diff --git a/paper/main.bib b/paper/main.bib
index 65f9944f7a27152e2214958f663a80682219236b..dd329d01ac3b0debfa11d0251ff7d4195420e4e8 100644
--- a/paper/main.bib
+++ b/paper/main.bib
@@ -1,5 +1,5 @@
 @INPROCEEDINGS{7528057, 
-author={D. �rn and M. Szilassy and B. Dil and F. Gustafsson}, 
+author={D. {\:O}rn and M. Szilassy and B. Dil and F. Gustafsson}, 
 booktitle={2016 19th International Conference on Information Fusion (FUSION)}, 
 title={A novel multi-step algorithm for low-energy positioning using GPS}, 
 year={2016}, 
diff --git a/paper/main.tex b/paper/main.tex
index 314b0c2a13544358fb7e38d8f7246813591f4263..204bd5c0a0daf3c2dd1c42b61e960c36077b3b3e 100644
--- a/paper/main.tex
+++ b/paper/main.tex
@@ -25,7 +25,7 @@
 \acmArticle{4}
 \acmPrice{15.00}
 
-%\input{sections/00-header}
+\input{sections/00-header}
 
 \usepackage{fontspec}
 \usepackage{graphicx}
@@ -156,6 +156,7 @@ Politecnico di Milano, Italy}
 
 \section{Conclusion}
 \label{sec:concl}
+\input{sections/07-concl}
 
 \bibliographystyle{ACM-Reference-Format}
 \bibliography{main}
diff --git a/paper/sections/06-results.tex b/paper/sections/06-results.tex
index e811ce5ac7c13263b7b8f83f0edde4fe06b86516..f9d2a9e6b9c8fcc84d43711fba3f3fdf02f392bb 100644
--- a/paper/sections/06-results.tex
+++ b/paper/sections/06-results.tex
@@ -197,9 +197,8 @@ very difficult to obtain. It is enough to have parallel state machines
 similar to the one shown in Figure~\ref{fig:cyberdynamics}, that
 independently capture the tracking of individual satellites.
 
-\subsection{Tracking the Trade-Off between Performance and Power
-  Consumption}
-\label{sec:res:tradeoff}
+\subsection{Positioning Accuracy}
+\label{sec:res:accuracy}
 
 \begin{figure*}[t]
 \begin{minipage}{0.95\columnwidth}
@@ -391,39 +390,145 @@ and~\ref{fig:car-trace-ctl} the signal used for the GPS triggering
 The figures show how the sampling strategy is able to keep the
 variance of the estimation bounded, while reducing the on time.
 
-Finally we run a large number \todo{specify} of simulations with different triggering thresholds of $P$ \todo{specify}. The idea is that higher values of P  will guarantee less power consumption at the price of lareger errors in the estimation and vice versa. In the simulations the acquisition time of the satellites signals is modeled as a random variable uniformly distributed. Moreover, while in the simulations with the cycling data the number of visible satellites is constant, in the ones with the car data it randomly changes\footnote{Specifically when the number of visible satellites is a given number there is some probability at every time step that t increases or decreases by one, always bounded betwen 3 and 6 in any case.}. The overall error of a tracking trace is here defined as the root-mean-square of the distance between the trace and the pure GPS signal.
-
-\begin{figure}[t]
- \begin{center}
-  \includegraphics[height=0.60\columnwidth, width=0.80\columnwidth]{images/cycling_trade_off.png}
-  \caption{Power-tracking error trade off with the cycling data.\todo{fix error}
-           \label{fig:cycling-trade-off}
-          }
- \end{center}
-\end{figure} 
-
-Figure~\ref{fig:cycling-trade-off} shows the simulation results for the cycing tracking. The different colors of the points correspond to the different values for the triggering threshold of the sensor fusion algorithm. We can see that the trade-off is present and well controlled through the choice of the threshold. Furthermore two other interesting phenomena are pointed out by this simulation. 
+\subsection{Tracking the Trade-Off between Performance and Power
+  Consumption}
+\label{sec:res:tradeoff}
 
-First, for low triggering values (red, green and purple points) there is less variance in terms of error since we are converging to the situation of the antenna being always turned on and therefore saturating the achievable tracking precision. Instead the variance in terms of energy consumption increases due to the sensor being more frequently turned on and off and threfore being affected by the random time required to fetch the satellites' signals. 
+\begin{figure*}
+\begin{minipage}{0.95\columnwidth}
+\begin{tikzpicture}
+\begin{axis}[%
+ height = 0.6\textwidth,
+ grid style = {black!30, dashed},
+ grid = major,
+ width = 0.95\textwidth,
+ scaled x ticks = false,
+ scaled y ticks = false,
+ y tick label style={/pgf/number format/fixed},
+ xlabel = {Energy \textcolor{red}{[Add Unit]}},
+ ylabel = {Normalized Error},
+ legend style={at={(1.3,1.1)},anchor=south},
+ legend columns=3,
+]
+\pgfkeys{/pgf/number format/.cd,1000 sep={}}
+\addplot[thick, only marks, mark=*, blue]
+ table[x index = {0}, y index = {1}, col sep=comma]
+ {data/exp_sb1.csv};
+\addlegendentry{Sensor Fusion $th=4$}
+\addplot[thick, only marks, mark=*, red]
+ table[x index = {0}, y index = {1}, col sep=comma]
+ {data/exp_sb2.csv};
+\addlegendentry{Sensor Fusion $th=5$}
+\addplot[thick, only marks, mark=*, green]
+ table[x index = {0}, y index = {1}, col sep=comma]
+ {data/exp_sb3.csv};
+\addlegendentry{Sensor Fusion $th=6$}
+\addplot[thick, only marks, mark=*, purple]
+ table[x index = {0}, y index = {1}, col sep=comma]
+ {data/exp_sb4.csv};
+\addlegendentry{Sensor Fusion $th=8$}
+\addplot[thick, only marks, mark=*, yellow]
+ table[x index = {0}, y index = {1}, col sep=comma]
+ {data/exp_sb5.csv};
+\addlegendentry{Sensor Fusion $th=10$}
+\addplot[thick, only marks, mark=*, orange]
+ table[x index = {0}, y index = {1}, col sep=comma]
+ {data/exp_sb6.csv};
+\addlegendentry{Sensor Fusion $th=12$}
+\end{axis}
+\end{tikzpicture}
+\caption{Trade-Off between Energy and Error when cycling. The error
+is normalized subtracting its minimum value.}
+\label{fig:bike-tradeoff}
+\end{minipage}
+\hspace{1mm}
+\begin{minipage}{0.95\columnwidth}
+  \vspace{1.5cm}
+\begin{tikzpicture}
+\begin{axis}[%
+ height = 0.6\textwidth,
+ grid style = {black!30, dashed},
+ grid = major,
+ width = 0.95\textwidth,
+ scaled x ticks = false,
+ scaled y ticks = false,
+ xlabel = {Energy \textcolor{red}{[Add Unit]}},
+ ylabel = {Normalized Error},
+]
+\pgfkeys{/pgf/number format/.cd,1000 sep={}}
+\addplot[thick, only marks, mark=*, blue]
+ table[x index = {0}, y index = {1}, col sep=comma]
+ {data/exp_sc1.csv};
+\addplot[thick, only marks, mark=*, red]
+ table[x index = {0}, y index = {1}, col sep=comma]
+ {data/exp_sc2.csv};
+\addplot[thick, only marks, mark=*, green]
+ table[x index = {0}, y index = {1}, col sep=comma]
+ {data/exp_sc3.csv};
+\addplot[thick, only marks, mark=*, purple]
+ table[x index = {0}, y index = {1}, col sep=comma]
+ {data/exp_sc4.csv};
+\addplot[thick, only marks, mark=*, yellow]
+ table[x index = {0}, y index = {1}, col sep=comma]
+ {data/exp_sc5.csv};
+\addplot[thick, only marks, mark=*, orange]
+ table[x index = {0}, y index = {1}, col sep=comma]
+ {data/exp_sc6.csv};
+\end{axis}
+\end{tikzpicture}
+\caption{Trade-Off between Energy and Error when driving
+(with signal loss). The error is normalized subtracting its minimum
+value.}
+\label{fig:car-tradeoff}
+\end{minipage}
+\end{figure*}
 
-Secondly, looking at higher triggering values instead (blue, light blue and yelow points) opposite behavior is experienced. There is smaller variance in terms of power consumption since the antenna is turned on less frequently and there will be less uncertainty on how much time is overall spent while fetching the signal. Insterad the error becomes both lager and with higher variance due to the necessity of using more the IMU data that are less reliable.
+Finally, we would like to expose the trade-off between performance
+(accuracy) and power (battery) consumption. For both the driving and
+the biking scenario, we run 30 simulations with different triggering
+thresholds for the sensor fusion algorithm (specifically, we use
+$th \in \{ 4, 5, 6, 8, 10, 12 \}$). In principle, higher values of
+the threshold guarantee a lower power consumption, at the price of
+larger position estimate errors (and viceversa).
 
-\begin{figure}[t]
- \begin{center}
-  \includegraphics[height=0.60\columnwidth, width=0.80\columnwidth]{images/car_trade_off_zoomout.png}
-  \caption{Power and tracking-error trade off with the car data.
-           \label{fig:car-trade-off}
-          }
- \end{center}
-\end{figure}
+The acquisition of satellites signals is modeled as a random variable,
+uniformly distributed in the intervals specified for the model. In the
+biking simulation, the number of visible satellites is a constant. In
+the driving simulation (more realistically), the value of the visible
+satellites is also a random variable. More in detail, for each time
+step in the simulation, there is a probability of increasing or
+decreasing the number of visible satellites (in a realistic bound
+between 3 and 6). The overall error of a trace is defined as the
+root-mean-square of the distance between the trace and the pure GPS
+signal. We also normalize (removing the minimum number encountered in
+the simulations), to highlight the trade-off.
 
-Finally figure~\ref{fig:car-trade-off} shows the simulations performed using the car data. Given the introduction of a varying number of visible satellites and therefore the possibility of losing GPS availability the behavior of the system becomes much more various. Specifically they spread radially away from the origin. This is reasonable since the loss of visibility will negatively affect both the accuracy, as the GPS data wont be available until a sufficient number of satellites become visible again, and the energy consumption, as the sensor will have to be turned on for relatively long time to re-aquire the ephemeris data. If, as shown in figure~\ref{fig:car-trade-off-zoom}, we zoom in the lower part of the plot we can see that the same behavior as the one described for the cycling data is evidenced.
+Figure~\ref{fig:bike-tradeoff} shows the simulation results for the
+cycling tracking. Different colors for the marks correspond to
+different threshold values. The figure highlights the trade-off and
+show that it is controllable using the choice of the threshold.
+Furthermore two other interesting phenomena are pointed out by this
+simulation. First, for low triggering values (blue, red, and green
+points) the variance of the error is lower, since the simulation is
+converging to always-on scenario, saturating the achievable tracking
+precision. At the same time, the energy consumption variance
+increases due to frequent changes in sensor state (with the receiver
+being affected by the time it takes to fetch satellites' signals).
+Second, higher triggering values instead (purple, yellow, and orange
+points) the opposite behavior is experienced. The variance in terms
+of energy consumption is smaller, since the antenna is turned on less
+frequently (implying that there will be less uncertainty on the time
+spent fetching satellites' signals). The error here becomes lager and
+with higher variance, due to the need to exploit more often (less
+reliable) IMU data.
 
-\begin{figure}[t]
- \begin{center}
-  \includegraphics[height=0.60\columnwidth, width=0.80\columnwidth]{images/car_trade_off_zoomin.png}
-  \caption{Zoom on the lower part of the power and tracking-error trade off with the car data.
-           \label{fig:car-trade-off-zoom}
-          }
- \end{center}
-\end{figure}
+Finally, Figure~\ref{fig:car-tradeoff} shows the simulations
+performed using the car data. Given the introduction of a varying
+number of visible satellites and therefore the possibility of losing
+GPS availability, the behavior of the system becomes much more
+diverse. Specifically, points spread radially away from the origin.
+This is reasonable, since the loss of visibility will negatively
+affect both the accuracy (as the GPS data wont be available until a
+sufficient number of satellites become visible again) and the energy
+consumption (as the sensor will have to be turned on for relatively
+long time to reacquire the ephemeris data).
diff --git a/paper/sections/07-concl.tex b/paper/sections/07-concl.tex
new file mode 100644
index 0000000000000000000000000000000000000000..5261d1d654b8d659fc92037a9e24a00d47a20c90
--- /dev/null
+++ b/paper/sections/07-concl.tex
@@ -0,0 +1,7 @@
+This paper presented a first-principle model of a GPS receiver, able
+to capture the dynamics of the data acquisition. We used the model to
+enhance a sensor fusion algorithm that merges data from intertial
+measurement sensors with the GPS trace, to improve the GPS battery
+consumption. The paper presented the model, simulation results, and
+experiments obtained with real GPS traces, and exposes the trade-off
+between Energy consumption and Positioning Accuracy.