added fast fingerprinting method to related work

tex_review/chapters/eval.tex

@@ -92,6 +92,8 @@ This might already provide enough accuracy for some use-cases and buildings, but
 Therefore, instead of using a pure empiric model, we deploy an optimization scheme to find a well-suited set of parameters ($\mPosAPVec{}, \mTXP{}, \mPLE{}, \mWAF{}$) per \docAPshort{}, where $\mPosAPVec{} = (x,y,z)^T$ denotes the \docAPshort{}'s estimated position. 
 The optimization is based on a set of reference measurements $\vec{s_{\text{opt}}}$ throughout the building, e.g. every \SI{3}{} to \SI{7}{\meter} centred within a corridor and between \SI{1}{} and \SI{4}{} references per room, depending on the room's size. 
 Compared to classical fingerprinting, where reference measurements are recorded on small grids between \SI{1}{} to \SI{2}{\meter}, this highly reduces their required number and thus the overall setup-time.
+\add{Of course, their are fast fingerprinting solutions like \cite{Guimaraes16} (cf. section \ref{sec:relatedWork}), which are able to record the reference measurements while walking on predefined paths. 
+Nevertheless, such an approach would also be compatible with the Wi-Fi model presented here and is thus a valid topic for future work.}
 
 The target function to optimize the $6$ model parameters for one \docAPshort{} is given by