3,139 research outputs found
WiPal: Efficient Offline Merging of IEEE 802.11 Traces
Merging wireless traces is a fundamental step in measurement-based studies
involving multiple packet sniffers. Existing merging tools either require a
wired infrastructure or are limited in their usability. We propose WiPal, an
offline merging tool for IEEE 802.11 traces that has been designed to be
efficient and simple to use. WiPal is flexible in the sense that it does not
require any specific services, neither from monitors (like synchronization,
access to a wired network, or embedding specific software) nor from its
software environment (e.g. an SQL server). We present WiPal's operation and
show how its features - notably, its modular design - improve both ease of use
and efficiency. Experiments on real traces show that WiPal is an order of
magnitude faster than other tools providing the same features. To our
knowledge, WiPal is the only offline trace merger that can be used by the
research community in a straightforward fashion.Comment: 6 page
Inferring Person-to-person Proximity Using WiFi Signals
Today's societies are enveloped in an ever-growing telecommunication
infrastructure. This infrastructure offers important opportunities for sensing
and recording a multitude of human behaviors. Human mobility patterns are a
prominent example of such a behavior which has been studied based on cell phone
towers, Bluetooth beacons, and WiFi networks as proxies for location. However,
while mobility is an important aspect of human behavior, understanding complex
social systems requires studying not only the movement of individuals, but also
their interactions. Sensing social interactions on a large scale is a technical
challenge and many commonly used approaches---including RFID badges or
Bluetooth scanning---offer only limited scalability. Here we show that it is
possible, in a scalable and robust way, to accurately infer person-to-person
physical proximity from the lists of WiFi access points measured by smartphones
carried by the two individuals. Based on a longitudinal dataset of
approximately 800 participants with ground-truth interactions collected over a
year, we show that our model performs better than the current state-of-the-art.
Our results demonstrate the value of WiFi signals in social sensing as well as
potential threats to privacy that they imply
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 ’signal 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 move’. Using this observation, we present a novel motion detection algorithm, Spectrally Spread Motion Detection (SpecSMD) based on the spectral analysis of
WLAN signal’s 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
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