An Analysis of Device-Free and Device-Based WiFi-Localization Systems

An Analysis of Device-Free and Device-Based WiFi-Localization Systems

Heba Aly (Department of Computer and Systems Engineering, Alexandria University, Alexandria, Egypt) and Moustafa Youssef (Wireless Research Center, Egypt-Japan University of Science and Technology and Alexandria University, Alexandria, Egypt)
Copyright: © 2014 |Pages: 19
DOI: 10.4018/ijaci.2014010101
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WiFi-based localization became one of the main indoor localization techniques due to the ubiquity of WiFi connectivity. However, indoor environments exhibit complex wireless propagation characteristics. Typically, these characteristics are captured by constructing a fingerprint map for the different locations in the area of interest. This finger print requires significant overhead in manual construction, and thus has been one of the major drawbacks of WiFi-based localization. In this paper, the authors present an automated tool for finger print constructions and leverage it to study novel scenarios for device-based and device-free WiFi-based localization that are difficult to evaluate in a real environment. In a particular, the authors examine the effect of changing the access points (AP) mounting location, AP technology upgrade, crowd effect on calibration and operation, among others; on the accuracy of the localization system. The authors present the analysis for the two classes of WiFi-based localization: device-based and device-free. The authors analysis highlights factors affecting the localization system accuracy, how to tune it for better localization, and provides insights for both researchers and practitioners.
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Due to recent advances in wireless networking, WiFi-based localization has been attracting significant attention. WiFi deployments are ubiquitous in public and private places including offices, malls, and hospitals.

WiFi-based localization techniques use the existing ubiquitous WLANs to provide accurate indoor localization without any additional hardware. It can be classified into two categories: device-based (Bahl & Padmanabhan, 2000; Youssef & Agrawala, 2005), and device-free techniques (Youssef, Mah, & Agrawala, 2007; M. Seifeldin & Youssef, 2009; Kosba, Saeed, & Youssef, 2012a; M. A. Seifeldin, El-keyi, & Youssef, 2011). Device-based systems track a WiFi-enabled device such as a cell-phone, based on the received signal strength (RSS) at this device; while device-free systems track entities that do not carry any devices based on their effect on the RSS at the infrastructure devices. A typical device-free system will consist of one or more signal receivers which are called monitoring points (MPs) such as laptops; signal transmitters such as access points (APs); and also an application server, which is usually one of the monitoring points, to collect data from monitoring points. Applications for the device-free systems include intrusion detection, smart homes, and sensor-less sensing. To overcome the complex propagation characteristics of WiFi signals in indoor environments (Youssef & Agrawala, 2003), typically both device-based and device-free systems require a calibration phase to construct a fingerprint or a radio-map that stores the RSS characteristics at different locations in the area of interest. Device-based systems use active-radio maps, where each stream represents the signal strength from an AP to the tracked device; while device-free systems use passive-radio maps, where each stream represents the signal strength from an AP to a MP capturing the effect of the tracked entity on the fixed streams. Figure 1 explains the difference between active and passive radio maps construction.

Figure 1.

Difference between device-based ((a) Active) and device-free ((b) Passive) radio-map construction


Traditional methods of radio-map construction require the use of manual calibration, which is a tedious and a time consuming process. Therefore radio map construction has been based on simple scenarios, usually involving one entity in a specific environment.

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