An outline of the protocol implementation is given by Algorithm~\ref{alg:DiLCO} which describes the execution of a period by a node (denoted by $s_j$ for a sensor node indexed by $j$). In the beginning, a node checks whether it has enough energy to stay active during the next sensing phase (i.e., the remaining energy $RE_j$ $\geq$ $E_{th}$ (the amount of energy required to be alive during one period)). If yes, it exchanges information with all the other nodes belonging to the same subregion: it collects from each node its position coordinates, remaining energy ($RE_j$), ID, and the number of one-hop neighbors still alive. Once the first phase is completed, the nodes of a subregion choose a leader to take the decision based on the criteria described in section \ref{ch4:sec:02:03:02}.
%the following criteria with decreasing importance: larger number of neighbors, larger remaining energy, and then in case of equality, larger index.
-After that, if the sensor node is leader, it will execute the integer program algorithm (see Section~\ref{ch4:sec:03}) which provides a set of sensors planned to be active in the next sensing phase. As leader, it will send an ActiveSleep packet to each sensor in the same subregion to indicate it if it has to be active or not. Alternately, if the sensor is not the leader, it will wait for the ActiveSleep packet to know its state for the coming sensing phase. \textcolor{blue}{The flow chart of DiLCO protocol that executed in each sensor node is presented in \ref{flow4}.}
+After that, if the sensor node is leader, it will execute the integer program algorithm (see Section~\ref{ch4:sec:03}) which provides a set of sensors planned to be active in the next sensing phase. As leader, it will send an ActiveSleep packet to each sensor in the same subregion to indicate it if it has to be active or not. Alternately, if the sensor is not the leader, it will wait for the ActiveSleep packet to know its state for the coming sensing phase. \textcolor{blue}{The flowchart of DiLCO protocol executed in each sensor node is presented in Figure \ref{flow4}.}
\begin{figure}[ht!]
\centering
\includegraphics[scale=0.50]{Figures/ch4/Algo1.png} % 70mm
-\caption{The flow chart of DiLCO protocol.}
+\caption{The flowchart of DiLCO protocol.}
\label{flow4}
\end{figure}
\item {{\bf Execution Time}}
%\subsubsection{Execution Time}
-In this experiment, the execution time of the distributed optimization approach has been studied. Figure~\ref{Figures/ch4/R1/T} gives the average execution times in seconds for the decision phase (solving of the optimization problem) during one period. They are given for the different approaches and various numbers of sensors. \\ \\% \\ \\ \\
+In this experiment, the execution time of the distributed optimization approach has been studied. Figure~\ref{Figures/ch4/R1/T} gives the average execution times in seconds for the decision phase (solving of the optimization problem) during one period. They are given for the different approaches and various numbers of sensors. % \\ \\ \\
\label{Figures/ch4/R1/LT}
\end{figure}
-For DiLCO-2 protocol, execution times quickly become unsuitable for a sensor network, and the energy consumed during the communication, seems to be huge because it is distributed over only two subregions. As highlighted by Figures~\ref{Figures/ch4/R1/LT}(a) and \ref{Figures/ch4/R1/LT}(b), the network lifetime obviously increases when the size of the network increases. The network lifetime also increases with the number of subregions, but only up to a given number. Thus we can see that DiLCO-16 leads to the larger lifetime improvement and not DiLCO-32. In fact, DilCO-32 protocol puts in active mode a larger number of sensor nodes especially near the borders of the subdivisions. It means that distributing the protocol in each node and subdividing the sensing field into many subregions, which are managed independently and simultaneously, is a relevant way to maximize the lifetime of a network.
+For DiLCO-2 protocol, execution times quickly become unsuitable for a sensor network, and the energy consumed during the communication, seems to be huge because it is distributed over only two subregions. As highlighted by Figures~\ref{Figures/ch4/R1/LT}(a) and \ref{Figures/ch4/R1/LT}(b), the network lifetime obviously increases when the size of the network increases. The network lifetime also increases with the number of subregions, but only up to a given number. Thus we can see that DiLCO-16 leads to the larger lifetime improvement and not DiLCO-32. \\ \\ \\ \\In fact, DilCO-32 protocol puts in active mode a larger number of sensor nodes especially near the borders of the subdivisions. It means that distributing the protocol in each node and subdividing the sensing field into many subregions, which are managed independently and simultaneously, is a relevant way to maximize the lifetime of a network.
\end{enumerate}
As can be seen in Figure~\ref{Figures/ch4/R2/CR}, at the beginning the models which use a larger number of primary points provide slightly better coverage ratios, but latter they are the worst.
%Moreover, when the number of periods increases, coverage ratio produced by Model-9, Model-13, Model-17, and Model-21 decreases in comparison with Model-5 due to a larger time computation for the decision process for larger number of primary points.
Moreover, when the number of periods increases, coverage ratio produced by all models decrease, but Model-5 is the one with the slowest decrease due to a smaller time computation of decision process for a smaller number of primary points.
-As shown in Figure ~\ref{Figures/ch4/R2/CR}, coverage ratio decreases when the number of periods increases due to dead nodes. Model-5 is slightly more efficient than other models, because it offers a good coverage ratio for a larger number of periods in comparison with other models.
+As shown in Figure ~\ref{Figures/ch4/R2/CR}, coverage ratio decreases when the number of periods increases due to dead nodes. \\\\\\Model-5 is slightly more efficient than other models, because it offers a good coverage ratio for a larger number of periods in comparison with other models.
\item {{\bf Active Sensors Ratio}}
%\subsubsection{Active Sensors Ratio}
\label{Figures/ch4/R2/SR}
\end{figure}
-When the number of primary points is increased, the percentage of the stopped simulation runs per period is increased. The reason behind the increase is the increasing number of dead sensors when the primary points increase. Model-5 is better than other models because it conserves more energy by turning on less sensors during the sensing phase and in the same time it preserves a good coverage for a larger number of periods in comparison with other models. Model~5 seems to be more suitable to be used in wireless sensor networks. \\
+When the number of primary points is increased, the percentage of the stopped simulation runs per period is increased. The reason behind the increase is the increasing number of dead sensors when the primary points increase. Model-5 is better than other models because it conserves more energy by turning on less sensors during the sensing phase and in the same time it preserves a good coverage for a larger number of periods in comparison with other models. Model~5 seems to be more suitable to be used in wireless sensor networks. \\\\\\\\
\item {{\bf Energy Consumption}}
Finally, we study the effect of increasing the primary points on the lifetime of the network.
%In Figure~\ref{Figures/ch4/R2/LT95} and in Figure~\ref{Figures/ch4/R2/LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes.
-As highlighted by Figures~\ref{Figures/ch4/R2/LT}(a) and \ref{Figures/ch4/R2/LT}(b), the network lifetime obviously increases when the size of the network increases, with Model-5 that leads to the larger lifetime improvement. \\ \\
+As highlighted by Figures~\ref{Figures/ch4/R2/LT}(a) and \ref{Figures/ch4/R2/LT}(b), the network lifetime obviously increases when the size of the network increases, with Model-5 that leads to the larger lifetime improvement.
Comparison shows that Model-5, which uses less number of primary points, is the best one because it is less energy consuming during the network lifetime. It is also the better one from the point of view of coverage ratio. Our proposed Model-5 efficiently prolongs the network lifetime with a good coverage ratio in comparison with other models. Therefore, we have chosen Model-5 for all the experiments presented thereafter.
\caption{Coverage ratio for 150 deployed nodes}
\label{Figures/ch4/R3/CR}
\end{figure}
-DESK and GAF provide a little better coverage ratio with 99.99\% and 99.91\% against 98.4\% and 98.9\% produced by DiLCO-16 and DiLCO-32 for the lowest number of periods. \\ \\ \\
+DESK and GAF provide a little better coverage ratio with 99.99\% and 99.91\% against 98.4\% and 98.9\% produced by DiLCO-16 and DiLCO-32 for the lowest number of periods.
This is due to the fact that DiLCO protocol versions put in sleep mode redundant sensors thanks to the optimization (which lightly decreases the coverage ratio), while there are more active nodes in the case of DESK and GAF.
\label{Figures/ch4/R3/ASR}
\end{figure}
-The results presented in Figure~\ref{Figures/ch4/R3/ASR} show the superiority of the proposed DiLCO-16 protocol and DiLCO-32 protocol, in comparison with the other approaches. DESK and GAF have, respectively, 37.5 \% and 44.5 \% active nodes, whereas DiLCO-16 and DiLCO-32 protocols compete perfectly with only 23.7 \% and 25.8 \% active nodes for the first 14 periods. Then as the number of periods increases DiLCO-16 and DiLCO-32 protocols have larger number of active nodes in comparison with DESK and GAF, especially from period $35^{th}$ because they give a better coverage ratio than other approaches. We see that DESK and GAF have less number of active nodes beginning at the periods $35^{th}$ and $32^{th}$ because there are many dead nodes due to the high energy consumption by the redundant nodes during the previous sensing phases. \\
+The results presented in Figure~\ref{Figures/ch4/R3/ASR} show the superiority of the proposed DiLCO-16 protocol and DiLCO-32 protocol, in comparison with the other approaches. DESK and GAF have, respectively, 37.5 \% and 44.5 \% active nodes, whereas DiLCO-16 and DiLCO-32 protocols compete perfectly with only 23.7 \% and 25.8 \% active nodes for the first 14 periods. \\\\\\\\\\\\Then as the number of periods increases DiLCO-16 and DiLCO-32 protocols have larger number of active nodes in comparison with DESK and GAF, especially from period $35^{th}$ because they give a better coverage ratio than other approaches. We see that DESK and GAF have less number of active nodes beginning at the periods $35^{th}$ and $32^{th}$ because there are many dead nodes due to the high energy consumption by the redundant nodes during the previous sensing phases.
\item {{\bf Stopped simulation runs}}
DiLCO-16 protocol and DiLCO-32 protocol are the most competitive from the energy consumption point of view. The other approaches have a high energy consumption due to activating a larger number of redundant nodes.
%as well as the energy consumed during the different modes of sensor nodes.
-In fact, the distribution of computation over the subregions greatly reduces the number of communications and the time of listening, thanks to the partitioning of the initial network into several independent subnetworks.
-
+In fact, the distribution of computation over the subregions greatly reduces the number of communications and the time of listening, thanks to the partitioning of the initial network into several independent subnetworks. \\\\\\\\
\item {{\bf Network Lifetime}}
-%\subsubsection{The Network Lifetime}
-%In this experiment, we have observed the superiority of DiLCO-16 protocol and DiLCO-32 protocol against other two approaches in prolonging the network lifetime.
-
+As highlighted by Figures~\ref{Figures/ch4/R3/LT}(a) and \ref{Figures/ch4/R3/LT}(b), the network lifetime obviously increases when the size of the network increases, with DiLCO-16 protocol and DiLCO-32 protocol which lead to maximize the lifetime of the network compared with other approaches.
%In figures~\ref{Figures/ch4/R3/LT95} and \ref{Figures/ch4/R3/LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes.
\begin{figure}[h!]
\label{Figures/ch4/R3/LT}
\end{figure}
-As highlighted by Figures~\ref{Figures/ch4/R3/LT}(a) and \ref{Figures/ch4/R3/LT}(b), the network lifetime obviously increases when the size of the network increases, with DiLCO-16 protocol and DiLCO-32 protocol which lead to maximize the lifetime of the network compared with other approaches.
+
By choosing the best suited nodes, for each period, by optimizing the coverage and lifetime of the network to cover the area of interest and by letting the other ones sleep in order to be used later in next periods, DiLCO-16 protocol and DiLCO-32 protocol efficiently prolong the network lifetime.
Comparison shows that DiLCO-16 protocol and DiLCO-32 protocol, which use distributed optimization over the subregions, are the best ones because they are robust to network disconnection during the network lifetime as well as they consume less energy in comparison with other approaches.
%It also means that distributing the algorithm in each node and subdividing the sensing field into many subregions, which are managed independently and simultaneously, is the most relevant way to maximize the lifetime of a network.
each round of the sensing phase. Each sensing phase is itself divided into $T$ rounds and for each round a set of sensors (a cover set) is responsible for the sensing task.
%Each sensor node in the subregion will receive an ActiveSleep packet from leader, informing it to stay awake or to go to sleep for each round of the sensing phase.
Algorithm~\ref{alg:MuDiLCO}, which will be executed by each node at the beginning of a period, explains how the ActiveSleep packet is obtained. 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. \textcolor{blue}{The flow chart of MuDiLCO protocol that executed in each sensor node is presented in \ref{flow5}.}
+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. \textcolor{blue}{The flowchart of MuDiLCO protocol executed in each sensor node is presented in Figure \ref{flow5}.}
\begin{figure}[ht!]
\centering
\includegraphics[scale=0.50]{Figures/ch5/Algo2.png} % 70mm
-\caption{The flow chart of MuDiLCO protocol.}
+\caption{The flowchart of MuDiLCO protocol.}
\label{flow5}
\end{figure}
%\label{ch5:sec:03:02:03}
Figure~\ref{fig6} reports the cumulative percentage of stopped simulations runs per round for 150 deployed nodes. This figure gives the breakpoint for each method.
-DESK stops first, after approximately 45~rounds, because it consumes the more energy by turning on a large number of redundant nodes during the sensing phase. GAF stops secondly for the same reason than DESK. MuDiLCO overcomes DESK and GAF because the optimization process distributed on several subregions leads to coverage preservation and so extends the network lifetime. Let us
-emphasize that the simulation continues as long as a network in a subregion is still connected. \\
+DESK stops first, after approximately 45~rounds, because it consumes the more energy by turning on a large number of redundant nodes during the sensing phase. GAF stops secondly for the same reason than DESK. \\\\\\ MuDiLCO overcomes DESK and GAF because the optimization process distributed on several subregions leads to coverage preservation and so extends the network lifetime. Let us
+emphasize that the simulation continues as long as a network in a subregion is still connected.
\begin{figure}[t]
We observe the impact of the network size and of the number of rounds on the
computation time. Figure~\ref{fig77} gives the average execution times in
-seconds (needed to solve optimization problem) for different values of $T$. The original execution time is computed as described in chapter 4, section \ref{ch4:sec:04:02}.
+seconds (needed to solve optimization problem) for different values of $T$. \\\\\\
%The original execution time is computed on a laptop DELL with Intel Core~i3~2370~M (2.4 GHz) processor (2 cores) and the MIPS (Million Instructions Per Second) rate equal to 35330. To be consistent with the use of a sensor node with Atmels AVR ATmega103L microcontroller (6 MHz) and a MIPS rate equal to 6 to run the optimization resolution, this time is multiplied by 2944.2 $\left( \frac{35330}{2} \times \frac{1}{6} \right)$ and reported on Figure~\ref{fig77} for different network sizes.
\label{fig77}
\end{figure}
-As expected, the execution time increases with the number of rounds $T$ taken into account to schedule the sensing phase. The times obtained for $T=1,3$ or $5$ seem bearable, but for $T=7$ they become quickly unsuitable for a sensor node, especially when the sensor network size increases. Again, we can notice that if we want to schedule the nodes activities for a large number of rounds,
+The original execution time is computed as described in chapter 4, section \ref{ch4:sec:04:02}. As expected, the execution time increases with the number of rounds $T$ taken into account to schedule the sensing phase. The times obtained for $T=1,3$ or $5$ seem bearable, but for $T=7$ they become quickly unsuitable for a sensor node, especially when the sensor network size increases. Again, we can notice that if we want to schedule the nodes activities for a large number of rounds,
we need to choose a relevant number of subregions in order to avoid a complicated and cumbersome optimization.
On the one hand, a large value for $T$ permits to reduce the energy overhead due to the three pre-sensing phases, on the other hand a leader node may waste a considerable amount of energy to solve the optimization problem. %\\ \\ \\ \\ \\ \\ \\
The slight decrease that can be observed for MuDiLCO-7 in case of $Lifetime_{95}$ with large wireless sensor networks results from the difficulty of the optimization problem to be solved by the integer program.
-This point was already noticed in \ref{subsec:EC} devoted to the
+\\\\\\\\This point was already noticed in \ref{subsec:EC} devoted to the
energy consumption, since network lifetime and energy consumption are directly linked.
\end{enumerate}
\noindent The WSN area of interest is, in a first step, divided into regular homogeneous subregions using a divide-and-conquer algorithm. In a second step our protocol will be executed in a distributed way in each subregion simultaneously to schedule nodes' activities for one sensing period. Sensor nodes are assumed to be deployed almost uniformly over the region. The regular subdivision is made such that the number of hops between any pairs of sensors inside a subregion is less than or equal to 3.
-As shown in Figure~\ref{fig2}, node activity scheduling is produced by our protocol in a periodic manner. Each period is divided into 4 stages: Information (INFO) Exchange, Leader Election, Decision (the result of an optimization problem), and Sensing. For each period, there is exactly one set cover responsible for the sensing task. Protocols based on a periodic scheme, like PeCO, are more robust against an unexpected node failure. On the one hand, if a node failure is discovered before taking the decision, the corresponding sensor
+As shown in Figure~\ref{fig2}, node activity scheduling is produced by our protocol in a periodic manner. Each period is divided into 4 stages: Information (INFO) Exchange, Leader Election, Decision (the result of an optimization problem), and Sensing. For each period, there is exactly one set cover responsible for the sensing task. Protocols based on a periodic scheme, like PeCO, are more robust against an unexpected node failure. On the one hand, if a node failure is discovered before taking the decision, the corresponding sensor
node will not be considered by the optimization algorithm. On the other hand, if the sensor failure happens after the decision, the sensing task of the network will be temporarily affected: only during the period of sensing until a new period starts, since a new set cover will take charge of the sensing task in the next period. 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 (INFO Exchange, Leader Election, and Decision)
are energy consuming, even for nodes that will not join the set cover to monitor the area.
\item larger remaining energy;
\item and then in case of equality, larger index.
\end{enumerate}
-Once chosen, the leader collects information to formulate and solve the integer program which allows to construct the set of active sensors in the sensing stage. \textcolor{blue}{The flow chart of PeCO protocol that executed in each sensor node is presented in \ref{flow6}.}
+Once chosen, the leader collects information to formulate and solve the integer program which allows to construct the set of active sensors in the sensing stage. \textcolor{blue}{The flowchart of PeCO protocol executed in each sensor node is presented in Figure \ref{flow6}.}
\begin{figure}[ht!]
\centering
\includegraphics[scale=0.45]{Figures/ch6/Algo3.pdf} % 70mm
-\caption{The flow chart of PeCO protocol.}
+\caption{The flowchart of PeCO protocol.}
\label{flow6}
\end{figure}
\contentsline {subsection}{\numberline {4.4.4}Performance Metrics}{86}{subsection.4.4.4}
\contentsline {subsection}{\numberline {4.4.5}Performance Analysis for Different Number of Subregions}{87}{subsection.4.4.5}
\contentsline {subsection}{\numberline {4.4.6}Performance Analysis for Different Number of Primary Points}{92}{subsection.4.4.6}
-\contentsline {subsection}{\numberline {4.4.7}Performance Comparison with other Approaches}{98}{subsection.4.4.7}
-\contentsline {section}{\numberline {4.5}Conclusion}{104}{section.4.5}
-\contentsline {chapter}{\numberline {5}Multiround Distributed Lifetime Coverage Optimization Protocol}{105}{chapter.5}
-\contentsline {section}{\numberline {5.1}Introduction}{105}{section.5.1}
-\contentsline {section}{\numberline {5.2}Description of the MuDiLCO Protocol }{105}{section.5.2}
-\contentsline {section}{\numberline {5.3}Primary Points based Multiround Coverage Problem Formulation}{107}{section.5.3}
-\contentsline {section}{\numberline {5.4}Experimental Study and Analysis}{109}{section.5.4}
-\contentsline {subsection}{\numberline {5.4.1}Simulation Setup}{109}{subsection.5.4.1}
-\contentsline {subsection}{\numberline {5.4.2}Metrics}{109}{subsection.5.4.2}
-\contentsline {subsection}{\numberline {5.4.3}Results Analysis and Comparison }{110}{subsection.5.4.3}
-\contentsline {section}{\numberline {5.5}Conclusion}{116}{section.5.5}
-\contentsline {chapter}{\numberline {6} Perimeter-based Coverage Optimization to Improve Lifetime in WSNs}{117}{chapter.6}
-\contentsline {section}{\numberline {6.1}Introduction}{117}{section.6.1}
-\contentsline {section}{\numberline {6.2}Description of the PeCO Protocol}{117}{section.6.2}
-\contentsline {subsection}{\numberline {6.2.1}Assumptions and Models}{117}{subsection.6.2.1}
-\contentsline {subsection}{\numberline {6.2.2}PeCO Protocol Algorithm}{120}{subsection.6.2.2}
-\contentsline {section}{\numberline {6.3}Perimeter-based Coverage Problem Formulation}{123}{section.6.3}
-\contentsline {section}{\numberline {6.4}Performance Evaluation and Analysis}{124}{section.6.4}
-\contentsline {subsection}{\numberline {6.4.1}Simulation Settings}{124}{subsection.6.4.1}
-\contentsline {subsection}{\numberline {6.4.2}Simulation Results}{125}{subsection.6.4.2}
-\contentsline {subsubsection}{\numberline {6.4.2.1}Coverage Ratio}{125}{subsubsection.6.4.2.1}
-\contentsline {subsubsection}{\numberline {6.4.2.2}Active Sensors Ratio}{125}{subsubsection.6.4.2.2}
-\contentsline {subsubsection}{\numberline {6.4.2.3}Energy Consumption}{126}{subsubsection.6.4.2.3}
-\contentsline {subsubsection}{\numberline {6.4.2.4}Network Lifetime}{126}{subsubsection.6.4.2.4}
-\contentsline {subsubsection}{\numberline {6.4.2.5}Impact of $\alpha $ and $\beta $ on PeCO's performance}{127}{subsubsection.6.4.2.5}
-\contentsline {section}{\numberline {6.5}Conclusion}{131}{section.6.5}
-\contentsline {part}{III\hspace {1em}Conclusion and Perspectives}{133}{part.3}
-\contentsline {chapter}{\numberline {7}Conclusion and Perspectives}{135}{chapter.7}
-\contentsline {section}{\numberline {7.1}Conclusion}{135}{section.7.1}
-\contentsline {section}{\numberline {7.2}Perspectives}{136}{section.7.2}
-\contentsline {part}{Publications}{139}{chapter*.15}
-\contentsline {part}{Bibliographie}{154}{chapter*.19}
+\contentsline {subsection}{\numberline {4.4.7}Performance Comparison with other Approaches}{96}{subsection.4.4.7}
+\contentsline {section}{\numberline {4.5}Conclusion}{105}{section.4.5}
+\contentsline {chapter}{\numberline {5}Multiround Distributed Lifetime Coverage Optimization Protocol}{107}{chapter.5}
+\contentsline {section}{\numberline {5.1}Introduction}{107}{section.5.1}
+\contentsline {section}{\numberline {5.2}Description of the MuDiLCO Protocol }{107}{section.5.2}
+\contentsline {section}{\numberline {5.3}Primary Points based Multiround Coverage Problem Formulation}{109}{section.5.3}
+\contentsline {section}{\numberline {5.4}Experimental Study and Analysis}{111}{section.5.4}
+\contentsline {subsection}{\numberline {5.4.1}Simulation Setup}{111}{subsection.5.4.1}
+\contentsline {subsection}{\numberline {5.4.2}Metrics}{111}{subsection.5.4.2}
+\contentsline {subsection}{\numberline {5.4.3}Results Analysis and Comparison }{112}{subsection.5.4.3}
+\contentsline {section}{\numberline {5.5}Conclusion}{118}{section.5.5}
+\contentsline {chapter}{\numberline {6} Perimeter-based Coverage Optimization to Improve Lifetime in WSNs}{119}{chapter.6}
+\contentsline {section}{\numberline {6.1}Introduction}{119}{section.6.1}
+\contentsline {section}{\numberline {6.2}Description of the PeCO Protocol}{119}{section.6.2}
+\contentsline {subsection}{\numberline {6.2.1}Assumptions and Models}{119}{subsection.6.2.1}
+\contentsline {subsection}{\numberline {6.2.2}PeCO Protocol Algorithm}{122}{subsection.6.2.2}
+\contentsline {section}{\numberline {6.3}Perimeter-based Coverage Problem Formulation}{125}{section.6.3}
+\contentsline {section}{\numberline {6.4}Performance Evaluation and Analysis}{126}{section.6.4}
+\contentsline {subsection}{\numberline {6.4.1}Simulation Settings}{126}{subsection.6.4.1}
+\contentsline {subsection}{\numberline {6.4.2}Simulation Results}{127}{subsection.6.4.2}
+\contentsline {subsubsection}{\numberline {6.4.2.1}Coverage Ratio}{127}{subsubsection.6.4.2.1}
+\contentsline {subsubsection}{\numberline {6.4.2.2}Active Sensors Ratio}{127}{subsubsection.6.4.2.2}
+\contentsline {subsubsection}{\numberline {6.4.2.3}Energy Consumption}{128}{subsubsection.6.4.2.3}
+\contentsline {subsubsection}{\numberline {6.4.2.4}Network Lifetime}{128}{subsubsection.6.4.2.4}
+\contentsline {subsubsection}{\numberline {6.4.2.5}Impact of $\alpha $ and $\beta $ on PeCO's performance}{129}{subsubsection.6.4.2.5}
+\contentsline {section}{\numberline {6.5}Conclusion}{133}{section.6.5}
+\contentsline {part}{III\hspace {1em}Conclusion and Perspectives}{135}{part.3}
+\contentsline {chapter}{\numberline {7}Conclusion and Perspectives}{137}{chapter.7}
+\contentsline {section}{\numberline {7.1}Conclusion}{137}{section.7.1}
+\contentsline {section}{\numberline {7.2}Perspectives}{138}{section.7.2}
+\contentsline {part}{Publications}{141}{chapter*.15}
+\contentsline {part}{Bibliographie}{156}{chapter*.19}
%% The second mandatory parameter is the date of the PhD defense.
%% The third mandatory parameter is the reference number given by the University Library after the PhD defense.
%%\declarethesis[Sous-titre]{Titre}{17 septembre 2012}{XXX}
-\declarethesis{Distributed Coverage Optimization Techniques for Improving Lifetime of Wireless Sensor Networks}{30 September 2015}{2015930}
+\declarethesis{Distributed Coverage Optimization Techniques for Improving Lifetime of Wireless Sensor Networks}{30 September 2015}{2015050}
%%--------------------
%% Set the author of the PhD thesis
