added wifi to related work

tex/chapters/relatedwork.tex

@@ -13,7 +13,7 @@ A resampling step is deployed to prevent that only a small number of particles h
 Most localisation approaches differ mainly in how the transition and evaluation steps are implemented and the available sensors are incorporated \cite{Fetzer-16, Ebner-16, Hilsenbeck2014}.
 Additionally, within this paper we present a method, which is designed to run solely on a smartphone.
 
-In its most basic form, the state transition is given by..
+In its most basic form, the state transition is given by.. einfach distanz und heading.. intersection with walls usw.
 
 \todo{nochmal mit frank klären was wir jetzt GENAU machen.}
 
@@ -22,11 +22,44 @@ like indoor graphs. Besonders geometric spatial models sind beliebt
 
 \todo{kurz auf voronoi eingehen mit neueren papern und dann auf grid basierte eingehen. schreiben das wir in previous work auch solche benutzt haben, aber das problem ist halt der gigantische speicheraufwand. deshalb haben wir uns für triangle based entscheiden, die erstellung ist einfacher, die verfahren sind aus der spieletheorie bekannt und erfolgreich im einatz. natürlich ist das ganze ein wenig rechenaufwendiger, da nun bla und blub gemacht werden muss, jedoch ist das laufen realisischer und nicht auf 45 grad winkel begrenzt. es wird also eine höhere genaugikeit erwartet, bei stark reduzierten speicher und zugrifssbedarf auf das netz.}
 
-The state evaluation process depends highly on the used sensors. 
-However, most smartphone-based localization systems are using wireless technologies like Wi-Fi and Bluetooth. 
-Here one can mainly differ between fingerprintg and signal-strength based solutions.
-For example \cite{} used a fingerprinting based method to...  however, high setup time. 
-\cite{} uses a robot for this, however in old buildings not every area is easily accesable for robots due to absätze und kleine treppen. außerdem sehr teuer der ganze mist. 
+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 Wi-Fi or Bluetooth as a source for absolute positioning information. 
+At this, one can mainly differ between fingerprinting and signal-strength prediction model based solutions \cite{Ebner-17}.
+Indoor localization using Wi-Fi 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.
+Based on this pioneering work, many further improvements where made within this field of research \cite{PropagationModelling, ProbabilisticWlan, meng11}. 
+However, despite a very high accuracy up to \SI{1}{\meter}, fingerprinting approaches suffer from tremendous setup- and maintenance times. 
+Using robots instead of human workforce might thus be a viable choice, still this seems not to be a valid option for old buildings with limited accessibility due to uneven grounds and small stairs.
+  
+Signal strength prediction models are a well-established field of research to determine signal strengths for arbitrary locations by using an estimation model instead of real measurements.
+While many of them are intended for outdoor and line-of-sight purposes \cite{PredictingRFCoverage, empiricalPathLossModel}, they are often applied to indoor use-cases as well \cite{Ebner-17, farid2013recent}.
+Besides their solid performance in many different localization solutions, a complex scenario requires a equally complex signal strength prediction model.
+As described in section 1, historical buildings represent such a scenario and thus the model has to take many different constraints into account.
+An example is the wall-attenuation-factor model \cite{}. 
+It introduces an additional parameter to the well-known log distance model \cite{}, that considers obstacles between (line-of-sight) the AP and the location in question by attenuating the signal with a constant value. 
+Depending on the use-case, this value describes the number and type of walls, ceilings, floors etc. between both positions. 
+For obstacles, this requires an intersection-test of each obstacle with the line-of-sight, which is costly for larger buildings. 
+Thus \cite{Ebner-17} suggests to only consider floors/ceilings, what can be calculated without intersection checks and allows for real-time use-cases running on smartphones.
+
+To further improve the ... \cite{} introduces an approach that works without any prior knowledge.
+
+
+
+
+For real-time use on a smartphone, a (discretized) model pre-computation might thus be necessary .
+
+
+
+
+A simple approach 
+
+Again, many pre-known parameters like the walls material need to be known
+
+much complexer model is required for a good performance within highly diverse buildings as explained in section 1
+
+needs to know the position of the access point
+
 wir haben ansonsten immer signalstrength basierte systeme genommen, welche aber eine simple line of sight annahme machen, außerdem haben wir nur eine materialkonstante angenommen, was für gebäude mit unterschiedlichen baumaterialen nicht klappen kann da das signal durch bla und blub abgelenkt wird. deshalb wird in dieser arbeit ein kompromiss zwischen beiden verwendet anhand eines optimierungsverfahren. ein vorteil der dabei entsteht, die position der ap's kann uns egal sein. da diese geschätzt werden. 
 \todo{gibt es dazu related work?} 
 

tex/egbib.bib

@@ -2896,3 +2896,12 @@ note = {\url{http://reichsstadtmuseum.rothenburg.de}, Accessed: 2018-03-22},
 address = {{Rothenburg, Germany}},
 }
 
+@article{farid2013recent,
+  title={Recent advances in wireless indoor localization techniques and system},
+  author={Farid, Zahid and Nordin, Rosdiadee and Ismail, Mahamod},
+  journal={Journal of Computer Networks and Communications},
+  volume={2013},
+  year={2013},
+  publisher={Hindawi}
+}
+