Indoor proximity verification has become an increasingly useful primitive for the scenarios where access is granted to the previously unknown users when they enter a given area (e.g., a hotel room). Existing solutions either rely on homogeneous sensing modalities shared by two parties or require additional human interactions. In this article, we propose a context-based indoor proximity verification scheme, called SenCS, to enable real-time autonomous access for mobile devices, utilizing the available heterogeneous sensors at the user side and at the room side. The intuition is that only when the user is within a room can sensors from both sides observe the same events in the room. Yet such a solution is challenging, because the events may not provide enough entropy within the required time and the heterogeneity in sensing modalities may not always agree on the sensed events. To overcome the challenges, we exploit the time intervals between successively human actions to create heterogeneous contextual fingerprints (HCF) at a millisecond level. By comparing the contextual similarity between the HCF s from both the room and user sides, SenCS accomplishes the indoor proximity verification. Through proof-of-concept implementation and evaluations on 30 participants, SenCS achieves an accuracy of 99.77% and an equal error rate (EER) of 0.23% across various hardware configurations.

中文翻译：

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SenCS

室内邻近度验证已成为一种越来越有用的原语，用于在以前未知的用户进入给定区域（例如，酒店房间）时授予访问权限的场景。现有的解决方案要么依赖于两方共享的同质传感模式，要么需要额外的人工交互。在本文中，我们提出了一种基于上下文的室内邻近验证方案，称为 SenCS，利用用户侧和房间侧可用的异构传感器，实现移动设备的实时自主访问。直觉是，只有当用户在房间内时，双方的传感器才能观察到房间内的相同事件。然而，这样的解决方案具有挑战性，因为事件可能无法在所需时间内提供足够的熵，并且感知方式的异质性可能并不总是与感知到的事件一致。为了克服这些挑战，我们利用连续人类动作之间的时间间隔来创建毫秒级别的异构上下文指纹（HCF）。通过比较房间侧和用户侧的 HCF 之间的上下文相似性，SenCS 完成了室内邻近度验证。通过对 30 名参与者的概念验证实施和评估，SenCS 在各种硬件配置中实现了 99.77% 的准确率和 0.23% 的相等错误率 (EER)。我们利用连续人类动作之间的时间间隔来创建毫秒级的异构上下文指纹（HCF）。通过比较房间侧和用户侧的 HCF 之间的上下文相似性，SenCS 完成了室内邻近度验证。通过对 30 名参与者的概念验证实施和评估，SenCS 在各种硬件配置中实现了 99.77% 的准确率和 0.23% 的相等错误率 (EER)。我们利用连续人类动作之间的时间间隔来创建毫秒级的异构上下文指纹（HCF）。通过比较房间侧和用户侧的 HCF 之间的上下文相似性，SenCS 完成了室内邻近度验证。通过对 30 名参与者的概念验证实施和评估，SenCS 在各种硬件配置中实现了 99.77% 的准确率和 0.23% 的相等错误率 (EER)。