65 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
FarSense: pushing the range limit of WiFi-based respiration sensing with CSI ratio of two antennas
International audienceThe past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring. However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target. This sensing range constraint greatly limits the application of the proposed approaches in real life. This paper presents FarSense-the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair. FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment. We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing. The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range. Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100%. 1 We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications
A Channel State Information Based Virtual MAC Spoofing Detector
Physical layer security has attracted lots of attention with the expansion of wireless devices to the edge networks in recent years. Due to limited authentication mechanisms, MAC spoofing attack, also known as the identity attack, threatens wireless systems. In this paper, we study a new type of MAC spoofing attack, the virtual MAC spoofing attack, in a tight environment with strong spatial similarities, which can create multiple counterfeits entities powered by the virtualization technologies to interrupt regular services. We develop a system to effectively detect such virtual MAC spoofing attacks via the deep learning method as a countermeasure. A deep convolutional neural network is constructed to analyze signal level information extracted from Channel State Information (CSI) between the communication peers to provide additional authentication protection at the physical layer. A significant merit of the proposed detection system is that this system can distinguish two different devices even at the same location, which was not well addressed by the existing approaches. Our extensive experimental results demonstrate the effectiveness of the system with an average detection accuracy of 95%, even when devices are co-located
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
CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human Presence Detection using Wi-Fi CSI
In recent years, the demand for pervasive smart services and applications has
increased rapidly. Device-free human detection through sensors or cameras has
been widely adopted, but it comes with privacy issues as well as misdetection
for motionless people. To address these drawbacks, channel state information
(CSI) captured from commercialized Wi-Fi devices provides rich signal features
for accurate detection. However, existing systems suffer from inaccurate
classification under a non-line-of-sight (NLoS) and stationary scenario, such
as when a person is standing still in a room corner. In this work, we propose a
system called CRONOS (Colorization and Contrastive Learning Enhanced NLoS Human
Presence Detection), which generates dynamic recurrence plots (RPs) and
color-coded CSI ratios to distinguish mobile people from vacancy in a room,
respectively. We also incorporate supervised contrastive learning to retrieve
substantial representations, where consultation loss is formulated to
differentiate the representative distances between dynamic and stationary
cases. Furthermore, we propose a self-switched static feature enhanced
classifier (S3FEC) to determine the utilization of either RPs or color-coded
CSI ratios. Our comprehensive experimental results show that CRONOS outperforms
existing systems that apply machine learning, non-learning based methods, as
well as non-CSI based features in open literature. CRONOS achieves the highest
presence detection accuracy in vacancy, mobility, line-of-sight (LoS), and NLoS
scenarios
Contact-Free Multitarget Tracking Using Distributed Massive MIMO-OFDM Communication System:Prototype and Analysis
Wireless-based human activity recognition has become an essential technology that enables contact-free human-machine and human-environment interactions. In this article, we consider contact-free multitarget tracking (MTT) based on available communication systems. A radar-like prototype is built upon a sub-6-GHz distributed massive multiple-input and multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication system. Specifically, the raw channel state information (CSI) is calibrated in the frequency and antenna domain before being used for tracking. Then, the targeted CSIs reflected or scattered from the moving pedestrians are extracted. To evade the complex association problem of distributed massive MIMO-based MTT, we propose to use a complex Bayesian compressive sensing (CBCS) algorithm to estimate the targets' locations based on the extracted target-of-interest CSI signal directly. The estimated locations from CBCS are fed to a Gaussian mixture probability hypothesis density (GM-PHD) filter for tracking. A multipedestrian tracking experiment is conducted in a room with a size of 6.5 Ă— 10 m to evaluate the performance of the proposed algorithm. According to the experimental results, we achieve 75th and 95th percentile accuracy of 12.7 and 18.2 cm for single-person tracking and 28.9 and 45.7 cm for multiperson tracking, respectively. Furthermore, the proposed algorithm achieves tracking purposes in real time, which is promising for practical MTT use cases.</p
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