Device-Free Indoor Localization Based on ... - Semantic Scholar
International Journal of Ambient Computing and Intelligence, 6(1), 35-44, January-March 2014 35
Device-Free Indoor Localization Based on Ambient FM Radio Signals Andrei Popleteev, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg, Luxembourg Thomas Engel, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg, Luxembourg
ABSTRACT This paper presents the concept of device-free indoor localization using only a passive receiver and ambient FM radio signals. Experimental results based on empirical measurements demonstrate the feasibility of the proposed approach. The authors also evaluate fine-grained localization performance of the system, its temporal stability, and highlight the role of frequency diversity for passive localization. Keywords:
Ambient Radio, Context Awareness, Device-Free Positioning, Indoor Localization, Smart Environment
INTRODUCTION A large body of research has been dedicated to localization of mobile devices. However, in certain situations the user does not (or cannot) carry any device (for example, smart environments, assisted daily living). Device-free localization (DFL) addresses the challenge of locating the user without any wearable devices. The existing DFL techniques have a number of limitations which prevent their wide adoption. In particular, computer vision methods require installation of multiple cameras, which need particular light conditions, infringe users’ privacy and cause psychological discomfort. Another DFL technology, pressure-sensitive floors, while preserving user privacy, have prohibitive cost
of deployment. Radio-based DFL methods, in turn, are based on effects of radio wave interactions with human body (such as diffraction, reflection, scattering), which ultimately result in measurable changes of signal properties (Scholz et al., 2011). These methods typically require low-cost hardware, allow hidden installation, and cannot violate users’ privacy as vision-based systems do. State-of-the-art radio-based DFL systems require deployment of multiple wireless devices which actively transmit, receive and analyse radio signals (Wilson & Patwari, 2011; Scholz et al., 2011; Kosba et al., 2012). Due to radio spectrum regulations, these devices are typically restricted to use only one or few frequency channels in a narrow license-free band. This paper investigates feasibility of DFL
DOI: 10.4018/ijaci.2014010103 Copyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
36 International Journal of Ambient Computing and Intelligence, 6(1), 35-44, January-March 2014
with a single indoor receiver employing ambient FM radio stations. In contrast to related work, the proposed system is completely passive (since it uses ambient transmitters) and monitors multiple radio channels simultaneously. Multi-frequency scanning is the key feature of the system; in this paper, several FM radio stations are monitored simultaneously with a software-defined radio (rtl, 2013). Experimental results demonstrate that channel diversity can considerably improve localization performance and thus enable sub-room level localization with ambient stations. The following sections review related work, introduce the proposed approach, and present experimental results for coarsegrained and fine-grained localization. The paper concludes with a summary of findings.
BACKGROUND Indoor localization using radio signals is a well-established research area with two dominant directions: device-based and device-free localization. In device-based approach, users carry mobile devices which transmit and/or receive radio signals from stationary transceivers (installed either indoors or outdoors). Received signal properties depend on location of the mobile device and thus by analyzing signal’s characteristics (typically the received signal strength indicator, RSSI) it is possible to localize the device. Previous works explored the use of various radio technologies, such as Wi-Fi (Bahl & Padmanabhan, 2000; Youssef & Agrawala, 2005), DECT (Kranz et al., 2010), GSM (Varshavsky et al., 2007) and FM radio (Popleteev et al., 2012; Matic et al., 2010). One of the key requirements/assumptions of device-based localization is that the user carries a mobile device. Unfortunately, this assumption does not always hold (Patel et al., 2006). Moreover, device-based localization is often not feasible at all, particularly in elderly care and assisted daily living scenarios. Device-free positioning approach, in contrast, aims to localize the user himself/herself. Traditional devicefree methods usually employ a set of cameras
and computer vision algorithms to track user location within the target environment. Apart from high computational requirements, visionbased tracking has serious privacy implications which limit its applicability in many scenarios. Radio-based methods, in turn, provide localization in any lighting conditions and without compromising users’ identity or fine details of their appearance. The concept of device-free radio-based localization has been introduced in (Youssef et al., 2007). In their further work, Youssef and colleagues explored device-free positioning with Wi-Fi radio signals, using both empirical (Kosba et al., 2012) and simulation approaches (Aly & Youssef, 2013). The authors’ approach leverages the fact that human presence in line-of-sight (LOS) between receiver and transmitter results in increased RSSI variance, which makes it possible to localize the user as he/she crosses the LOS between devices deployed in the environment. Due to the LOS shadowing, wall-mounted access points provide higher localization performance than ceilingmounted ones. In comparison between the two Wi-Fi bands, 2.4 GHz and 5.7 GHz, the latter has been found to have lower RSS variance different locations and hence lower positioning accuracy in a DFL system. Attenuation of radio signals by human body on LOS between transceivers is also employed in the radio tomographic imaging (RTI) approach (Wilson & Patwari, 2012; Wilson & Patwari, 2011; Kaltiokallio et al., 2012b; Patwari & Wilson, 2011; Zhao & Patwari, 2011). In RTI systems, an array of wireless nodes is installed around and inside the target area. By monitoring RSSI statistics for each pair of nodes, it is possible to detect human presence on LOS path, and by increasing the number of nodes, it is possible to increase localization accuracy and track multiple users (Wilson & Patwari, 2012). Besides localization, radio-based sensing is also employed for devicefree activity recognition (DFAR) (Scholz et al., 2011). In Sigg et al. (2013), an active DFAR system comprising one transmitter and two receivers could recognize five activities, such as walking, standing, lying, crawling, with up to 72% accuracy. Recently, a device-free gesture
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This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Computer Science, Security, and Information Technology. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2
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Device-Free Indoor Localization Based on Ambient FM Radio Signals Andrei Popleteev, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg, Luxembourg Thomas Engel, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg, Luxembourg
ABSTRACT This paper presents the concept of device-free indoor localization using only a passive receiver and ambient FM radio signals. Experimental results based on empirical measurements demonstrate the feasibility of the proposed approach. The authors also evaluate fine-grained localization performance of the system, its temporal stability, and highlight the role of frequency diversity for passive localization. Keywords:
Ambient Radio, Context Awareness, Device-Free Positioning, Indoor Localization, Smart Environment
INTRODUCTION A large body of research has been dedicated to localization of mobile devices. However, in certain situations the user does not (or cannot) carry any device (for example, smart environments, assisted daily living). Device-free localization (DFL) addresses the challenge of locating the user without any wearable devices. The existing DFL techniques have a number of limitations which prevent their wide adoption. In particular, computer vision methods require installation of multiple cameras, which need particular light conditions, infringe users’ privacy and cause psychological discomfort. Another DFL technology, pressure-sensitive floors, while preserving user privacy, have prohibitive cost
of deployment. Radio-based DFL methods, in turn, are based on effects of radio wave interactions with human body (such as diffraction, reflection, scattering), which ultimately result in measurable changes of signal properties (Scholz et al., 2011). These methods typically require low-cost hardware, allow hidden installation, and cannot violate users’ privacy as vision-based systems do. State-of-the-art radio-based DFL systems require deployment of multiple wireless devices which actively transmit, receive and analyse radio signals (Wilson & Patwari, 2011; Scholz et al., 2011; Kosba et al., 2012). Due to radio spectrum regulations, these devices are typically restricted to use only one or few frequency channels in a narrow license-free band. This paper investigates feasibility of DFL
DOI: 10.4018/ijaci.2014010103 Copyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
36 International Journal of Ambient Computing and Intelligence, 6(1), 35-44, January-March 2014
with a single indoor receiver employing ambient FM radio stations. In contrast to related work, the proposed system is completely passive (since it uses ambient transmitters) and monitors multiple radio channels simultaneously. Multi-frequency scanning is the key feature of the system; in this paper, several FM radio stations are monitored simultaneously with a software-defined radio (rtl, 2013). Experimental results demonstrate that channel diversity can considerably improve localization performance and thus enable sub-room level localization with ambient stations. The following sections review related work, introduce the proposed approach, and present experimental results for coarsegrained and fine-grained localization. The paper concludes with a summary of findings.
BACKGROUND Indoor localization using radio signals is a well-established research area with two dominant directions: device-based and device-free localization. In device-based approach, users carry mobile devices which transmit and/or receive radio signals from stationary transceivers (installed either indoors or outdoors). Received signal properties depend on location of the mobile device and thus by analyzing signal’s characteristics (typically the received signal strength indicator, RSSI) it is possible to localize the device. Previous works explored the use of various radio technologies, such as Wi-Fi (Bahl & Padmanabhan, 2000; Youssef & Agrawala, 2005), DECT (Kranz et al., 2010), GSM (Varshavsky et al., 2007) and FM radio (Popleteev et al., 2012; Matic et al., 2010). One of the key requirements/assumptions of device-based localization is that the user carries a mobile device. Unfortunately, this assumption does not always hold (Patel et al., 2006). Moreover, device-based localization is often not feasible at all, particularly in elderly care and assisted daily living scenarios. Device-free positioning approach, in contrast, aims to localize the user himself/herself. Traditional devicefree methods usually employ a set of cameras
and computer vision algorithms to track user location within the target environment. Apart from high computational requirements, visionbased tracking has serious privacy implications which limit its applicability in many scenarios. Radio-based methods, in turn, provide localization in any lighting conditions and without compromising users’ identity or fine details of their appearance. The concept of device-free radio-based localization has been introduced in (Youssef et al., 2007). In their further work, Youssef and colleagues explored device-free positioning with Wi-Fi radio signals, using both empirical (Kosba et al., 2012) and simulation approaches (Aly & Youssef, 2013). The authors’ approach leverages the fact that human presence in line-of-sight (LOS) between receiver and transmitter results in increased RSSI variance, which makes it possible to localize the user as he/she crosses the LOS between devices deployed in the environment. Due to the LOS shadowing, wall-mounted access points provide higher localization performance than ceilingmounted ones. In comparison between the two Wi-Fi bands, 2.4 GHz and 5.7 GHz, the latter has been found to have lower RSS variance different locations and hence lower positioning accuracy in a DFL system. Attenuation of radio signals by human body on LOS between transceivers is also employed in the radio tomographic imaging (RTI) approach (Wilson & Patwari, 2012; Wilson & Patwari, 2011; Kaltiokallio et al., 2012b; Patwari & Wilson, 2011; Zhao & Patwari, 2011). In RTI systems, an array of wireless nodes is installed around and inside the target area. By monitoring RSSI statistics for each pair of nodes, it is possible to detect human presence on LOS path, and by increasing the number of nodes, it is possible to increase localization accuracy and track multiple users (Wilson & Patwari, 2012). Besides localization, radio-based sensing is also employed for devicefree activity recognition (DFAR) (Scholz et al., 2011). In Sigg et al. (2013), an active DFAR system comprising one transmitter and two receivers could recognize five activities, such as walking, standing, lying, crawling, with up to 72% accuracy. Recently, a device-free gesture
Copyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
8 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/device-free-indoor-localizationbased-on-ambient-fm-radio-signals/109627?camid=4v1
This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Computer Science, Security, and Information Technology. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2
Related Content Soft Computing Paradigms and Regression Trees in Decision Support Systems Cong Tran, Ajith Abraham and Lakhmi Jain (2008). Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications (pp. 1011-1035).
www.igi-global.com/chapter/soft-computing-paradigms-regressiontrees/24328?camid=4v1a Enjoy.IT!: A Platform to Integrate Entertainment Services M. Amparo Navarro-Salvador, Ana Belén Sánchez-Calzón, Carlos Fernández-Llatas and Teresa Meneu (2011). International Journal of Ambient Computing and Intelligence (pp. 1-8).
www.igi-global.com/article/enjoy-platform-integrate-entertainmentservices/61136?camid=4v1a Biometric Security Technology Marcos Faundez-Zanuy (2009). Encyclopedia of Artificial Intelligence (pp. 262-269).
www.igi-global.com/chapter/biometric-securitytechnology/10258?camid=4v1a
On Improving the Visibility of Hard-Measurable Process Performance Tim Pidun and Carsten Felden (2012). International Journal of Intelligent Information Technologies (pp. 59-74).
www.igi-global.com/article/improving-visibility-hard-measurableprocess/66872?camid=4v1a
