SenCS
Autor: | Bin Wang, Wenyuan Xu, Chaohao Li, Xiaoyu Ji, Wang Kai |
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Rok vydání: | 2021 |
Předmět: |
Scheme (programming language)
021110 strategic defence & security studies Modalities Exploit Computer Networks and Communications Computer science Real-time computing 0211 other engineering and technologies Word error rate 020206 networking & telecommunications Context (language use) 02 engineering and technology 0202 electrical engineering electronic engineering information engineering Entropy (information theory) computer Mobile device Wireless sensor network computer.programming_language |
Zdroj: | ACM Transactions on Sensor Networks. 17:1-22 |
ISSN: | 1550-4867 1550-4859 |
Popis: | 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. |
Databáze: | OpenAIRE |
Externí odkaz: |