\end{enumerate}
-\section{Energy Consumption Models:}
+\section{Energy Consumption Modeling:}
\label{ch1:sec:9}
\indent The WSNs have been received a lot of interest because their low energy consumption sensor nodes. Since the sensor node has a limited power battery; therefore, one of the most critical issues in WSNs is how to reduce the energy consumption of sensor nodes so as to prolong the network lifetime as long as possible. In order to model the energy consumption, four states for a sensor node have been used~\cite{ref140}: transmission, reception, listening, and sleeping; in addition, two states that should be taken into account: computation and sensed data acquisition. The main tasks of each of these states include:
\end{enumerate}
-In this section, two energy consumption models are explained. The first model called radio energy dissipation model and the second model represent our energy consumption model, which has been used by the proposed protocols in this dissertation.
+%In this section, two energy consumption models are explained. The first model called radio energy dissipation model and the second model represent our energy consumption model, which has been used by the proposed protocols in this dissertation.
-\subsection{Radio Energy Dissipation Model:}
-\label{ch1:sec9:subsec1}
+%\subsection{Radio Energy Dissipation Model:}
+%\label{ch1:sec9:subsec1}
\indent Since the communication unit is the most energy-consuming part inside the sensor node, therefore, many authors are used the radio energy dissipation model that proposed in~\cite{ref109,ref110} as energy consumption model during the simulation and evaluation of their works in WSNs. Figure~\ref{RDM} shows the radio energy dissipation model.
\begin{figure}[h!]
\centering
\indent The radio energy dissipation model have been considered only the energy, which is consumed by the communication part inside the sensor node; however, in order to achieve a more accurate model, it is necessary to take into account the energy is consumed by the other parts inside the sensor node such as: computation unit and sensing unit.
-\subsection{Our Energy Consumption Model:}
-\label{ch1:sec9:subsec2}
-\indent In this dissertation, the coverage protocols have been used an energy consumption model proposed by~\cite{ref111} and based on \cite{ref112} with slight modifications. The energy consumption for sending/receiving the packets is added, whereas the part related to the sensing range is removed because we consider a fixed sensing range.
-
-\indent For our energy consumption model, we refer to the sensor node Medusa~II which uses an Atmels AVR ATmega103L microcontroller~\cite{ref112}. The typical architecture of a sensor is composed of four subsystems: the MCU subsystem which is capable of computation, communication subsystem (radio) which is responsible for transmitting/receiving messages, the sensing subsystem that collects data, and the power supply which powers the complete sensor node \cite{ref112}. Each of the first three subsystems can be turned on or off depending on the current status of the sensor. Energy consumption (expressed in milliWatt per second) for the different status of the sensor is summarized in Table~\ref{table1}.
-
-\begin{table}[ht]
-\caption{The Energy Consumption Model}
-% title of Table
-\centering
-% used for centering table
-\begin{tabular}{|c|c|c|c|c|}
-% centered columns (4 columns)
- \hline
-%inserts double horizontal lines
-Sensor status & MCU & Radio & Sensing & Power (mW) \\ [0.5ex]
-\hline
-% inserts single horizontal line
-LISTENING & on & on & on & 20.05 \\
-% inserting body of the table
-\hline
-ACTIVE & on & off & on & 9.72 \\
-\hline
-SLEEP & off & off & off & 0.02 \\
-\hline
-COMPUTATION & on & on & on & 26.83 \\
-%\hline
-%\multicolumn{4}{|c|}{Energy needed to send/receive a 1-bit} & 0.2575\\
- \hline
-\end{tabular}
-
-\label{table1}
-% is used to refer this table in the text
-\end{table}
-
-\indent For the sake of simplicity we ignore the energy needed to turn on the radio, to start up the sensor node, to move from one status to another, etc. Thus, when a sensor becomes active (i.e., it has already chosen its status), it can turn its radio off to save battery. The value of energy spent to send a 1-bit-content message is obtained by using the equation in ~\cite{ref112} to calculate the energy cost for transmitting messages and we propose the same value for receiving the packets. The energy needed to send or receive a 1-bit packet is equal to $0.2575~mW$.
+%\subsection{Our Energy Consumption Model:}
+%\label{ch1:sec9:subsec2}
\section{Conclusion}
\label{ch1:sec:10}
-\indent In this chapter, an overview about the wireless sensor networks have been presented that represent our focus in this dissertation. The structure of the the typical wireless sensor network and the main components of the sensor nodes have been demonstrated. Several types of wireless sensor networks are described. Various fields of applications covering a wide spectrum for a WSNs have been presented, including health, home, environmental, military, and industrial applications. As demonstrated, since sensor nodes have limited battery life; since it is impossible to replace batteries, especially in remote and hostile environments; the limited power of a battery represents the critical challenge in WSNs. The main challenges in WSNs have been explained; on the other hand, the energy efficient solutions have been proposed in order to handle these challenges through energy conservation to prolong the network lifetime. Many energy efficient mechanisms have been illustrated, which are aimed to reduce the energy consumption by the different units of the wireless sensor nodes in WSNs. The definition of the network lifetime has been presented and in different contexts. The problem of the coverage is explained, where constructing energy efficient coverage protocols one of the main scientific research challenges in WSNs. This chapter highlights the main design issues for the coverage problems that need to be considered during designing a coverage protocol for WSNs. In addition, some energy consumption models have been demonstrated.
+\indent In this chapter, an overview about the wireless sensor networks have been presented that represent our focus in this dissertation. The structure of the the typical wireless sensor network and the main components of the sensor nodes have been demonstrated. Several types of wireless sensor networks are described. Various fields of applications covering a wide spectrum for a WSNs have been presented, including health, home, environmental, military, and industrial applications. As demonstrated, since sensor nodes have limited battery life; since it is impossible to replace batteries, especially in remote and hostile environments; the limited power of a battery represents the critical challenge in WSNs. The main challenges in WSNs have been explained; on the other hand, the energy efficient solutions have been proposed in order to handle these challenges through energy conservation to prolong the network lifetime. Many energy efficient mechanisms have been illustrated, which are aimed to reduce the energy consumption by the different units of the wireless sensor nodes in WSNs. The definition of the network lifetime has been presented and in different contexts. The problem of the coverage is explained, where constructing energy efficient coverage protocols one of the main scientific research challenges in WSNs. This chapter highlights the main design issues for the coverage problems that need to be considered during designing a coverage protocol for WSNs. In addition, the energy consumption Modeling have been demonstrated.
interest of $(50 \times 25)~m^2 $ in such a way that they cover the field with a
high coverage ratio.
-We chose as energy consumption model the one described in chapter 1, section \ref{ch1:sec9:subsec2}. Each node has an initial energy level, in Joules, which is randomly drawn in $[500-700]$. If its energy provision reaches a value below the threshold $E_{th}=36$~Joules, the minimum energy needed for a node to stay active during
-one period, it will no longer take part in the coverage task. This value corresponds to the energy needed by the sensing phase, obtained by multiplying the energy consumed in active state (9.72 mW) by the time in seconds for one period (3,600 seconds), and adding the energy for the pre-sensing phases.
-According to the interval of initial energy, a sensor may be active during at most 20 periods.
+
+
+\subsection{Energy Consumption Model}
+\label{ch3:sec:04:02}
+
+\indent In this dissertation, we have used an energy consumption model proposed by~\cite{ref111} and based on \cite{ref112} with slight modifications. The energy consumption for sending/receiving the packets is added, whereas the part related to the sensing range is removed because we consider a fixed sensing range.
+
+\indent For our energy consumption model, we refer to the sensor node Medusa~II which uses an Atmels AVR ATmega103L microcontroller~\cite{ref112}. The typical architecture of a sensor is composed of four subsystems: the MCU subsystem which is capable of computation, communication subsystem (radio) which is responsible for transmitting/receiving messages, the sensing subsystem that collects data, and the power supply which powers the complete sensor node \cite{ref112}. Each of the first three subsystems can be turned on or off depending on the current status of the sensor. Energy consumption (expressed in milliWatt per second) for the different status of the sensor is summarized in Table~\ref{table1}.
+
+\begin{table}[ht]
+\caption{The Energy Consumption Model}
+% title of Table
+\centering
+% used for centering table
+\begin{tabular}{|c|c|c|c|c|}
+% centered columns (4 columns)
+ \hline
+%inserts double horizontal lines
+Sensor status & MCU & Radio & Sensing & Power (mW) \\ [0.5ex]
+\hline
+% inserts single horizontal line
+LISTENING & on & on & on & 20.05 \\
+% inserting body of the table
+\hline
+ACTIVE & on & off & on & 9.72 \\
+\hline
+SLEEP & off & off & off & 0.02 \\
+\hline
+COMPUTATION & on & on & on & 26.83 \\
+%\hline
+%\multicolumn{4}{|c|}{Energy needed to send/receive a 1-bit} & 0.2575\\
+ \hline
+\end{tabular}
+
+\label{table1}
+% is used to refer this table in the text
+\end{table}
+
+\indent For the sake of simplicity we ignore the energy needed to turn on the radio, to start up the sensor node, to move from one status to another, etc. Thus, when a sensor becomes active (i.e., it has already chosen its status), it can turn its radio off to save battery. The value of energy spent to send a 1-bit-content message is obtained by using the equation in ~\cite{ref112} to calculate the energy cost for transmitting messages and we propose the same value for receiving the packets. The energy needed to send or receive a 1-bit packet is equal to $0.2575~mW$.
+
+
+%We have used an energy consumption model, which is presented in chapter 1, section \ref{ch1:sec9:subsec2}.
+
+The initial energy of each node is randomly set in the interval $[500;700]$. A sensor node will not participate in the next round if its remaining energy is less than $E_{th}=36~\mbox{Joules}$, the minimum energy needed for the node to stay alive during one round. This value has been computed by multiplying the energy consumed in active state (9.72 mW) by the time in second for one round (3600 seconds), and adding the energy for the pre-sensing phases. According to the interval of initial energy, a sensor may be alive during at most 20 rounds.
\subsection{Performance Metrics}
-\label{ch3:sec:04:02}
+\label{ch3:sec:04:03}
In the simulations, we introduce the following performance metrics to evaluate
the efficiency of our approach:
\subsection{Performance Analysis for Different Subregions}
-\label{ch3:sec:04:03}
+\label{ch3:sec:04:04}
In this subsection, we are studied the performance of our DiLCO protocol for a different number of subregions (Leaders).
The DiLCO-1 protocol is a centralized approach on all the area of the interest, while DiLCO-2, DiLCO-4, DiLCO-8, DiLCO-16 and DiLCO-32 are distributed on two, four, eight, sixteen, and thirty-two subregions respectively. We did not take the DiLCO-1 protocol in our simulation results because it need high execution time to give the decision leading to consume all it's energy before producing the solution for optimization problem.
smaller areas, called subregions, and then our MuDiLCO protocol will be
implemented in each subregion in a distributed way.
-As can be seen in Figure~\ref{fig2}, our protocol works in periods fashion,
+As can be seen in Figure~\ref{fig2}, our protocol works in periods fashion,
where each is divided into 4 phases: Information~Exchange, Leader~Election,
Decision, and Sensing. Each sensing phase may be itself divided into $T$ rounds
and for each round a set of sensors (a cover set) is responsible for the sensing
-task. In this way a multiround optimization process is performed during each
+task. In this way a multiround optimization process is performed during each
period after Information~Exchange and Leader~Election phases, in order to
produce $T$ cover sets that will take the mission of sensing for $T$ rounds.
\begin{figure}[ht!]
This protocol minimizes the impact of unexpected node failure (not due to batteries
running out of energy), because it works in periods.
- On the one hand, if a node failure is detected before making the
-decision, the node will not participate to this phase, and, on the other hand,
-if the node failure occurs after the decision, the sensing task of the network
-will be temporarily affected: only during the period of sensing until a new
-period starts.
+ On the one hand, if a node failure is detected before making the decision, the node will not participate to this phase, and, on the other hand, if the node failure occurs after the decision, the sensing task of the network will be temporarily affected: only during the period of sensing until a new period starts.
-The energy consumption and some other constraints can easily be taken into
-account, since the sensors can update and then exchange their information
-(including their residual energy) at the beginning of each period. However, the
-pre-sensing phases (Information Exchange, Leader Election, and Decision) are
-energy consuming for some nodes, even when they do not join the network to
-monitor the area.
+The energy consumption and some other constraints can easily be taken into account, since the sensors can update and then exchange their information (including their residual energy) at the beginning of each period. However, the pre-sensing phases (Information Exchange, Leader Election, and Decision) are energy consuming for some nodes, even when they do not join the network to monitor the area.
These phases can be described in more details as follow:
\subsection{Decision phase}
\label{ch4:sec:02:02:03}
-Each WSNL will solve an integer program to select which cover sets will be
+Each WSNL will solve an integer program to select which cover sets will be
activated in the following sensing phase to cover the subregion to which it
belongs. The integer program will produce $T$ cover sets, one for each round.
The WSNL will send an Active-Sleep packet to each sensor in the subregion based
the benefit from the optimization and the execution time needed to solve
it. Therefore, we have set the number of subregions to 16 rather than 32.
-We have used an energy consumption model, which is presented in chapter 1, section \ref{ch1:sec9:subsec2}. The initial energy of each node is randomly set in the interval $[500;700]$. A sensor node will not participate in the next round if its remaining energy is less than $E_{R}=36~\mbox{Joules}$, the minimum energy needed for the node to stay alive during one round. This value has been computed by multiplying the energy consumed in active state (9.72 mW) by the time in second for one round (3600 seconds). According to the interval of initial energy, a sensor may be alive during at most 20 rounds.
+We have used an energy consumption model, which is presented in chapter 3, section \ref{ch3:sec:04:02}.
+
+%The initial energy of each node is randomly set in the interval $[500;700]$. A sensor node will not participate in the next round if its remaining energy is less than $E_{R}=36~\mbox{Joules}$, the minimum energy needed for the node to stay alive during one round. This value has been computed by multiplying the energy consumed in active state (9.72 mW) by the time in second for one round (3600 seconds). According to the interval of initial energy, a sensor may be alive during at most 20 rounds.
\subsection{Metrics}
\label{ch4:sec:03:02}
\item We add an improved model of energy consumption to assess the efficiency of our protocols as well as we conducted extensive simulation experiments, using the discrete event simulator OMNeT++, to demonstrate the efficiency of our protocols. We compared our proposed distributed optimization protocols to two approaches found in the literature: DESK~\cite{DESK} and GAF~\cite{GAF}, simulation results based on multiple criteria (energy consumption, coverage ratio, network lifetime and so on) show that the proposed protocols can prolong efficiently the network lifetime and improve the coverage performance.
-
-
\end{enumerate}
%In this dissertation, analytical, as well as computational, methods are used.
-
% \section{ Refereed Journal and Conference Publications}
\section{Dissertation Outline}
\include{CHAPITRE_04}
%%
-%\include{CHAPITRE_05}
+\include{CHAPITRE_05}
\part{Conclusions and Perspectives}
%% Conclusion et perspectives
%\include{CONCLUSION}
\contentsline {section}{\numberline {1.7}Network Lifetime in Wireless Sensor Networks}{18}{section.1.7}
\contentsline {section}{\numberline {1.8}Coverage in Wireless Sensor Networks }{19}{section.1.8}
\contentsline {section}{\numberline {1.9}Design Issues for Coverage Problems:}{20}{section.1.9}
-\contentsline {section}{\numberline {1.10}Energy Consumption Models:}{21}{section.1.10}
-\contentsline {subsection}{\numberline {1.10.1}Radio Energy Dissipation Model:}{22}{subsection.1.10.1}
-\contentsline {subsection}{\numberline {1.10.2}Our Energy Consumption Model:}{23}{subsection.1.10.2}
-\contentsline {section}{\numberline {1.11}Conclusion}{24}{section.1.11}
+\contentsline {section}{\numberline {1.10}Energy Consumption Modeling:}{21}{section.1.10}
+\contentsline {section}{\numberline {1.11}Conclusion}{23}{section.1.11}
\contentsline {chapter}{\numberline {2}Related Literatures}{25}{chapter.2}
\contentsline {section}{\numberline {2.1}Introduction}{25}{section.2.1}
\contentsline {section}{\numberline {2.2}Coverage Algorithms}{26}{section.2.2}
\contentsline {section}{\numberline {3.3}COVERAGE PROBLEM FORMULATION}{41}{section.3.3}
\contentsline {section}{\numberline {3.4}Simulation Results and Analysis}{43}{section.3.4}
\contentsline {subsection}{\numberline {3.4.1}Simulation Framework}{43}{subsection.3.4.1}
-\contentsline {subsection}{\numberline {3.4.2}Performance Metrics}{44}{subsection.3.4.2}
-\contentsline {subsection}{\numberline {3.4.3}Performance Analysis for Different Subregions}{45}{subsection.3.4.3}
-\contentsline {subsubsection}{\numberline {3.4.3.1}Coverage Ratio}{45}{subsubsection.3.4.3.1}
-\contentsline {subsubsection}{\numberline {3.4.3.2}Active Sensors Ratio}{46}{subsubsection.3.4.3.2}
-\contentsline {subsubsection}{\numberline {3.4.3.3}The percentage of stopped simulation runs}{47}{subsubsection.3.4.3.3}
-\contentsline {subsubsection}{\numberline {3.4.3.4}The Energy Consumption}{47}{subsubsection.3.4.3.4}
-\contentsline {subsubsection}{\numberline {3.4.3.5}Execution Time}{49}{subsubsection.3.4.3.5}
-\contentsline {subsubsection}{\numberline {3.4.3.6}The Network Lifetime}{49}{subsubsection.3.4.3.6}
-\contentsline {subsection}{\numberline {3.4.4}Performance Analysis for Primary Point Models}{50}{subsection.3.4.4}
-\contentsline {subsubsection}{\numberline {3.4.4.1}Coverage Ratio}{51}{subsubsection.3.4.4.1}
-\contentsline {subsubsection}{\numberline {3.4.4.2}Active Sensors Ratio}{51}{subsubsection.3.4.4.2}
-\contentsline {subsubsection}{\numberline {3.4.4.3}The percentage of stopped simulation runs}{52}{subsubsection.3.4.4.3}
-\contentsline {subsubsection}{\numberline {3.4.4.4}The Energy Consumption}{53}{subsubsection.3.4.4.4}
-\contentsline {subsubsection}{\numberline {3.4.4.5}Execution Time}{54}{subsubsection.3.4.4.5}
-\contentsline {subsubsection}{\numberline {3.4.4.6}The Network Lifetime}{54}{subsubsection.3.4.4.6}
-\contentsline {subsection}{\numberline {3.4.5}Performance Comparison with other Approaches}{56}{subsection.3.4.5}
-\contentsline {subsubsection}{\numberline {3.4.5.1}Coverage Ratio}{56}{subsubsection.3.4.5.1}
-\contentsline {subsubsection}{\numberline {3.4.5.2}Active Sensors Ratio}{57}{subsubsection.3.4.5.2}
-\contentsline {subsubsection}{\numberline {3.4.5.3}The percentage of stopped simulation runs}{57}{subsubsection.3.4.5.3}
-\contentsline {subsubsection}{\numberline {3.4.5.4}The Energy Consumption}{58}{subsubsection.3.4.5.4}
-\contentsline {subsubsection}{\numberline {3.4.5.5}The Network Lifetime}{59}{subsubsection.3.4.5.5}
-\contentsline {section}{\numberline {3.5}Conclusion}{60}{section.3.5}
+\contentsline {subsection}{\numberline {3.4.2}Energy Consumption Model}{44}{subsection.3.4.2}
+\contentsline {subsection}{\numberline {3.4.3}Performance Metrics}{44}{subsection.3.4.3}
+\contentsline {subsection}{\numberline {3.4.4}Performance Analysis for Different Subregions}{46}{subsection.3.4.4}
+\contentsline {subsubsection}{\numberline {3.4.4.1}Coverage Ratio}{46}{subsubsection.3.4.4.1}
+\contentsline {subsubsection}{\numberline {3.4.4.2}Active Sensors Ratio}{47}{subsubsection.3.4.4.2}
+\contentsline {subsubsection}{\numberline {3.4.4.3}The percentage of stopped simulation runs}{47}{subsubsection.3.4.4.3}
+\contentsline {subsubsection}{\numberline {3.4.4.4}The Energy Consumption}{48}{subsubsection.3.4.4.4}
+\contentsline {subsubsection}{\numberline {3.4.4.5}Execution Time}{49}{subsubsection.3.4.4.5}
+\contentsline {subsubsection}{\numberline {3.4.4.6}The Network Lifetime}{49}{subsubsection.3.4.4.6}
+\contentsline {subsection}{\numberline {3.4.5}Performance Analysis for Primary Point Models}{51}{subsection.3.4.5}
+\contentsline {subsubsection}{\numberline {3.4.5.1}Coverage Ratio}{51}{subsubsection.3.4.5.1}
+\contentsline {subsubsection}{\numberline {3.4.5.2}Active Sensors Ratio}{51}{subsubsection.3.4.5.2}
+\contentsline {subsubsection}{\numberline {3.4.5.3}The percentage of stopped simulation runs}{53}{subsubsection.3.4.5.3}
+\contentsline {subsubsection}{\numberline {3.4.5.4}The Energy Consumption}{53}{subsubsection.3.4.5.4}
+\contentsline {subsubsection}{\numberline {3.4.5.5}Execution Time}{54}{subsubsection.3.4.5.5}
+\contentsline {subsubsection}{\numberline {3.4.5.6}The Network Lifetime}{55}{subsubsection.3.4.5.6}
+\contentsline {subsection}{\numberline {3.4.6}Performance Comparison with other Approaches}{56}{subsection.3.4.6}
+\contentsline {subsubsection}{\numberline {3.4.6.1}Coverage Ratio}{56}{subsubsection.3.4.6.1}
+\contentsline {subsubsection}{\numberline {3.4.6.2}Active Sensors Ratio}{57}{subsubsection.3.4.6.2}
+\contentsline {subsubsection}{\numberline {3.4.6.3}The percentage of stopped simulation runs}{58}{subsubsection.3.4.6.3}
+\contentsline {subsubsection}{\numberline {3.4.6.4}The Energy Consumption}{58}{subsubsection.3.4.6.4}
+\contentsline {subsubsection}{\numberline {3.4.6.5}The Network Lifetime}{59}{subsubsection.3.4.6.5}
+\contentsline {section}{\numberline {3.5}Conclusion}{61}{section.3.5}
\contentsline {chapter}{\numberline {4}Multiround Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}{63}{chapter.4}
\contentsline {section}{\numberline {4.1}Summary}{63}{section.4.1}
\contentsline {section}{\numberline {4.2}MuDiLCO protocol description}{63}{section.4.2}
\contentsline {subsection}{\numberline {4.2.5}Sensing phase}{66}{subsection.4.2.5}
\contentsline {section}{\numberline {4.3}Experimental Study and Analysis}{66}{section.4.3}
\contentsline {subsection}{\numberline {4.3.1}Simulation Setup}{66}{subsection.4.3.1}
-\contentsline {subsection}{\numberline {4.3.2}Metrics}{68}{subsection.4.3.2}
+\contentsline {subsection}{\numberline {4.3.2}Metrics}{67}{subsection.4.3.2}
\contentsline {subsection}{\numberline {4.3.3}Results analysis and Comparison }{69}{subsection.4.3.3}
\contentsline {subsection}{\numberline {4.3.4}Coverage ratio}{69}{subsection.4.3.4}
\contentsline {subsection}{\numberline {4.3.5}Active sensors ratio}{69}{subsection.4.3.5}
\contentsline {subsection}{\numberline {4.3.6}Stopped simulation runs}{70}{subsection.4.3.6}
-\contentsline {subsection}{\numberline {4.3.7}Energy consumption}{70}{subsection.4.3.7}
-\contentsline {subsection}{\numberline {4.3.8}Execution time}{72}{subsection.4.3.8}
+\contentsline {subsection}{\numberline {4.3.7}Energy consumption}{71}{subsection.4.3.7}
+\contentsline {subsection}{\numberline {4.3.8}Execution time}{71}{subsection.4.3.8}
\contentsline {subsection}{\numberline {4.3.9}Network lifetime}{72}{subsection.4.3.9}
-\contentsline {section}{\numberline {4.4}Conclusion}{73}{section.4.4}
-\contentsline {part}{III\hspace {1em}Conclusions and Perspectives}{75}{part.3}
-\contentsline {part}{Bibliographie}{85}{chapter*.6}
+\contentsline {section}{\numberline {4.4}Conclusion}{72}{section.4.4}
+\contentsline {chapter}{\numberline {5}Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks}{75}{chapter.5}
+\contentsline {section}{\numberline {5.1}summary}{75}{section.5.1}
+\contentsline {section}{\numberline {5.2}THE PeCO PROTOCOL DESCRIPTION}{75}{section.5.2}
+\contentsline {subsection}{\numberline {5.2.1}Assumptions and Models}{75}{subsection.5.2.1}
+\contentsline {subsection}{\numberline {5.2.2}The Main Idea}{78}{subsection.5.2.2}
+\contentsline {subsection}{\numberline {5.2.3}PeCO Protocol Algorithm}{79}{subsection.5.2.3}
+\contentsline {section}{\numberline {5.3}LIFETIME COVERAGE PROBLEM FORMULATION}{80}{section.5.3}
+\contentsline {section}{\numberline {5.4}Performance Evaluation and Analysis}{82}{section.5.4}
+\contentsline {subsection}{\numberline {5.4.1}Simulation Settings}{82}{subsection.5.4.1}
+\contentsline {subsection}{\numberline {5.4.2}Simulation Results}{82}{subsection.5.4.2}
+\contentsline {subsubsection}{\numberline {5.4.2.1}Coverage Ratio}{83}{subsubsection.5.4.2.1}
+\contentsline {subsubsection}{\numberline {5.4.2.2}Active Sensors Ratio}{84}{subsubsection.5.4.2.2}
+\contentsline {subsubsection}{\numberline {5.4.2.3}The Energy Consumption}{84}{subsubsection.5.4.2.3}
+\contentsline {subsubsection}{\numberline {5.4.2.4}The Network Lifetime}{84}{subsubsection.5.4.2.4}
+\contentsline {section}{\numberline {5.5}Conclusion}{85}{section.5.5}
+\contentsline {part}{III\hspace {1em}Conclusions and Perspectives}{89}{part.3}
+\contentsline {part}{Bibliographie}{100}{chapter*.6}
year={2008},
organization={IEEE}
}
+
+@ARTICLE{glpk,
+author = {Andrew Makhorin},
+title = {The GLPK (GNU Linear Programming Kit)},
+journal = {Available: https://www.gnu.org/software/glpk/},
+year = {2012},
+}
+
+@techreport{Idrees2,
+ author = {Idrees, Ali Kadhum and Deschinkel, Karine and Salomon, Michel and Couturier, Rapha{\"e}l},
+ institution = {University of Franche-Comte - FEMTO-ST Institute, DISC Research Department},
+ title = {Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks},
+ number = {DISC2014-X},
+ month = {Octobre},
+ year = {2014}
+}
+
+@article{0031-9155-44-1-012,
+ author={Eva K Lee and Richard J Gallagher and David Silvern and Cheng-Shie Wuu and Marco Zaider},
+ title={Treatment planning for brachytherapy: an integer programming model, two computational approaches and experiments with permanent prostate implant planning},
+ journal={Physics in Medicine and Biology},
+ volume={44},
+ number={1},
+ pages={145},
+ url={http://stacks.iop.org/0031-9155/44/i=1/a=012},
+ year={1999}
+}
+
+@BOOK{AMPL,
+ AUTHOR = "Robert Fourer and David M. Gay and Brian W. Kernighan",
+ TITLE = "AMPL: A Modeling Language for Mathematical Programming",
+ PUBLISHER = "Cengage Learning",
+ YEAR = "November 12, 2002",
+ edition = "2nd",
+
+}
+
