signal spillover. Specifically, we formulate a cluster indexing problem as a combinatorial optimization problem and show that it is equivalent to solving a traveling salesman problem, whose (near-)optimal solution can be found efficiently. We have implemented FIS-ONE and validated its effectiveness on the Microsoft dataset and in three large shopping malls. Our results show that FIS- ONE outperforms other baseline algorithms significantly, with up to 23 % improvement in adjusted rand index and 25% improvement in normalized mutual information using only one floor-labeled signal sample.