11,383 research outputs found
Sub-Nanosecond Time of Flight on Commercial Wi-Fi Cards
Time-of-flight, i.e., the time incurred by a signal to travel from
transmitter to receiver, is perhaps the most intuitive way to measure distances
using wireless signals. It is used in major positioning systems such as GPS,
RADAR, and SONAR. However, attempts at using time-of-flight for indoor
localization have failed to deliver acceptable accuracy due to fundamental
limitations in measuring time on Wi-Fi and other RF consumer technologies.
While the research community has developed alternatives for RF-based indoor
localization that do not require time-of-flight, those approaches have their
own limitations that hamper their use in practice. In particular, many existing
approaches need receivers with large antenna arrays while commercial Wi-Fi
nodes have two or three antennas. Other systems require fingerprinting the
environment to create signal maps. More fundamentally, none of these methods
support indoor positioning between a pair of Wi-Fi devices
without~third~party~support.
In this paper, we present a set of algorithms that measure the time-of-flight
to sub-nanosecond accuracy on commercial Wi-Fi cards. We implement these
algorithms and demonstrate a system that achieves accurate device-to-device
localization, i.e. enables a pair of Wi-Fi devices to locate each other without
any support from the infrastructure, not even the location of the access
points.Comment: 14 page
H2B: Heartbeat-based Secret Key Generation Using Piezo Vibration Sensors
We present Heartbeats-2-Bits (H2B), which is a system for securely pairing
wearable devices by generating a shared secret key from the skin vibrations
caused by heartbeat. This work is motivated by potential power saving
opportunity arising from the fact that heartbeat intervals can be detected
energy-efficiently using inexpensive and power-efficient piezo sensors, which
obviates the need to employ complex heartbeat monitors such as
Electrocardiogram or Photoplethysmogram. Indeed, our experiments show that
piezo sensors can measure heartbeat intervals on many different body locations
including chest, wrist, waist, neck and ankle. Unfortunately, we also discover
that the heartbeat interval signal captured by piezo vibration sensors has low
Signal-to-Noise Ratio (SNR) because they are not designed as precision
heartbeat monitors, which becomes the key challenge for H2B. To overcome this
problem, we first apply a quantile function-based quantization method to fully
extract the useful entropy from the noisy piezo measurements. We then propose a
novel Compressive Sensing-based reconciliation method to correct the high bit
mismatch rates between the two independently generated keys caused by low SNR.
We prototype H2B using off-the-shelf piezo sensors and evaluate its performance
on a dataset collected from different body positions of 23 participants. Our
results show that H2B has an overwhelming pairing success rate of 95.6%. We
also analyze and demonstrate H2B's robustness against three types of attacks.
Finally, our power measurements show that H2B is very power-efficient
Sensing motion using spectral and spatial analysis of WLAN RSSI
In this paper we present how motion sensing can be obtained just by observing the WLAN radio signal strength and its fluctuations. The temporal, spectral and spatial characteristics of WLAN signal are analyzed. Our analysis
confirms our claim that âsignal strength from access points appear to jump around more vigorously when the device is moving compared to when it is still and the number of detectable access points vary considerably while the user is on the moveâ. Using this observation, we present a novel motion detection algorithm, Spectrally Spread Motion Detection (SpecSMD) based on the spectral analysis of
WLAN signalâs RSSI. To benchmark the proposed algorithm, we used Spatially Spread Motion Detection (SpatSMD), which is inspired by the recent work of Sohn et al. Both algorithms were evaluated by carrying out extensive measurements
in a diverse set of conditions (indoors in different buildings and outdoors - city center, parking lot, university campus etc.,) and tested against the same
data sets. The 94% average classification accuracy of the proposed SpecSMD is outperforming the accuracy of SpatSMD (accuracy 87%). The motion detection algorithms presented in this paper provide ubiquitous methods for deriving the
state of the user. The algorithms can be implemented and run on a commodity device with WLAN capability without the need of any additional hardware support
Development of an intelligent object for grasp and manipulation research
KĂľiva R, Haschke R, Ritter H. Development of an intelligent object for grasp and manipulation research. Presented at the ICAR 2011, Tallinn, Estonia.In this paper we introduce a novel device, called iObject, which is equipped with tactile and motion tracking sensors that allow for the evaluation of human and robot grasping and manipulation actions. Contact location and contact force, object acceleration in space (6D) and orientation relative to the earth (3D magnetometer) are measured and transmitted wirelessly over a Bluetooth connection. By allowing human-human, human-robot and robot-robot comparisons to be made, iObject is a versatile tool for studying manual interaction.
To demonstrate the efficiency and flexibility of iObject for the study of bimanual interactions, we report on a physiological experiment and evaluate the main parameters of the considered dual-handed manipulation task
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