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revise intro

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toni
2018-10-16 16:38:49 +02:00
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tex_review/chapters/introduction.tex
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@@ -23,7 +23,7 @@ We also use \del{a novel} \add{an} approach for finding an exact estimation of t
Many historical buildings, especially bigger ones like castles, monasteries or churches, are built of massive stone walls and have annexes from different historical periods out of different construction materials.
\del{This leads to problems} \add{This makes it more challenging to ensure good radio coverage of the entire building, especially} for technologies using received signal strengths indications (RSSI) from \docWIFI{} or Bluetooth.
\add{For methods requiring environmental knowledge, like the wall-attenuation-factor model, the high signal attenuation between different rooms causes further problems.}
\add{For methods requiring environmental knowledge, like signal strength prediction models, the high signal attenuation between different rooms causes further problems.}
Many unknown quantities, like the walls definitive material or thickness, make it expensive to determine important parameters, \eg{} the signal's depletion over distance.
Additionally, \del{most wireless} \add{many of these} approaches are based on a line-of-sight assumption.
Thus, the performance will be even more limited due to the irregularly shaped spatial structure of such buildings.
@@ -44,7 +44,10 @@ However, this usually requires new cabling, e.g. an extra power over Ethernet co
In contrast, the beacons can simply be plugged into already existing power outlets and due to their low price they can be distributed in large quantities, if necessary.
In the here presented scenario, the beacons do not establish a wireless network and thus serve only to provide signal strengths.}
%To sum up, this work presents a smartphone-based localization system using.
%Was brauchen wir für unser system?
%Im Gegensatz zu vielen anderen Arbeiten
To sum up, \add{this work presents an updated version of the winning localization system of the smartphone-based competition at IPIN 2016 \cite{Ebner-15}, including the improvements and newly developed methods that have been made since then \cite{Ebner-16, Ebner-17, Fetzer-17, Bullmann-18}.
This is the first time that all these previously acquired findings have been fully combined and applied simultaneously.
@@ -59,8 +62,12 @@ During the here presented update, the following novel contributions will be pres
%Instead we use a simple optimization scheme based on reference measurements to estimate a corresponding \docWIFI{} model.
The goal of this work is to propose a fast to deploy \del{and low-cost} localization solution, that provides reasonable results in a high variety of situations.
\add{However, many state-of-the-art solutions are evaluating their systems within office or faculty buildings, offering a modern environment and well described infrastructure.}
Consequently, we believe that by utilizing our localization approach to such a challenging scenario, it is possible to prove those characteristics.
\add{Despite evaluating the novel contributions and the overall performance of the system, we have carried out additional experiments to determine the performance of our Wi-Fi optimization in such a complex scenario as well as a detailed comparison between KDE-based and weighted-average position estimation.}
\add{To initially set up the system we only require a blueprint to create the floorplan, some Wi-Fi infrastructure, without any further information about access point positions or parameters, and a smartphone carried by the pedestrian to be localized.
The existing Wi-Fi infrastructure can consist of the aforementioned Wi-Fi beacons and / or already existing access points.
The combination of both technologies is feasible, depending on the scenario and building.
Despite evaluating the novel contributions and the overall performance of the system, we have carried out additional experiments to determine the performance of our Wi-Fi optimization in such a complex scenario as well as a detailed comparison between KDE-based and weighted-average position estimation.}
%novel experiments to previous methods due to the complex scenario blah und blub.}
%Finally, it should be mentioned that the here presented work is an highly updated version of the winner of the smartphone-based competition at IPIN 2016 \cite{Ebner-15}.

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tex_review/chapters/relatedwork.tex
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@@ -54,7 +54,7 @@ for truly continuous transitions along the surface spanned by all triangles.
%eval - wifi, fingerprinting
The outcomes of the state evaluation process depend highly on the used sensors.
Most smartphone-based systems are using received signal strength indications (RSSI) given by \docWIFI{} or Bluetooth as a source for absolute positioning information.
At this, one can mainly distinguish between fingerprinting and signal-strength prediction model based solutions \cite{Ebner-17}.
At this, one can mainly distinguish between fingerprinting and signal strength prediction model based solutions \cite{Ebner-17}.
Indoor localization using \docWIFI{} fingerprints was first addressed by \cite{radar}.
During a one-time offline-phase, a multitude of reference measurements are conducted.
During the online-phase the pedestrian's location is then inferred by comparing those prior measurements against live readings.