Commit f8ace013 authored by Martina Maggio's avatar Martina Maggio
Browse files

fix Claudios comments

parent 16a46e2f
......@@ -40,24 +40,25 @@ activity trackers to tracking systems mounted on drones.
Specifically, this paper makes the following contributions:
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\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
......
......@@ -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
......
......@@ -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}),
......
......@@ -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.
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