current TeX

tex/chapters/experiments.tex

@@ -359,10 +359,10 @@
 				
 		
 		We therefore examined variations of the probability calculation from \refeq{eq:wifiProb}.
-		Removing the strongest/weakest \docAPshort{} from $\mRssiVec{}$ yielded similar results.
-		While some estimations were improved, the overall estimation error increased for our walks,
-		as there are many situations where only a handful \docAP{}s can be seen. Removing (valid)
-		information will highly increase the error for such situations.
+		Despite the results show in \cite{PotentialRisks}, removing weak \docAPshort{}s from $\mRssiVec{}$
+		yielded similar results. While some estimations were improved, the overall estimation error increased
+		for our walks, as there are many situations where only a handful \docAP{}s can be seen.
+		Removing this (valid) information will highly increase the error for such situations.
 		
 		Incorporating additional knowledge provided by virtual \docAP{}s (see section \ref{sec:vap}) mitigated this issues.
 		If only one out of six virtual networks is observed, this observation is likely to be erroneous, no matter

tex/chapters/relatedwork.tex

@@ -1,7 +1,48 @@
-relatedwork
+\section{Related Work}
 
-wifi anfänge von radar (microsoft) etc
-\cite{radar} \cite{horus} \cite{secureAndRobust}
+	Indoor localization based on \docWIFI{} signal strengths dates back to the year
+	2000 and the work of Bahl and Padmanabhan \cite{radar}. During an offline-phase, a
+	multitude of reference measurements are conducted once. Those measurements are compared
+	against live readings during an online-phase. The pedestrian's location is inferred 
+	using the $k$-nearest neighbor(s) based on the Euclidean distance between currently
+	received signal strengths and the readings during the offline phase.
+	
+	Inspired by this initial work, Youssef et al. \cite{horus} proposed a more robust, probabilistic 
+	approach. Fingerprints were placed every \SI{1.52}{\meter} and estimated by scanning each location 
+	100 times. The resulting signal strength propagation for one location is hereafter denoted by a histogram.
+	The latter can be compared against live measurements to infer its matching-probability. The center
+	of mass among the $k$ highest probabilities, including their weight, describes the pedestrian's current location.
+	%
+	In \cite{ProbabilisticWlan}, a similar approach is used and compared against nearest neighbor and machine learning.
+	Furthermore, they mention potential issues of unseen transmitters and describe a simple heuristic of how to handle such cases.
+	
+	Meng et al \cite{secureAndRobust} further discuss several fingerprinting issues like environmental changes 
+	after the fingerprints were recorded. They propose an outlier detected based on RANSAC to remove potentially
+	distorted measurements and thus improve the matching process.
+	
+	Despite a very high accuracy due to real-world comparisons, all approaches suffer from tremendous setup-
+	and maintainance times. 
+	
+	Therefore it makes sense to replace those time consuming fingerprints by model predictions. 
+	Those are a well established research field, mainly used to determine the \docWIFI{}-coverage
+	for new installations. \cite{ANewPathLossPrediction, PredictingRFCoverage, empiricalPathLossModel}
+	
+	
+	einfach messen, ab und zu einen GPS fix und danach genetisch alles zuusammenoptimieren. also kein vorwissen
+	\cite{WithoutThePain}
+	
+	das muesste noch was aehnliches sein:
+	\cite{crowdinside}
+	
+	
+	
+	
+	neben signalstärke gibt es noch viele andere methoden über laufzeiten wie beim gps etc.
+	diese erfordern meist aber spezial-hardware und laufen deshalb nicht so einfach auf dem smartphone [= ueberleitung!]
+	
+	
+	
+ \cite{secureAndRobust}
 
 
 andere methoden neben signalstärke

tex/chapters/work.tex

@@ -118,7 +118,7 @@
 	
 		Just optimizing \mTXP{} and \mPLE{} with constant \mWAF{} and known transmitter position
 		usually means optimizing a convex function as can be seen in figure \ref{fig:wifiOptFuncTXPEXP}.
-		For such error functions, algorithms like gradient descent \cite{TODO} and (downhill) simpelx \cite{TODO}
+		For such error functions, algorithms like gradient descent and simplex \cite{gradientDescent, downhillSimplex1, downhillSimplex2}
 		are well suited and will provide the global minima:
 		
 		\begin{equation}