MonoPHY: Mono-stream-based device-free WLAN localization via physical layer information

Abstract

Device-free (DF) indoor localization has grasped great attention recently as a value-added service to the already installed WiFi infrastructure as it allows the tracking of entities that do not carry any devices nor participate actively in the localization process. Current approaches, however, require a relatively large number of wireless streams, i.e. transmitterreceiver pairs, which is not available in many typical scenarios, such as home monitoring.In this paper, we introduce MonoPHY as an accurate monostream device-free WLAN localization system. MonoPHY leverages the physical layer information of WiFi networks supported by the IEEE 802.11n standard to provide accurate DF localization with only one stream. In particular, MonoPHY leverages both the low-level Channel State Information and the MIMO information to capture the human effect on signal strength. Experimental evaluation in a typical apartment, with a side-by-side comparison with the state-of-the-art, shows that MonoPHY can achieve an accuracy of 1.36m. This corresponds to at least 48% enhancement in median distance error over the state-of-the-art DF localization systems using a single stream only.Index Terms-Device-free localization, detection and tracking, physical-layer based localization.

Fingerprint-Based Device-Free Localization Performance in Changing Environments

“…Use of LDA has been tested for DFL in [11], [14], and [18], and is closely related to a Bayesian classifier with Gaussiandistributed observations such as used in [17]. It is a type of classifier that finds linear decision boundaries between classification regions that divide the input space [31].…”

“…We could conclude that, to minimize the degradation in localization performance over time, we simply need to gather data on multiple channels and process them together. Although the measurement now requires significantly more bandwidth, the result is motivation for use of devices that can measure CSI, which includes signal strength at each of multiple frequency channels (in addition to phase), such as reported in [17], [16]. Note that the eight channels we measure span 80 MHz of bandwidth, while WiFi channels at 2.4 GHz span only 20 MHz.…”

“…In fingerprint-based DFL, training data in the form of signal measurements of each link are gathered as a person walks to each of several predetermined locations in the network [11], [12], [13], [14], [15], [16], [17]. These measured "fingerprints" gathered during the training session are stored with the known (or even estimated [18]) coordinate of the person.…”

“…RSS is a nearly ubiquitous measurement available from standard transceivers. Recent work has shown that use of channel state information (CSI), i.e., the signal strength and phase of the received signal at multiple discrete frequencies, provides a higher dimensional measurement per link which can then improve DFL performance [16], [17]. Although we do not measure phase, this paper also studies measuring the signal strength at multiple discrete frequencies and its effect on DFL.…”

MagBoard: Magnetic-Based Ubiquitous Homomorphic Off-the-Shelf Keyboard

“…[22], recognize keystrokes based on that the electromagnetic emission from the electric circuits underneath different keys in a keyboard. WiFi-based approaches extend the concept of WiFi-sensing [1], [3] and device free localization [21], [8], [9], [14], [20], [2] and leverage the channel state information of the received MIMO WiFi system to detect the finger location [4]. All these systems suffer from occlusion; interference from other movement, humans, surrounding noises; and/or high energy consumption.…”