From 3ccd5169414e81df1c9440870a31167f36dd452f Mon Sep 17 00:00:00 2001
From: Martina Maggio <maggio.martina@gmail.com>
Date: Fri, 12 Oct 2018 17:15:56 +0200
Subject: [PATCH] fixes up to sec5
---
paper/main.tex | 39 ++++---------
paper/sections/01-intro.tex | 75 +++++++++++++++++++++++++
paper/sections/03-model.tex | 103 ++++++++++++++++++-----------------
paper/sections/04-fusion.tex | 11 ++--
4 files changed, 143 insertions(+), 85 deletions(-)
create mode 100644 paper/sections/01-intro.tex
diff --git a/paper/main.tex b/paper/main.tex
index d763642..a975571 100644
--- a/paper/main.tex
+++ b/paper/main.tex
@@ -96,7 +96,15 @@ Politecnico di Milano, Italy}
\renewcommand{\shortauthors}{C. Mandrioli et al.}
\begin{abstract}
-Abstract.
+ This paper proposes a first-principle model of GPS receivers, that
+ allows us to exploit the trade-off between battery consumption and
+ positioning accuracy. We present the model and propose a GPS
+ sampling strategy that uses both the current positioning confidence,
+ and information about the GPS status. We complement the GPS sensor
+ with internal measurment units and show how the given model exposes
+ the battery-accuracy trade-off in the context of sensor fusion. We
+ demonstrate the usefulness of the proposed sampling strategy using
+ both simulation and real data.
\end{abstract}
%
@@ -123,33 +131,8 @@ Abstract.
\maketitle
\section{Introduction}
-
-\begin{itemize}
-
-\item GPS navigation consumes a lot of battery
- \todo{Find references that back up this information}.
-
-\item This is part of the reason why GPS is often used in conjunction
- with other sensors \todo{Find references that back up this
- information and discuss sensor fusion}. In fact, GPS tracking
- systems provide very precise information, that could be used when
- the uncertainty of the measurements of other (less power-hungry)
- types of sensors becomes too big.
-
-\item We have studied and modeled the GPS protocol to aid the sensor
- fusion process, and optimize for battery consumption (which is
- crucial in some circumstances, for example if the tracking system is
- mounted on drones). \textcolor{red}{The proposed model can find usage also in trajectory-based services, in which the device is supposed to sample the position in order to allow a-posteriori reconstruction of the followed path.\\ It must be noted also that the sensor presents dynamical behavior, in the sense that it can take different times from the position request to the measurement, this requires dynamical modeling.}
-
-\item This paper makes the following contributions: (1) it provides a
- model of the GPS stack behavior, identifying how it is possible to
- optimize it to save battery; (2) it complements the model with other
- sensors and shows how the GPS can enhance the accuracy of the
- provided data; (3) it develops an open-loop control strategy to
- manage the GPS receiver and evaluates its potential battery savings.
- \textcolor{red}{We should point out also that the modeling is independent from the specific sensor from the specific brand, it only relies on the technological choices that are used to implement it.(il problema si presta ad un approccio adattativo)}
-
-\end{itemize}
+\label{sec:intro}
+\input{sections/01-intro}
\section{Related Work}
\label{sec:related}
diff --git a/paper/sections/01-intro.tex b/paper/sections/01-intro.tex
new file mode 100644
index 0000000..65de673
--- /dev/null
+++ b/paper/sections/01-intro.tex
@@ -0,0 +1,75 @@
+Global Positioning System (GPS) receivers are well known to be
+power-hungry with respect to the power consumed by a small electronic
+device~\cite{bib:computation-offloading, bib:selective-tracking,
+ bib:microsoft-leap, bib:sparse-fourier, bib:enloc-smartphones,
+ bib:virtualGPS, bib:accuracy-adaptation,
+ bib:feasibility-duty-cycling, bib:traffic-delay,
+ bib:entracked-datadriven-modeling, bib:senseLess,
+ bib:framework-for-energy-efficiency,
+ bib:desing-principles-for-energy-efficiency}. This is probably the
+main reason that motivates research on the optimization of the GPS
+stack and on multi-sensor data merging, for example via sensor
+fusion~\cite{Liggins:2008}. In fact, combining the information
+provided by more than one sensor type could allow one to exploit the
+sensor benefits and to limit their drawbacks.
+
+One alternative is merging data from GPS sensors with data provided by
+intertial measurments sensors~\cite{bib:gps-imu}. While the GPS is
+power-hungry but provided very precise information, inertial
+measurments sensors are less demanding in terms of battery, but also
+less precise. In the literature, optimizations of this type are
+accompanied by experimental data~\cite{bib:accuracy-adaptation,
+bib:feasibility-duty-cycling, bib:traffic-delay,
+bib:entracked-datadriven-modeling, bib:senseLess,
+bib:framework-for-energy-efficiency}, that are time-consuming to
+retrieve and only valid for the specific testing setup.
+
+This paper follows a different approach to tackle the problem of
+optimizing power consumption for localization in GPS devices. We
+propose a first-principle model of the GPS receiver. This model
+captures the physical \emph{phenomena} that determine the sensor
+behavior. The main advantage of using such a model is its independence
+from its specific hardware and software. We capture the behavior of a
+\emph{generic} GPS receiver and its dynamics. This allows us to test
+strategies for power optimization and to obtain insights on its
+effectiveness before implementing them on a real device. We envision
+our work to provide contributions in the design of GPS-assisted
+navigation strategies for devices with battery constraints, from
+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
+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, a GPS sensor that
+receive the same input data can behave differently, depending on its
+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
+intertial measurment sensors. While this is not a new idea, thanks to
+our model we are able to capture the trade-offs between the different
+sensor types programmatically and to exploit the characteristics of
+each sensor.
+\end{itemize}
+%
+This paper is organized as follows. As much research has been done on
+the topic of GPS optimization, Section~\ref{sec:related} describes
+related work. Section~\ref{sec:gps} describes the physical principles
+behind the GPS receiver and shows how we capture these principles with
+our model. Section~\ref{sec:fusion} discusses a sensor fusion
+algorithm that merges the information obtained by Intertial
+Measurement Units (IMUs) with the GPS data. The sampling strategy that
+is derived using the given models is described in
+Section~\ref{sec:control}. We evaluate our proposal with data from
+real GPS and IMU devices and simulated traces in
+Section~\ref{sec:experiments}. Finally, Section~\ref{sec:concl}
+concludes the paper.
\ No newline at end of file
diff --git a/paper/sections/03-model.tex b/paper/sections/03-model.tex
index 90d79a1..0333ba1 100644
--- a/paper/sections/03-model.tex
+++ b/paper/sections/03-model.tex
@@ -3,30 +3,6 @@ paper. Specifically, Section~\ref{sec:gps:phy} describes the physics
behind the model and Section~\ref{sec:gps:mod} discusses our modelling
choices.
-\subsection{GPS physics}
-\label{sec:gps:phy}
-
-GPS sensors locate themselves through a process called
-\emph{trilateration}~\cite{bib:gps-book}. This process consists in
-measuring the distance from 4 or more points in space (satellites),
-whose position is known. Given the distance measurements, the GPS
-sensor then performs a least square estimation to determine its
-current position. Figure~\ref{fig:globe} shows an example with five
-satellites. To correctly estimate the current position, the GPS
-receiver must measure the distance from $s_1, s_2, s_3$ and $s_4$.
-Additionally, measuring the distance from $s_5$ is not necessary, but
-improves the position accuracy.
-
-The GPS framework includes (circa) 30 satellites. These satellites
-orbit around the Earth following known trajectories. While orbiting,
-they broadcast periodic signals that encode a set of parameters,
-called \emph{ephemeris data}. The emphemeris data describe the
-satellites' orbits (see for example the trajectory of satelite $s_3$
-in Figure~\ref{fig:globe}), and therefore allow the GPS receiver to
-accurately determine their position in time. The satellite
-trajectories are not constant in time, due to uncertainties and
-disturbances, like corrections for collision avoidance.
-
\begin{figure}
\centering
\scalebox{0.6}{%
@@ -66,6 +42,30 @@ disturbances, like corrections for collision avoidance.
\label{fig:globe}
\end{figure}
+\subsection{GPS physics}
+\label{sec:gps:phy}
+
+GPS sensors locate themselves through a process called
+\emph{trilateration}~\cite{bib:gps-book}. This process consists in
+measuring the distance from 4 or more points in space (satellites),
+whose position is known. Given the distance measurements, the GPS
+sensor then performs a least square estimation to determine its
+current position. Figure~\ref{fig:globe} shows an example with five
+satellites. To correctly estimate the current position, the GPS
+receiver must measure the distance from $s_1, s_2, s_3$ and $s_4$.
+Additionally, measuring the distance from $s_5$ is not necessary, but
+improves the position accuracy.
+
+The GPS framework includes (circa) 30 satellites. These satellites
+orbit around the Earth following known trajectories. While orbiting,
+they broadcast periodic signals that encode a set of parameters,
+called \emph{ephemeris data}. The emphemeris data describe the
+satellites' orbits (see for example the trajectory of satelite $s_3$
+in Figure~\ref{fig:globe}), and therefore allow the GPS receiver to
+accurately determine their position in time. The satellite
+trajectories are not constant in time, due to uncertainties and
+disturbances, like corrections for collision avoidance.
+
The ephemeris data expire after 30 minutes, i.e., after 30 minutes
they are not considered valid anymore. To correctly estimate the
current position, the receiver should ensure that the ephemeris data
@@ -76,6 +76,26 @@ 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 is
turned on right after the start of a new message transmission).
+% --------------------------------------------------------------------
+\begin{table*}[t]
+\caption{Combinations of Ephemeris Data, Ranging Data and Antenna Status.}
+\label{fig:states}
+\centering
+\begin{tabular}{lcccc}
+\hline\hline
+\textbf{State Name} & \textbf{Ephemeris Data} & \textbf{Ranging Data} & \textbf{Antenna} & \textbf{State Number} \\ \hline\hline
+Position Available & Available & Available & On & \textcircled{\scriptsize 4} \\ \hline
+--- & \st{Available} & \st{Available} & \st{Off} & \\ \hline
+Warm Start & Available & Not Available & On & \textcircled{\scriptsize 5} \\ \hline
+Warm Start Available & Available & Not Available & Off & \textcircled{\scriptsize 6} \\ \hline
+Read Ephemeris Data & Not Available & Available & On & \textcircled{\scriptsize 3} \\ \hline
+--- & \st{Not Available} & \st{Available} & \st{Off} & \\ \hline
+Cold Start & Not Available & Not Available & On & \textcircled{\scriptsize 2} \\ \hline
+Not Available Info & Not Available & Not Available & Off & \textcircled{\scriptsize 1} \\ \hline\hline
+\end{tabular}
+\end{table*}
+% --------------------------------------------------------------------
+
All the satellites transmit on the same frequency and then the
different signals are multiplexed using the Code Division Multiple
Access (CDMA) technique. Using CDMA, the signal has three components:
@@ -117,26 +137,15 @@ includes the measurement of the ranging data\footnote{Additionally,
ephemeris data.}. This process takes some milleseconds (usually in
the range from $2ms$ to $10ms$), depending mainly on signal stength.
-% --------------------------------------------------------------------
+\subsection{GPS modeling}
+\label{sec:gps:mod}
+
+We plan to use a model of the GPS receiver behavior to optimize
+battery consumption subject to position data availability. Our
+receiver model needs to capture two relevant \emph{phenomena}: GPS
+position availability and power consumption.
+
\begin{table*}[t]
-\caption{Combinations of Ephemeris Data, Ranging Data and Antenna Status.}
-\label{fig:states}
-\centering
-\begin{tabular}{lcccc}
-\hline\hline
-\textbf{State Name} & \textbf{Ephemeris Data} & \textbf{Ranging Data} & \textbf{Antenna} & \textbf{State Number} \\ \hline\hline
-Position Available & Available & Available & On & \textcircled{\scriptsize 4} \\ \hline
---- & \st{Available} & \st{Available} & \st{Off} & \\ \hline
-Warm Start & Available & Not Available & On & \textcircled{\scriptsize 5} \\ \hline
-Warm Start Available & Available & Not Available & Off & \textcircled{\scriptsize 6} \\ \hline
-Read Ephemeris Data & Not Available & Available & On & \textcircled{\scriptsize 3} \\ \hline
---- & \st{Not Available} & \st{Available} & \st{Off} & \\ \hline
-Cold Start & Not Available & Not Available & On & \textcircled{\scriptsize 2} \\ \hline
-Not Available Info & Not Available & Not Available & Off & \textcircled{\scriptsize 1} \\ \hline\hline
-\end{tabular}
-\end{table*}
-% --------------------------------------------------------------------
-\begin{table*}
\caption{Transitions, summary table with name, conditions and eventual updates.}
\label{fig:transitions}
\centering
@@ -224,14 +233,6 @@ font=\footnotesize]
\end{figure*}
% --------------------------------------------------------------------
-\subsection{GPS modeling}
-\label{sec:gps:mod}
-
-We plan to use a model of the GPS receiver behavior to optimize
-battery consumption subject to position data availability. Our
-receiver model needs to capture two relevant \emph{phenomena}: GPS
-position availability and power consumption.
-
The GPS receiver provides a position estimate when it has collected
both the ephemeris and the ranging data for at least 4 satellites.
Detecing data from more than 4 satellites improves the positioning
diff --git a/paper/sections/04-fusion.tex b/paper/sections/04-fusion.tex
index 4362c77..34312df 100644
--- a/paper/sections/04-fusion.tex
+++ b/paper/sections/04-fusion.tex
@@ -14,12 +14,11 @@ gyroscope. The accelerometer measures the acceleration along the three
axis while the gyroscope measures the angular velocity of the
orientation changes. IMUs can be considered complementary to the
GPS. In fact, they provide a relative measurement (while the GPS
-position is absolute), and are characterized by low power
-consumption~\todo{ref}, since they do not consume the extra power
-needed to use an antenna. Their measurements are also continuously
-available, since they do not rely on other devices. A navigation
-system only based on IMUs is referred to as an Inertial Navigation
-System (INS).
+position is absolute), and are characterized by low power consumption,
+since they do not consume the extra power needed to use an
+antenna. Their measurements are also continuously available, since
+they do not rely on other devices. A navigation system only based on
+IMUs is referred to as an Inertial Navigation System (INS).
The main weakness of an INS is that, given the differential nature of
the sensors, their measurements must be integrated. This exposes the
--
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