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+\section{Conclusion and Future Work}
+
+	As denoted within the previous evaluations and discussions, the accuracy of 
+	indoor localization systems based on \docWIFI{} depends on a manifold
+	of parameters and even minor adjustments can yield visible improvements.
+	Depending on required accuracy and acceptable setup- and maintenance times,
+	several approaches are conceivable:
+
+	If the use-case does not require utmost accuracy and the locations of permanently
+	installed transmitters is already well known, just using empiric model parameters
+	is a viable choice for many situations.
+	
+	However, when combined with (particle) filtering, a heavily constrained 
+	movement model might be a potential issue, as it can get stuck when
+	sensor observations or model predictions are too erroneous.
+	
+	Using a small number of reference measurements to optimize the model parameters will already suffice
+	to improve such errors. Furthermore, it also removes the need for prior knowledge
+	about transmitter locations, as those can be estimated via optimization.
+	
+	For the best accuracy, more complex signal strength propagation models
+	are required, which in turn demand for more reference measurements.
+	%
+	However, while using several instances of a simple propagation model
+	for different regions within a building is able to decrease the estimation 
+	error, this approach might require prior guessing of where to place those regions.
+	As indicated by the error plots, just using one model for every floor within the building
+	seems to be a viable alternative.
+	
+	More complex models, that include information about walls and other obstacles, should 
+	be able to reduce the maximum error, which remains for some locations, at the cost of additional computations.
+	Special data-structures for pre-computation combined with online interpolation might
+	be a viable choice for utmost accuracy that is still able to run on
+	a commercial smartphone in real-time.
+	
+	While we were able to improve the performance of the \docWIFI{} sensor component,
+	the filtering process should be more robust against erroneous observations.
+	Getting stuck should be prevented, independent of minor changes in quality for
+	the signal strength prediction model \cite{Fetzer-17}.
+	
+	Our \docWIFI{}  quality metric often was able to determine situations that 
+	would yield multimodal or bad \docWIFI{} estimations and temporarily 
+	ignoring this sensor prevented additional errors. Still, there were some
+	cases where the metric failed to correctly determine a potentially bad
+	observation, which leaves room for future improvements.
+	
+	
+	%100 prozent optimierung ist nicht moeglich, es gibt
+	%immer stellen, die, zugunsten von anderen, schlechter werden.
+	%es haengt auch stark davon ab, was man optimiert, das modell,
+	%die uebereinstimmung, welche fingerprints [schlechte vs. gute stellen]
+
+	%zudem ist das modell fuer unser gebaeude nicht gut ggeeignet.
+	%zu viele verschiedene materialien und trennwaende, APs immer in raeumen,
+	%nie auf dem flur. viele hindernisse, wenige freie raeume.
+	%andere modelle koennten hier helfen, erfordern dann aber zur
+	%aufzeit mehr berechnung, oder muessten vorab auf einem grid berechnet
+	%werden \todo{cite auf competition}
+
+
+	%besseres modell mit (leichter) interpolation zwischen den randbereichen?
+