Nuzzer: A Large-Scale Device-Free Passive Localization System for Wireless Environments

By Moustafa Seifeldin, Ahmed Saeed, Ahmed E. Kosba, Amr Fawzy El-Keyi, Moustafa Youssef. IEEE Transactions on Mobile Computing, 2009

375 citations recorded by the source corpus.

The widespread usage of WLANs and mobile devices has fostered the interest in localization systems for wireless environments. The majority of research in the context of wireless-based localization systems has focused on device-based active localization, in which devices are attached to tracked entities. Recently, device-free passive localization (DfP) has been proposed where the tracked entity is neither required to carry devices nor to participate actively in the localization process. Previous studies have focused on small areas and/or controlled environments. In this paper, we present the design, implementation, and analysis of Nuzzer, a large-scale DfP localization system, which tracks entities in real environments, rich in multipath. We first present probabilistic techniques for DfP localization of a single entity and evaluate their performance both analytically and in typical office buildings. Our results show that Nuzzer gives location estimates with less than 2-meters median distance error. We then give an algorithm for estimating the number of entities in an area of interest and localizing them into coarse-grained zones to enhance the scalability of the system. This indicates the suitability of Nuzzer to a large number of application domains.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/dbbafc1b3c8b8ba4308c025d7e5ab291b6ef00e0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Electrical and Electronic Engineering

In Ocean Engineering

Citations in the corpus

WiFall: Device-free fall detection by wireless networks (2017, methodology): “Nuzzer [27] is a representative work, which estimates the location of the target by comparing RSS to the fingerprint stored in a beforehand passive radio map.”
Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (2016, background): “However, some recent methods do not require the user to carry any device and are known as passive (device-free) localization [57]–[60].”
E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures (2014, background): “The state of art RTI requires tens or hundreds of wireless sensors to achieve sub-meter accuracy, while DfP localization [19,29] with four WiFi APs has an accuracy of several meters.”
Dynamic-MUSIC: accurate device-free indoor localization (2016, methodology): “Seifeldin et al. developed the Nuzzer system [30] using the RSSI signature as a ﬁngerprint.”
Capturing the human figure through a wall (2015, methodology): “…them to carry a wireless transmitter, e.g., [Adib et al. 2014; Joshi et al. 2015; Adib and Katabi 2013; Wang et al. 2014b; Pu et al. 2013; Abdelnasser et al. 2015; Chetty et al. 2012; Wilson and Patwari 2011; Youssef et al. 2007; Nan-nuru et al. 2013; Seifeldin et al. 2013; Adib et al. 2015].”
A Survey on Wireless Indoor Localization from the Device Perspective (2016, background): “WiFi + acoustic: Tarzia et al. [2011] present the idea of using an acoustic background spectrum for room-level indoor localization.”
3D Tracking via Body Radio Reflections (2014, background): “Other works on device-free localization rely on RSSI ﬁngerprints [32, 24], which are generated in a training phase by asking a person to stand in different locations throughout the area of interest.”
UbiBreathe: A Ubiquitous non-Invasive WiFi-based Breathing Estimator (2015, background): “…more detailed channel state information (CSI) in standard WiFi networks, have emerged as ubiquitous solutions for presence detection [1,4,10–12,18,20,34,36,40,44] , tracking individuals [19, 33, 35, 39] , recognition of different human activities [3,8,16,42], and gesture recognition systems [2,32].”