With the increasing demand for edge device powered location-based services in indoor environments, Wi-Fi received signal strength (RSS) fingerprinting has become popular, given the unavailability of GPS indoors. However, achieving robust and efficient indoor localization faces several challenges, due to RSS fluctuations from dynamic changes in indoor environments and heterogeneity of edge devices, leading to diminished localization accuracy. While advances in machine learning (ML) have shown promise in mitigating these phenomena, it remains an open problem. Additionally, emerging threats from adversarial attacks on ML-enhanced indoor localization systems, especially those introduced by malicious or rogue access points (APs), can deceive ML models to further increase localization errors. To address these challenges, we present SENTINEL, a novel embedded ML framework utilizing modified capsule neural networks to bolster the resilience of indoor localization solutions against adversarial attacks, device heterogeneity, and dynamic RSS fluctuations. We also introduce RSSRogueLoc, a novel dataset capturing the effects of rogue APs from several real-world indoor environments. Experimental evaluations demonstrate that SENTINEL achieves significant improvements, with up to 3.5x reduction in mean error and 3.4x reduction in worst-case error compared to state-of-the-art frameworks using simulated adversarial attacks. SENTINEL also achieves improvements of up to 2.8x in mean error and 2.7x in worst-case error compared to state-of-the-art frameworks when evaluated with the real-world RSSRogueLoc dataset.

翻译：随着室内环境中基于边缘设备的位置服务需求日益增长，鉴于GPS在室内不可用，基于Wi-Fi接收信号强度（RSS）指纹的定位技术已变得流行。然而，由于室内环境动态变化导致的RSS波动以及边缘设备的异构性，实现鲁棒且高效的室内定位面临诸多挑战，导致定位精度下降。尽管机器学习（ML）的进步在缓解这些现象方面显示出潜力，但这仍是一个悬而未决的问题。此外，针对ML增强型室内定位系统的对抗性攻击（尤其是由恶意或流氓接入点（AP）引入的攻击）所构成的新兴威胁，可能欺骗ML模型，从而进一步增加定位误差。为应对这些挑战，我们提出了SENTINEL，一种新颖的嵌入式ML框架，它利用改进的胶囊神经网络来增强室内定位解决方案对抗对抗性攻击、设备异构性和动态RSS波动的韧性。我们还引入了RSSRogueLoc，这是一个捕获了多个真实世界室内环境中流氓AP影响的新数据集。实验评估表明，与使用模拟对抗性攻击的最先进框架相比，SENTINEL实现了显著改进，平均误差最多降低了3.5倍，最坏情况误差最多降低了3.4倍。在使用真实世界RSSRogueLoc数据集进行评估时，与最先进框架相比，SENTINEL在平均误差和最坏情况误差上也分别实现了最多2.8倍和2.7倍的改进。