fix Claudios comments

paper/sections/01-intro.tex

@@ -40,24 +40,25 @@ activity trackers to tracking systems mounted on drones.
 Specifically, this paper makes the following contributions:
 %
 \begin{itemize}
-\item \textbf{Modeling:} It provides a \emph{first-principle} model of
-the GPS behavior, identifying the dynamics that regulate it. A
+\item \textbf{Modeling:} It provides a \emph{first-principle} model
+of the GPS behavior, identifying the dynamics that regulate it. A
 first-principle model is a model that captures the technological
 design choices that are behind the GPS system. These choices greatly
 influence what can be achieved with any GPS sensor, as they introduce
 basic limitations and characteristics of the technology. In this
 specific context, we highlight how a dynamical model is necessary to
 capture the involved \emph{phenomena}. In fact, GPS sensors that
-receive the same \textcolor{red}{\emph{stimula}} can behave differently, depending on the
-sensor's internal state.
+receive the same \emph{stimula} can behave differently, depending on
+the sensor's internal state.
 \item \textbf{Design:} It identifies opportunities for battery
 savings. Specifically, modeling the GPS-related \emph{phenomena}
 allows us to devise a sampling strategy that exploits the technology
 characteristics.
-\item \textbf{Integration:} It integrates the GPS with an ecosystem of
-inertial measurement sensors. While this is not a new idea, thanks to
-our model we are able to capture the trade-offs \textcolor{red}{of the different merging algorithms programmatically and to expose the characteristics of
-each solution}.
+\item \textbf{Integration:} It integrates the GPS with an ecosystem
+of inertial measurement sensors. While this is not a new idea, thanks
+to our model we are able to capture the trade-offs (of the different
+merging algorithms) programmatically and to expose the characteristics
+of each solution.
 \end{itemize}
 %
 This paper is organized as follows. As much research has been done on

paper/sections/02-related-work.tex

@@ -10,7 +10,7 @@ The first class includes results like~\cite{7528057,
 bib:computation-offloading, bib:selective-tracking,
 bib:microsoft-leap, bib:sparse-fourier}. The authors of
 \cite{bib:computation-offloading} aim at outsourcing the device
-computation (once the data has been \textcolor{red}{retrieved}) to some server, using a
+computation (once the data has been retrieved) to some server, using a
 network connection. \cite{bib:selective-tracking} improves the GPS
 receiver power-efficiency selecting only a subset of visible
 satellites to be tracked. Other works aim at improving the speed of
@@ -27,7 +27,8 @@ trade-off controller, that trades accuracy for energy consumption. In
 the same class we can include works that exploit other sensors. When
 the adaptation layer detects that the user state does not need high
 accuracy, it minimizes the GPS receiver usage by turning it off and
-enabling it again only on demand \textcolor{red}{or eventually switching to other positioning techniques}~\cite{bib:feasibility-duty-cycling,
+enabling it again only on demand or eventually switching to other
+positioning techniques~\cite{bib:feasibility-duty-cycling,
   bib:traffic-delay, bib:entracked-datadriven-modeling, bib:senseLess,
   bib:framework-for-energy-efficiency}. Among the works on this
 additional adaptation
@@ -35,7 +36,7 @@ layer,~\cite{bib:desing-principles-for-energy-efficiency} proposes a
 set of design principles for smartphone applications, to improve the
 smartphone battery efficiency.
 
-This work has a complementarly role with respect to the ones mentioned
+This work has a complementary role with respect to the ones mentioned
 above. We propose here a modeling approach based only on how the GPS
 receiver is designed. This is therefore transversal with respect to
 the implementation details of the specific sensor. We argue that we
@@ -44,7 +45,7 @@ first principled way, using this model. We can also determine how
 different factors (including satellite visibility, and timing
 behaviors) affect the receiver.
 
-Modeling GPS sensors is not a new reserach area, see for
+Modeling GPS sensors is not a new research area, see for
 example~\cite{bib:selective-tracking,
   bib:entracked-datadriven-modeling}. These prior efforts are mainly
 data-driven, i.e., they collect data for a specific receiver with a

paper/sections/03-model.tex

@@ -96,9 +96,11 @@ they are not considered valid anymore. To correctly estimate the
 current position, the receiver should ensure that the ephemeris data
 are frequently updated. The transmission of the ephemeris data has a
 duration of 30 seconds, and the satellites continuously broadcast new
-data. In order to ensure the correct acquisition of one data point, the
-receiver then has to fetch and decode the signal for a time that is in
-the interval $[30,60)$ seconds (in the worst case, the receiver \textcolor{red}{starts reading the message} right after the start of a new message transmission).
+data. In order to ensure the correct acquisition of one data point,
+the receiver then has to fetch and decode the signal for a time that
+is in the interval $[30,60)$ seconds (in the worst case, the receiver
+starts reading the message right after the start of a new message
+transmission).
 
 All the satellites transmit on the same frequency and then the
 different signals are multiplexed using the Code Division Multiple
@@ -240,13 +242,12 @@ accuracy. As for power consumption, the receiver always consumes a
 additional power when its radio is turned on, which is precisely the
 cause of battery draining. This power has been experimentally shown to
 be constant in time~\cite{bib:enloc-smartphones, bib:microsoft-leap}
-<<<<<<< HEAD
-and usually around 400mW\footnote{This quantity apparently depends on the specific device. It can of course be changed according to the given use-case. Moreover for the simple evaluation of the trade-off it is not strictly relevant since we would be interested in the differential values. If instead is required an absolute estimation of the consumed energy, then a precise evaluation of this quantity is required.}. Our model needs to capture whether:
-=======
+%<<<<<<< HEAD
+%and usually around 400mW\footnote{This quantity apparently depends on the specific device. It can of course be changed according to the given use-case. Moreover for the simple evaluation of the trade-off it is not strictly relevant since we would be interested in the differential values. If instead is required an absolute estimation of the consumed energy, then a precise evaluation of this quantity is required.}. Our model needs to capture whether:
+%=======
 and usually between 20mW and 400mW. We use the latter for our model,
 but this is just a constant that can be changed depending on the
 device. The important states that our model needs to capture are:
->>>>>>> ee578b1defc31eed95f6615dbf639fb706f295b5
 \begin{enumerate}
   \item \emph{ephemeris data} are available or not;
   \item \emph{ranging data} are available or not;
@@ -286,7 +287,7 @@ loss of information with respect to our model usage) we do not include
 the specific tracking of different satellites in the model and,
 consequently, we do not distinguish between these two cases. The
 transition \texttt{ephemeris\_expire} implements both. The ranging 
-data \textcolor{red}{instead} become available as soon as the satellites' signals are
+data instead become available as soon as the satellites' signals are
 fetched. We refer to this transition as
 \texttt{fetch\_freq\&phase}. The loss of ranging data can have two
 causes: (i) the antenna is turned off (transition \texttt{turn\_off}),

paper/sections/06-results.tex

@@ -195,7 +195,8 @@ increase the complexity of the model and decrease it usability. An
 extension of the model to include also this phenomenon would not be
 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 \textcolor{red}{but are synchronized in the antenna's state}.
+independently capture the tracking of individual satellites but are
+synchronized in the antenna's state.
 
 \subsection{Positioning Accuracy}
 \label{sec:res:accuracy}
@@ -413,8 +414,7 @@ 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. \textcolor{red}{We also normalize (removing the minimum number encountered in
-the simulations), to highlight the trade-off.(NO MORE)}
+signal.
 
 \begin{figure*}
 \centering
@@ -432,8 +432,6 @@ the simulations), to highlight the trade-off.(NO MORE)}
  ylabel = {Error},
  legend style={at={(1.3,1.1)},anchor=south},
  legend columns=3,
- ytick = {0, 0.01, 0.02, 0.03},
- yticklabels = {45493.25, 45493.26, 45493.27, 45493.28},
 ]
 \pgfkeys{/pgf/number format/.cd,1000 sep={}}
 \addplot[thick, only marks, mark=*, blue]
@@ -534,5 +532,6 @@ 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). \textcolor{red}{Still, if we look only at the simulations where no visiblity-loss happens, the same behavior is exposed.}
-
+long time to reacquire the ephemeris data). Still, the same behavior
+is detectable looking only at the simulations where no satellite
+visibility loss event happens.