| dc.date.accessioned | 2019-04-16T07:38:11Z | |
| dc.date.available | 2019-04-16T07:38:11Z | |
| dc.date.issued | 2019 | |
| dc.identifier | doi:10.17170/kobra-20190409403 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/11186 | |
| dc.language.iso | eng | |
| dc.rights | Urheberrechtlich geschützt | |
| dc.rights.uri | https://rightsstatements.org/page/InC/1.0/ | |
| dc.subject.ddc | 620 | |
| dc.title | Understanding Factors Influencing the Performance of a Wi-Fi Fingerprinting System | eng |
| dc.type | Dissertation | |
| dcterms.abstract | Location information plays a vital role in today’s society. People usually carry their mobile devices everywhere they go to benefit from real-time location services; the location of the device is the location of users. The focus of positioning services is shifting from outdoors to indoors. Technological services which depend on indoor locations are increasing in popularity. Wi-Fi fingerprinting is a promising technique that can be used for indoor localization. In this regard, this dissertation targets at improving understanding on the influence of factors on Wi-Fi received signal strength. It provides useful information applicable in the implementation of a reliable, consistent Wi-Fi fingerprinting system that takes into account factors such as accuracy, recognition rate, and energy consumption.
Different techniques and algorithms have been used in developing a Wi-Fi fingerprinting system. Several studies have been done to analyze factors that influence the performance of a Wi-Fi fingerprinting system. New technologies in wireless networks may provide useful features to improve the performance of Wi-Fi fingerprinting systems but may also give rise to new challenges. Hence, despite the intense research on the field, there are still factors which influence the Wi-Fi signal and performance of Wi-Fi fingerprinting that have not been thoroughly investigated.
In this Ph.D. thesis, I performed various experiments to investigate factors influencing signal strength of a Wi-Fi network and the performance of a Wi-Fi fingerprinting system. I compared the fluctuation of 2.4 and 5 GHz bands by considering factors such as how the presence of people in office environments such as corridors, halls, and office rooms affects Wi Fi signals. The performance of a Wi-Fi fingerprinting system using the 2.4 and 5 GHz Wi Fi signal is also evaluated in terms of accuracy, recognition rate, and power consumption in scanning those networks. The influence of small-scale fading and the device heterogeneity problem on Wi-Fi signal strength and Wi-Fi fingerprinting was also be investigated in this thesis. The statistical ANOVA and t-test were used to validate the influence of small-scale fading and device heterogeneity on Wi-Fi signal strength. I analyzed the distribution and the fluctuation of measured Wi-Fi data and then compared the performance of the Wi-Fi fingerprinting system WHERE under the influence of those factors. Consequently, the results showed that the Wi-Fi fingerprinting system achieves similar accuracy when using 2.4 GHz and 5 GHz bands. However, the recognition rate of a system using signals of 5 GHz was found to be higher than that using 2.4 GHz signals. Scanning 2.4 GHz networks consumes less power than scanning 5 GHz networks. The statistical tests also showed that there is a difference between mean values of Wi-Fi signals measured over a short distance. The Wi-Fi signal strength measured at the same location by different devices is also different. The recognition rate decreases from 100% to 47.76% when heterogeneous devices are used in the training phase and the positioning phase. In addition to device heterogeneity, small-scale fading was also found to impact fingerprints of the measured positions in such a way that devices that were only one centimeter apart were erroneously recorded as different locations. To mitigate the influence of small-scale fading, the collection of Wi-Fi data collected over a small distance can be used to generate the fingerprint of the location and results in an improvement in the recognition to 92.13%.
The results of this Ph.D. thesis help to better understand the different characteristics of the 2.4 and 5 GHz Wi-Fi signals as well as the influence of different factors on the performance of a Wi-Fi fingerprinting system. The selection of frequency bands in Wi-Fi fingerprinting approaches may not influence the results of accuracy but may influence the recognition rate and the power consumption of the system. In this regard, a trade-off of the performance should be considered when designing an indoor localization system using Wi-Fi fingerprinting. I propose to record the motion state of measurement devices when training data is collected. The justification is that when the measurement devices are slightly moved, the collected data was more reliable than when the measurement devices are kept stationary. These understandings provide useful information for the design and implementation of Wi-Fi fingerprinting systems. | eng |
| dcterms.accessRights | open access | ger |
| dcterms.creator | Duong, Ngoc Doan | |
| dcterms.dateAccepted | 2019-03-25 | |
| dcterms.extent | vi, 102 Seiten | |
| dc.contributor.corporatename | Kassel, Universität Kassel, Fachbereich Elektrotechnik / Informatik | ger |
| dc.contributor.referee | David, Klaus (Prof. Dr.) | |
| dc.contributor.referee | Dahlhaus, Dirk (Prof. Dr.) | |
| dc.subject.swd | Lokalisation | ger |
| dc.subject.swd | Innenraum | ger |
| dc.subject.swd | Drahtloses lokales Netz | ger |
| dc.subject.swd | Funksignal | ger |
| dc.type.version | publishedVersion | |
