\section{Conclusion}
Within this work a novel approach for utilising the forward-backward smoother and backward simulation to problems of indoor localisation was presented.
Both were implemented as fixed-lag and fixed-interval smoother. 
It was shown that smoothing methods are able to decrease the estimation error and improve the overall localisation.
Especially fixed-lag smoothing is a great tool for runtime support by reducing temporal errors and improving the overall estimation with affordable costs.
However, a fixed-lag smoother is not able to change the lag dynamically, as its name suggests. 
Therefore, a dynamic-lag smoother could be able to further improve the estimation by considering higher lags in critical areas. 

Finally, the smoothing transition does not use any information provided by the underlying graph structure. 
This would allow to use environmental informations and to replace the current line-of-sight model with a graph-based one.
By incorporating the Wi-Fi's signal strength measurements a more advanced smoothing transition should be able to compensate for faulty Wi-Fi measurements and the hereby resulting jumps between positions.