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Small fixes

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MBulli
2018-09-16 20:16:47 +02:00
parent 05da3a9324
commit 08af2ad411
9 changed files with 33 additions and 29 deletions
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tex/chapters/relatedwork.tex
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@@ -36,7 +36,7 @@ Smooth 3D transitions, like walking stairs, would require much more complex inte
To overcome both limitations, the building's floorplan can be used to derive a graph-based structure,
like voronoi diagrams or fixed-distance grids, moving all costly intersection tests into a one-time offline phase \cite{Ebner-16, Hilsenbeck2014}.
Hereafter, graph-based random walks along the created data-structure can be used as a fast transition approximation.
When generating nodes and edges along stairs and elevators, this also allows for smooth transitions in 3D space.
Smooth transitions in 3D space can be achieved by generating nodes and edges along stairs and elevators.
Furthermore, the nodes can be used to store additional information, like their distance towards a pedestrian's desired destination.
Such information can be included during the transitions step, \eg{} increasing the likelihood of all potential movements that approach
this destination \cite{Ebner-16}.
@@ -73,10 +73,10 @@ While many of them are intended for outdoor and line-of-sight purposes \cite{Pre
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{PathLossPredictionModelsForIndoor}.
It introduces an additional parameter to the well-known log distance model \cite{IntroductionToRadio}, that considers obstacles between (line-of-sight) the AP and the location in question by attenuating the signal with a constant value.
It introduces an additional parameter to the well-known log distance model \cite{IntroductionToRadio}, which 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.
Thus \cite{Ebner-17} suggests to only consider floors/ceilings, which can be calculated without intersection checks and allows for real-time use-cases running on smartphones.
%wifi optimization
To further reduce the setup-time, \cite{WithoutThePain} introduces an approach that works without any prior knowledge.