\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?