1,704 research outputs found
AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information
With expeditious development of wireless communications, location
fingerprinting (LF) has nurtured considerable indoor location based services
(ILBSs) in the field of Internet of Things (IoT). For most pattern-matching
based LF solutions, previous works either appeal to the simple received signal
strength (RSS), which suffers from dramatic performance degradation due to
sophisticated environmental dynamics, or rely on the fine-grained physical
layer channel state information (CSI), whose intricate structure leads to an
increased computational complexity. Meanwhile, the harsh indoor environment can
also breed similar radio signatures among certain predefined reference points
(RPs), which may be randomly distributed in the area of interest, thus mightily
tampering the location mapping accuracy. To work out these dilemmas, during the
offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI
amplitude as location fingerprint, which shares the structural simplicity of
RSS while reserving the most location-specific statistical channel information.
Moreover, an additional angle of arrival (AoA) fingerprint can be accurately
retrieved from CSI phase through an enhanced subspace based algorithm, which
serves to further eliminate the error-prone RP candidates. In the online phase,
by exploiting both CSI amplitude and phase information, a novel bivariate
kernel regression scheme is proposed to precisely infer the target's location.
Results from extensive indoor experiments validate the superior localization
performance of our proposed system over previous approaches
Stay Connected, Leave no Trace: Enhancing Security and Privacy in WiFi via Obfuscating Radiometric Fingerprints
The intrinsic hardware imperfection of WiFi chipsets manifests itself in the
transmitted signal, leading to a unique radiometric fingerprint. This
fingerprint can be used as an additional means of authentication to enhance
security. In fact, recent works propose practical fingerprinting solutions that
can be readily implemented in commercial-off-the-shelf devices. In this paper,
we prove analytically and experimentally that these solutions are highly
vulnerable to impersonation attacks. We also demonstrate that such a unique
device-based signature can be abused to violate privacy by tracking the user
device, and, as of today, users do not have any means to prevent such privacy
attacks other than turning off the device.
We propose RF-Veil, a radiometric fingerprinting solution that not only is
robust against impersonation attacks but also protects user privacy by
obfuscating the radiometric fingerprint of the transmitter for non-legitimate
receivers. Specifically, we introduce a randomized pattern of phase errors to
the transmitted signal such that only the intended receiver can extract the
original fingerprint of the transmitter. In a series of experiments and
analyses, we expose the vulnerability of adopting naive randomization to
statistical attacks and introduce countermeasures. Finally, we show the
efficacy of RF-Veil experimentally in protecting user privacy and enhancing
security. More importantly, our proposed solution allows communicating with
other devices, which do not employ RF-Veil.Comment: ACM Sigmetrics 2021 / In Proc. ACM Meas. Anal. Comput. Syst., Vol. 4,
3, Article 44 (December 2020
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