Sensing Motion Using Spectral and Spatial Analysis of WLAN RSSI

Abstract

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.

DECODE: Exploiting Shadow Fading to DEtect COMoving Wireless DEvices

“…Sohn et al [8] determine mobility from GSM traces using seven different metrics one of which is the variance in Signal Strength which is similar to our approach. Similarly, Muthukrishnan et al [9] discuss detecting mobility from RSSI in WLAN. LOCADIO [10] again used variance to detect mobility and combined it with a two state Hidden Markov Model (HMM) to eliminate oscillations between the static and mobile states.…”

“…Recall that DECODE focuses on periods of mobility because during these periods, it can observe correlated signal changes due to shadow fading, and during these periods it can filter out multipath fading. Several techniques have been proposed to detect mobility [8], [9], [10], [25]. Of these, we choose the straightforward signal strength variance threshold detection technique.…”

Inferring human activity using overcomplete dictionary based pattern recognition

“…In [8], a novel motion detection algorithm was proposed called Spectrally Spread Motion Detection (SpecSMD). This algorithm is able to classify user motion as either 'moving' or 'still' by examining the characteristics of the Received Signal Strength Indicator (RSSI) of a WiFi signal.…”

Prediction of near likely nodes in data-centric mobile wireless networks

“…To detect node mobility, [5] used received signal strength in wireless LAN to detect wireless device mobility. [6] determined mobility from GSM traces using different metrics.…”