The Design and Implementation of a Wireless Location Estimation System in a Wireless Local Area Network

Shum, Kevin Chin Yiu; Ng, Joseph Kee‐Yin; Cheng, Quan Jia

doi:10.1109/waina.2012.169

Cited by 3 publications

(1 citation statement)

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“…The most common algorithm used to estimate the location computes the Euclidean distance between the sample-measured RSS vector and each fingerprint in the database. For indoor positioning systems, other advanced algorithms and techniques, such as neural networks (Chun-Yu, Lyu-Han, Gen-Huey, Wu, & Ming-Hui, 2012;Figuera, Rojo-Alvarez, Wilby, Mora-Jimenez, & Caamano, 2012;Mengual, Marban, & Elbe, 2010), probabilistic methods (Chai & Yang, 2007;Chung, Chen, & Tang, 2012;Kaemarungsi & Krishnamurthy, 2012;Shum, Ng, & Quan, 2012) or fuzzy logic (Abdelhadi & Anan, 2012) have been introduced to determine the relationship between the RSS samples and the location fingerprint on the radio map.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent location system in wireless networks

Mengual

Marbán

Eibe

et al. 2013

Expert Systems with Applications

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In this paper we propose a flexible Multi-Agent Architecture together with a methodology for indoor location which allows us to locate any mobile station (MS) such as a Laptop, Smartphone, Tablet or a robotic system in an indoor environment using wireless technology. Our technology is complementary to the GPS location finder as it allows us to locate a mobile system in a specific room on a specific floor using the Wi-Fi networks.The idea is that any MS will have an agent known at a Fuzzy Location Software Agent (FLSA) with a minimum capacity processing at its disposal which collects the power received at different Access Points distributed around the floor and establish its location on a plan of the floor of the building. In order to do so it will have to communicate with the Fuzzy Location Manager Software Agent (FLMSA). The FLMSAs are local agents that form part of the management infrastructure of the Wi-Fi network of the Organiza-tion.The FLMSA implements a location estimation methodology divided into three phases (measurement, calibration and estimation) for locating mobile stations (MS). Our solution is a fingerprint-based position-ing system that overcomes the problem of the relative effect of doors and walls on signal strength and is independent of the network device manufacturer.In the measurement phase, our system collects received signal strength indicator (RSSl) measurements from multiple access points. In the calibration phase, our system uses these measurements in a normal-ization process to create a radio map, a database of RSS patterns. Unlike traditional radio mapbased methods, our methodology normalizes RSS measurements collected at different locations on a floor. In the third phase, we use Fuzzy Controllers to locate an MS on the plan of the floor of a building.Experimental results demonstrate the accuracy of the proposed method. From these results it is clear that the system is highly likely to be able to locate an MS in a room or adjacent room.

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