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2010 Indoor Positioning and Indoor Navigation
 
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WLAN RSS (Signal Strength Based Methods), Fingerprinting

This session encompasses:

Session Chair: Prof. François Spies, University of Franche-Comte

Abstracts


PDF version of extended abstracts

Oral Presentations

Wednesday, September 15
Auditorium G7

16:00 - 16:30 Sebastian Gansemer, Uwe Grossmann (presenting author): RSSI-based Euclidean Distance Algorithm for Indoor Positioning adapted for the use in dynamically changing WLAN environments and multi-level buildings
This paper presents a fingerprinting positioning algorithm for WLAN environments based on Euclidean Distance (EDA). The adapted algorithm can be used in large and dynamically changing environments and multi-level buildings. Evaluation results show a reduction of median location estimation error (LEE) from 12m using standard EDA to 2.12m when the adapted EDA is used. The discrete vertical z-coordinate could be estimated correctly in 97.45% of cases. Moreover, it is shown that the calibration effort can be reduced clearly by using larger calibration grids with an acceptable increase of LEE.
   
16:30 - 17:00 Laura Koski (presenting author at TUT), Tommi Perälä and Robert Piché: Indoor Positioning Using WLAN Coverage Area Estimates
This paper introduces a novel method for positioning using coverage area estimates of wireless communication nodes. The coverage areas are estimated in a Bayesian inference framework using location fingerprints that are collected in an offline calibration phase, and the estimated coverage areas are stored in a database. In the online positioning phase the coverage area estimates of the heard communication nodes are used as measurements of the position of the mobile terminal (MT). One way to improve the position estimate is to use filtering, which enables the use of past measurements in the computation of the current position. Also, in indoor positioning the floor plan is an obvious way to enhance the positioning accuracy. We combine the floor plan information with coverage area –based positioning. It is studied how the floor plan improves the positioning accuracy in coverage area based positioning. Performance of KF, GMF and PF is studied with a real WLAN measurement data.
   
17:00 - 17:30 Rene Hansen (presenting author, at Aalborg University), Rico Wind, Christian S. Jensen, Bent Thomsen: Algorithmic Strategies for Adapting to Environmental Changes in 802.11 Location Fingerprinting
This paper presents novel algorithmic strategies for adapting to environmental changes in 802.11 location fingerprinting. A long-standing challenge in location fingerprinting has been that dynamic changes, such as people presence, opening/closing of doors, or changing humidity levels, may influence the 802.11 signal strengths to an extent where a static radio map is rendered useless. To counter this effect, related research efforts have proposed to install additional sensors in order to adapt a previously built radio map to the circumstances at a given time. Although effective, this is not a viable solution for ubiquitous positioning where localization is required in many different buildings. Instead, we propose algorithmic strategies for dealing with changing environmental dynamics. We have performed an evaluation of our algorithms on signal strength data collected over a two month period at Aalborg University. The results show a vast improvement over using traditional static radio maps.
   
17:30 - 18:00 Christos Laoudias (presenting author), Michalis P. Michaelides, Christos G. Panayiotou: Fault Tolerant Positioning using WLAN Signal Strength Fingerprints
In this extended abstract we present our ongoing research on WLAN positioning. Our focus is on the fault tolerance of positioning methods, rather than the absolute accuracy in case of no faults. We introduce several fault models to capture the effect of AP malfunctions or malicious attacks during positioning and describe how these models can be applied in practice. We compare some well-known algorithms in terms of fault tolerance and present preliminary experimental results on their accuracy degradation as the percentage of faulty APs increases.

WLAN RSS (Signal Strength Based Methods), Fingerprinting, Part 2

Session Chair: Marc Ciurana, CTAE - Aerospace Research & Technology Centre (Barcelona).

Thursday, September 16
Auditorium G7

08:15 - 08:45 Jakhongir Narzullaev (presenting author), Anvar Narzullaev, Yongwan Park, Kook-Yeol Yoo: Implementation of Hyperbolic Location Estimation Using RSSI in WLANs
As the deployment of Wireless Local Area Networks (WLAN) in dense-urban areas is growing rapidly, it can be a perfect supplement for providing location information of users in indoor environments and metropolitan areas, where other positioning techniques such as GPS, are not much effective. In this study, we propose a new WLAN positioning method that combines Received Signal Strength Indication (RSSI) fingerprinting and Time Difference-of-Arrival (TDOA) positioning techniques, which will provide reliable location accuracy and does not require any additional changes on actual WLAN infrastructure.
   
08:45 - 09:15 Kamran Sayrafian: A Perspective on Robustness and Deployment Complexity for RSS-based Indoor Positioning
There are two major issues with practical deployment of RSS-based indoor positioning systems. These issues are coverage design (or equivalently reference node placement strategy) and development of the measurement-based radio-map. In this research, each problem is described and possible techniques that can simplify each problem are suggested. In the process, it shown that there is an elegant trade-off between these issues where simpler coverage design could lead to a higher complexity radio-map and vice versa. Various experiments and simulations are provided to demonstrate the results.
   
09:15 - 09:30 Thorsten Vaupel (presenting author, at Fraunhofer IIS), Jochen Seitz, Frédéric Kiefer, Stephan Haimerl, Jörn Thielecke: Wi-Fi Positioning: System Considerations and Device Calibration
Because of an increasing number of public and private access points in indoor and urban environments, Wi-Fi® positioning becomes more and more attractive for pedestrian navigation. Different approaches and solutions have been developed in the last ten years. In this article influences of the surrounding environment, the Wi-Fi infrastructure and hardware characteristics are presented and evaluated with a focus on the so called Wi-Fi fingerprinting technique for positioning. As a testbed for positioning the metropolitan area of Nuremberg, Fürth and Erlangen is used. The comprehensive database contains about 50,000 fingerprints and about 62,000 unique access points have been observed. From the results a classification of characteristics of mobile devices is derived and a calibration approach for Wi-Fi devices is presented. Finally conclusions are drawn.
   
09:30 - 09:45 I-En Liao (presenting author), Kuo-Fong Kao, Jia-Siang Lyu: An Indoor Location Based Service Using Access Points as Signal Strength Data Collectors
WLAN location determination algorithms can be classified into client-based approach and infrastructure-based approach. Unlike the other infrastructure-based algorithms, we proposed a calibration-free infrastructure-based indoor location determination algorithm using access points as signal strength data collectors. In the proposed system, each access point runs OpenWrt, Kismet, and MySQL for collecting signal strength data from other access points and mobile devices. The location server builds a RSSI vs. Distance model based on inter-APs RSSI measurements and then predicts the location of mobile device based on the received signal strength measurements of all access points from the target mobile device. A location based service which provides timely class notes in a university environment is also presented in this paper to show the possible applications of the proposed technique.

WLAN RSS (Signal Strength Based Methods), Fingerprinting, Part 3

Session Chair: Marc Ciurana, CTAE - Aerospace Research & Technology Centre (Barcelona).

10:15 - 10:45 Matteo Cypriani (presenting author), Philippe Canalda, Frédéric Lassabe, François Spies: Wi-Fi-Based Indoor Positioning: Basic Techniques, Hybrid Algorithms and Open Software Platform
802.11 networks democratisation, combined with new mobility and needs, makes us interested in continuity of innovative services. The need for contextual knowledge grows, based on the availability of positioning services. It takes account of environmental dynamic changes and exploits Wi-Fi-based sensors from the market. After state of the art reveals the need, considering characteristics of indoor and outdoor heterogeneous environment, we briefly introduce the initial system OWLPS-0.8 with the description of basic components, positioning algorithms and very first elements of expertise. We then present a set of new contributions from a topological model, a history memorisation algorithm derived from Viterbi and its implementation in positioning algorithms from the literature. We also propose a new design platform (OWLPS-1.0) addressing the dynamic changes in the environment and composing new algorithms to reduce the calibration and cartography cost as well as to minimise the distortion of signal strength dynamic variations in modern buildings.
   
10:45 - 11:00 Thomas Gallagher, Binghao Li, Andrew G. Dempster, Chris Rizos (presenting author): A sector-based campus-wide indoor positioning system
The purpose of this paper is to describe a campus-wide indoor and outdoor positioning system developed at the School of Surveying and Spatial Information Systems at the University of New South Wales, Australia. The system presented uses a Wi-Fi positioning technology called fingerprinting to locate users indoors, and GPS to locate users outdoors. Fingerprinting first requires the building up of a database of signal strengths from different Wi-Fi access points taken at different points across the area of interest. Then, the user scans the signal strengths in the wireless network and sends the result to the database which will find the closest match, and return the likeliest location of the user. Our work investigates different approaches to database generation and fingerprints matching, and their impact on system performance.
   
11:00 - 11:15 Pedro Mestre (presenting author), Hugo Pinto, João Matias, João Moura, Paula Oliveira and Carlos Serôdio: Multiple Wireless Technologies Fusion for Indoor Location Estimation
Results from the analysis of electromagnetic signals are a possible source of information to feed the input of a indoor location system, as wireless communications are becoming more and more ubiquitous and widely available in consumer electronic devices. In this work a fingerprinting-based solution for indoor location that uses information from multiple communication technologies is presented. For testing and proof of concept purposes authors used IEEE802.15.4 and IEEE802.11 as wireless communications technologies. When using multiple sources of information (technologies) to do location estimation two approaches can be used to integrate them: use each one separately, in layers, where each technology adds a detail level based on its coverage area, or, merge data collected from several technologies and thread them all together. In this work we use both approaches.
   
11:15 - 11:30 Widyawan: Resolving the Fingerprinting Problem: Comparison of Propagation Modelling and Machine Learning Approach
A major drawback of indoor localizations based on RSS (Received Signal Strength) measurements is the necessity to generate a fingerprint. Generating a fingerprint database is an exhaustive, time consuming and cumbersome effort. Moreover, major changes in the environment (movement of large pieces of furniture or appliances, adding or removing walls) will render a current fingerprint inaccurate and require re-building of a new fingerprint. This disadvantage makes the indoor localization system inoperable as a localization system. This work explores two main approaches to overcome this problem: fingerprint prediction with a propagation model and fingerprint modelling with a machine learning approach. There are two propagation models that can be used, namely the One Slope Model (OSM) and the Multi Wall Model (MWM). A particle filter is used as a filtering algorithm to estimate the user position. The Support Vector Machine (SVM) is one of the machine learning algorithms that is used to model the complete fingerprint from few training data.
   

Oral Presentations (Poster Teasers)

Thursday, September 16
Auditorium G7

   
11:30 - 11:32 You Zheng (presenting author at JDIR Belfort-Montbéliard), Oumaya Baala, Alexandre Caminada: Optimization Model for Indoor WLAN-based Positioning System (Poster Teaser)
Positioning systems using Wireless Local Area Networks (WLANs) have been suggested as a viable alternative to provide location information for indoor areas. But, increasing the density of AP can improve the system accuracy and precision, whereas the communication quality due to frequency interferences and installation cost are increasing too. This paper presents an approach to deploy a WLAN in order to guarantee the requested Quality of Service (QoS) while reducing the location error by using optimization method. We defined a formal model for this problem and proposed the objective function which is to minimize the network installation cost, QoS lack and positioning error. The performance evaluation results demonstrate that our solution works well when the penalty coefficient of QoS lack to penalty coefficient of positioning error ratio is given an appropriate value.
   
11:32 - 11:34 Peter Brida (presenting author), Juraj Machaj, Jozef Benikovsky: Effect of Environmental Changes on Accuracy of IEEE 802.11 Indoor Fingerprinting Positioning System WifiLOC (Poster Teaser)
The performance of our indoor positioning system based on IEEE 802.11 is evaluated for real environments. We call the system WifiLOC and it is implemented as a mobile assisted positioning system. The architecture and fundamental principles of the system are presented. The positioning system is based on the fingerprinting method, which utilizes signal strength information for position estimation. A lot of factors influence the propagation of radio signals in indoor environments. Therefore it is complicated to clearly model the properties of the signal propagation. This fact has also significant impact on particular properties of a RSSI based positioning system. In this paper, the impact of the positioning accuracy is presented taking into account various conditions such as moving objects in the observed area or the type of indoor environment, e.g. corridor, office and room. The influence of different conditions during the off-line and the on-line phase of fingerprinting positioning method on the positioning accuracy is also investigated. The observed facts are very important for successful implementation of location based services.
   
11:34 - 11:36 Paolo Addesso, Luigi Bruno (presenting author, at University of Salerno), Roberto Garufi, Maurizio Longo, Rocco Restaino, Anton Luca Robustelli: A Model – Based Approach for WLAN Localization in Indoor Parking Areas (Poster Teaser)
Wireless location of a User Equipment (UE) has received growing attention in recent years. The first step for the design of a wireless location system consists in choosing the system architecture and the localization algorithm that match the requirements of the working scenario. In this paper the area of interest is represented by an indoor parking lot, in which the presence of motor vehicles alters the electromagnetic field and causes large errors in vehicle location estimation. A possible strategy to deal with this problem is the use of a server-based architecture, that ensures a secure and scalable architecture and that allows the knowledge of system state, such as the number and the positions of the motor vehicles. Indeed this knowledge can be used to design suitable algorithm, based on simplified electromagnetic models, to improve the localization performance.
   
 

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