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