Crowdsourcing is considered an efficient and promising paradigm for constructing large-scale signal fingerprint radio maps due to the proliferation of Wi-Fi-enabled devices. However, a crowdsourced indoor positioning system (IPS) has to handle diverse devices and the inherent heterogeneity in received signal strength (RSS) measurements. To address the device heterogeneity problem, differential fingerprinting methods have been explored, which mitigate the device characteristics that cause RSS from different commercial devices to report differently. In this article, we focus on mean differential fingerprinting (MDF) that produces the differential fingerprints by subtracting the mean RSS value of all access points from the original RSS fingerprints. We study the localization performance of the MDF method by means of the Cramér–Rao lower bound (CRLB) and show analytically that it outperforms another method that addresses device diversity. Furthermore, we evaluate the localization accuracy of existing solutions using real-life Wi-Fi RSS data sets collected by multiple consumer devices. The experimental results confirm our analytical findings and demonstrate the effectiveness of the MDF method to mitigate device diversity, as well as other factors that affect the RSS readings, including the device carrying mode and power control schemes of the Wi-Fi infrastructure, thus contributing to the wider adoption of crowdsourced IPS.

中文翻译：

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用于众包物联网本地化的差分信号强度指纹的 Cramér-Rao 下界分析

由于启用 Wi-Fi 的设备的激增，众包被认为是构建大规模信号指纹射电图的有效且有前途的范例。然而，众包室内定位系统 (IPS) 必须处理各种设备以及接收信号强度 (RSS) 测量中固有的异质性。为了解决设备异构性问题，已经探索了差异指纹识别方法，这些方法减轻了导致来自不同商业设备的 RSS 报告不同的设备特性。在本文中，我们重点关注平均差分指纹 (MDF)，它通过从原始 RSS 指纹中减去所有接入点的平均 RSS 值来生成差分指纹。我们通过 Cramér-Rao 下界 (CRLB) 研究了 MDF 方法的定位性能，并通过分析表明它优于另一种解决设备多样性的方法。此外，我们使用由多个消费设备收集的真实 Wi-Fi RSS 数据集来评估现有解决方案的定位准确性。实验结果证实了我们的分析结果，并证明了 MDF 方法在减轻设备多样性方面的有效性，以及影响 RSS 读数的其他因素，包括 Wi-Fi 基础设施的设备携带模式和功率控制方案，从而有助于更广泛地采用众包 IPS。我们使用由多个消费设备收集的真实 Wi-Fi RSS 数据集来评估现有解决方案的定位准确性。实验结果证实了我们的分析结果，并证明了 MDF 方法在减轻设备多样性方面的有效性，以及影响 RSS 读数的其他因素，包括 Wi-Fi 基础设施的设备携带模式和功率控制方案，从而有助于更广泛地采用众包 IPS。我们使用由多个消费设备收集的真实 Wi-Fi RSS 数据集来评估现有解决方案的定位准确性。实验结果证实了我们的分析结果，并证明了 MDF 方法在减轻设备多样性方面的有效性，以及影响 RSS 读数的其他因素，包括 Wi-Fi 基础设施的设备携带模式和功率控制方案，从而有助于更广泛地采用众包 IPS。