Block-based Weighted Clustering (BWC) scheme for radio database clus tering 

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Indoor/WLAN positioning

Indoor positioning includes localization techniques that are intended to be used inside buildings, on university campuses, etc. It is generally based on radio, infrared or ultrasound technologies.
In the following we review brie°y the techniques based on radio infrastructures, i.e. po- sitioning techniques developed for Radio Frequency Identi¯cation (RFID) networks, Ultra Wide Band (UWB) systems, and Wireless Local Area Networks (WLANs).

RFID positioning

An RFID (Radio Frequency Identi¯cation) system consists of tags, a scanner (reader), and software such as a driver and middleware. The main function of the RFID system is to retrieve information (ID) from a tag (also known as a transponder). A tag can include additional information other than the ID, which opens up opportunities to new application areas ([43]).
RFID positioning is merely based on proximity sensing technique. RFID systems de- termine the position of a target based on the presence of that target in a particular area, within the range of a RFID scanner. Deployment of an RFID system over a large campus or company area is very expensive because of the need for installing a multitude of scanners. Also, changing the layout of a
manufacturing plant or moving walls in an o±ce requires remounting and rewiring of the RFID readers. Besides, RFID positioning needs proprietary hardware; such proprietary hardware is usually only available from a single vendor, making equipment prices higher than standard-based solutions.

UWB positioning

Ultra Wide Band (UWB) is a radio technology based on using ultrashort pulses (typi- cally <1 ns). On the spectral domain, the system enables transmission of data over a large bandwidth (> 500 MHz) ([44]). UWB positioning systems, similar to most other positioning solutions, have proprietary scanners that continuously monitor UWB radio transceivers attached to clients. Positioning approaches for UWB are either based on lateration (by using time or RSS measurements), or angulation (AoA) ([45], [46]). According to [45], due to the high time resolution of UWB signals, time-based location estimation schemes usually provide better accuracy than the others. The lateration based on RSS measurements in UWB su®ers from the same problems as in cellular networks. The AoA approache is not suitable neither, since it demands use of antenna arrays, increasing notably the system cost. More importantly, due to the large bandwidth of a UWB signal, the number of paths may be very large, especially in indoor environments. Therefore, accurate angle estimation becomes a very challenging issue.

WiFi positioning

The major problem of indoor positioning technologies discussed so far is their propri- etary nature, which demands a separate infrastructure to perform the localization. This attribute makes these techniques costly to deploy, scale, and support. Integrated solutions are certainly preferable in order to reduce these costs and operational support risks. Over the past few years, WiFi has been adopted as the primary standard for wireless LANs in company facilities and homes worldwide. Based on the IEEE 802.11 standards, WiFi addresses needs for secure, high performance mobile data networking. With the widespread adoption of wireless LANs, WiFi is an ideal infrastructure for positioning tech- nologies. The WiFi signal does not contain any exploitable temporal information. Thus, in order to design an integrated positioning technique, we must rely on the received power measurements. There exist some commercial WiFi positioning solutions in the market that use temporal methods such as TDoA. But all these solutions demand certain modi¯cations to the actual WiFi sructure (such as modi¯ed WiFi access points) ([42]). Since the available information in the WiFi signal is the received power level, the pro- posed positioning approaches are: proximity sensing, RSS-based trilateration, location ¯n- gerprinting. Proximity sensing is equivalent to Cell-ID method in GSM and UMTS. The position of the terminal is simply determined by considering its serving access point. In the RSS based trilateration, the position is determined by lateration with respect to three or more access points. The distance between the mobile and the access points is calculated by using a radio propagation model. The main problem in this technique is the lack of a precise radio propagation model for the complex indoor environments ([42]). Fingerprinting method requires a database containing the signal strength records. The position is deter- mined by comparing the measurements of the mobile terminal with the database stored ¯ngerprints. The main constraint in this method is building and upgrading the database ([42]). There exists another problem in ¯ngerprinting technique which stems from the re- ceived signal temporal variations. As the received signal strength °uctuates over the time, the position extracted from the database will °uctuate as well. Several ¯ltering methods have been introduced in the literature to improve WiFi positioning techniques (see [42]).

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Table of contents :

Acknowledgement
R¶esum¶e
1 Mobile localization via Location Fingerprinting 
1.1 Localization in cellular networks
1.2 Location ¯ngerprinting, Some challenging aspects
1.3 Thesis outline
1.4 Publications
2 Localization technologies in wireless networks 
2.1 Location Based Services (LBS) context
2.1.1 Origins, evolution
2.1.2 Operational actors
2.1.3 Privacy
2.1.4 Databases in LBS
2.2 Fundamental concepts
2.2.1 Basic localization techniques
2.2.2 Time-based versus RSS-based range measurements
2.3 Satellite positioning
2.4 Cellular positioning
2.4.1 Cell-ID and enhancements
2.4.2 Time Di®erence of arrival (TDoA)
2.4.3 Angle of Arrival (AOA)
2.4.4 Location Fingerprinting (LFP)
2.5 Indoor/WLAN positioning
2.5.1 RFID positioning
2.5.2 UWB positioning
2.5.3 WiFi positioning
2.6 A performance comparison
2.7 Location ¯ngerprinting in a machine learning viewpoint
3 Location ¯ngerprinting: A performance study for cellular systems 
3.1 Background and basic de¯nitions
3.2 System model
3.2.1 Propagation environment
3.2.2 Measurements error
3.2.3 Fingerprinting system
3.3 Performance analysis
3.3.1 Impact of the path loss exponent
3.3.2 Impact of the measurements error
3.3.3 Impact of the grid resolution
3.4 Conclusion
4 Cluster analysis for radio database compression 
4.1 Introduction: cluster analysis for location ¯ngerprinting
4.2 Cluster analysis
4.3 Radio database clustering
4.3.1 Concept and notations
4.3.2 Clustering algorithms
4.4 Complexity analysis
4.4.1 Transmission load
4.4.2 Computation load
4.5 Positioning performance evaluation
4.5.1 Simulations setup
4.5.2 Parameters setting
4.5.3 Simulations results
4.6 Conclusion
5 Block-based Weighted Clustering (BWC) scheme for radio database clus tering 
5.1 Weighted variants of k-means algorithm
5.2 Block-based Weighted Clustering (BWC) scheme
5.3 Positioning performance evaluation
5.3.1 Experiments setup
5.3.2 Parameters setting
5.3.3 Evaluation of clustering techniques
5.3.4 Comparison with other compression techniques
5.4 Conclusion
6 Handling missing data in RSS-based location ¯ngerprinting 
6.1 Introduction: Missing data in location ¯ngerprinting
6.2 Inference from missing data
6.2.1 The framework
6.2.2 Methods for handling missing data
6.3 Missing data in RSS measurements
6.3.1 Complete RSS measurements
6.3.2 Missing mechanism for RSS measurements
6.4 Handling missing data in ¯ngerprinting systems
6.4.1 Notations
6.4.2 Complete database – Incomplete mobile measurements
6.4.3 Incomplete database – Incomplete mobile measurements
6.5 Simulations setup
6.5.1 Modeling the radio propagation
6.5.2 Fingerprinting system con¯gurations
6.6 Simulation results
6.6.1 Complete database-Incomplete mobile measurements
6.6.2 Incomplete database-Incomplete mobile measurements
6.7 Conclusion and discussion
7 Conclusions and perspectives 
Glossary
List of ¯gures
List of tables

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