From 690c400e96fee00906133262a97c8ea95b315b87 Mon Sep 17 00:00:00 2001 From: kazu Date: Mon, 1 May 2017 21:03:13 +0200 Subject: [PATCH] current TeX --- tex/chapters/experiments.tex | 8 +++--- tex/chapters/relatedwork.tex | 47 +++++++++++++++++++++++++++++++++--- tex/chapters/work.tex | 2 +- 3 files changed, 49 insertions(+), 8 deletions(-) diff --git a/tex/chapters/experiments.tex b/tex/chapters/experiments.tex index 0cbe670..2fabdb5 100755 --- a/tex/chapters/experiments.tex +++ b/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 diff --git a/tex/chapters/relatedwork.tex b/tex/chapters/relatedwork.tex index 995fc6f..bcca9e5 100755 --- a/tex/chapters/relatedwork.tex +++ b/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 diff --git a/tex/chapters/work.tex b/tex/chapters/work.tex index 149b5b3..98a7316 100755 --- a/tex/chapters/work.tex +++ b/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}