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