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--- a/CHAPITRE_01.tex
+++ b/CHAPITRE_01.tex
@@ -146,31 +146,32 @@ The most significant goal for many companies is the automation of controlling an
 
 \section{The Main Challenges in Wireless Sensor Networks}
 \label{ch1:sec:05}
 
 \section{The Main Challenges in Wireless Sensor Networks}
 \label{ch1:sec:05}

-\indent Many challenges need to be faced in WSNs, which are received increasing attention by a large number of researchers during the last few years. These challenges represent the main reason to propose a different solutions so as to face them as will be explained in next section~\ref{ch1:sec:06}.
+\indent Many challenges need to be faced in WSNs, which are received increasing attention by a large number of researchers during the last few years. 
+%These challenges represent the main reason to propose a different solutions so as to face them as will be explained in next section~\ref{ch1:sec:06}.

 \begin{enumerate} [(I)]
 \item \textbf{Extended Network Lifetime:} One fundamental issue in WSNs is how to prolong the network lifetime as long as possible. Since sensor battery has a limited power; and since it is difficult to recharge or replace it especially in remote or hostile environment; It is necessary to reduce the energy consumption by using energy-efficient methods in order to extend the network lifetime.
 
 \item \textbf{Coverage:} One of the fundamental challenges in WSNs is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area (or region) of interest. The major objective is to choose the minimum number of sensor nodes in order to monitor the target sensing field without affecting on the application requirements in executing its tasks as long time as possible. 
 
 \begin{enumerate} [(I)]
 \item \textbf{Extended Network Lifetime:} One fundamental issue in WSNs is how to prolong the network lifetime as long as possible. Since sensor battery has a limited power; and since it is difficult to recharge or replace it especially in remote or hostile environment; It is necessary to reduce the energy consumption by using energy-efficient methods in order to extend the network lifetime.
 
 \item \textbf{Coverage:} One of the fundamental challenges in WSNs is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area (or region) of interest. The major objective is to choose the minimum number of sensor nodes in order to monitor the target sensing field without affecting on the application requirements in executing its tasks as long time as possible. 
 

-\item \textbf{Routing:} Represents one of the important problems in WSNs that needs to be solved efficiently. The limited resources of WSNs and the impacts of wireless communication are led to a big challenge in ensuring energy-efficient routing. However, it is not enough to use the shortest path to route the packets among the sensor nodes toward the sink. It is necessary to design an energy-efficient routing protocol that considers the remaining energy of sensor node during taking the decision to route the packet to the next hop toward the destination. This can participates in energy conservation and balancing among the sensor node in WSNs. 
+\item \textbf{Routing:} Represents one of the important problems in WSNs that needs to be solved efficiently. The limited resources of WSNs and the impacts of wireless communication led to a big challenge in ensuring energy-efficient routing. However, it is not enough to use the shortest path to route the packets among the sensor nodes toward the sink. It is necessary to design an energy-efficient routing protocol that considers the remaining energy of sensor node during taking the decision to route the packet to the next hop toward the destination. This participates in energy conservation and balancing among the sensor node in WSNs. 

 
 \item \textbf{Autonomous and Distributed Management:}
 
 \item \textbf{Autonomous and Distributed Management:}

-Since the nature of many WSN applications that need to be deployed in a remote or hostile environment, it is important that the wireless sensor nodes work in autonomous and distributed way to communicate and cooperate with other sensor nodes without human intervention because the maintenance or the repair maybe difficult. The distributed management consumes less energy because it is based on only local information from the neighboring sensor nodes; on the other hand, it does not give the optimal solution, so the main challenge is how to apply the a distributed management in WSNs and in the same time ensuring an optimal or near optimal solution.
+Since the nature of many WSN applications need to be deployed in a remote or hostile environment, it is important that the wireless sensor nodes work in autonomous and distributed way to communicate and cooperate with other sensor nodes without human intervention because the maintenance or the repair may be difficult. The distributed management consumes less energy because it is based on only local information from the neighboring sensor nodes; on the other hand, it does not give the optimal solution, so the main challenge is how to apply the a distributed management in WSNs and in the same time ensuring an optimal or near optimal solution.

    
    

-\item \textbf{Scalability:} Many physical phenomenons require to be deployed densely with a large number of sensor nodes for different reasons such as: the large sensed area,  reliability requirement, or network lifetime prolongation. It is necessary that the proposed protocols in WSNs be scalable for these large number of sensor
+\item \textbf{Scalability:} Many physical phenomenons require to be deployed densely with a large number of sensor nodes for different reasons such as: the large sensed area,  reliability requirement, or network lifetime prolongation. It is necessary that the proposed protocols in WSNs are scalable for these large number of sensor

 nodes in order to achieve their tasks efficiently.
 
   
 
 nodes in order to achieve their tasks efficiently.
 
   
 

-\item \textbf{Reliability:} Many applications require a high quality of coverage. These applications need to deploy a large number of a cheap sensor nodes so as to satisfy the requirement of application. These large number of the sensor nodes may be prone to the failure and this will affect on the quality of service provided by the application. However, it is important to build mechanisms inside the protocols so as to avoid the failure of some sensor nodes during the network operation and to increase the robustness of the proposed protocol in WSNs.
+\item \textbf{Reliability:} Many applications require a high quality of coverage. These applications need to deploy a large number of a cheap sensor nodes so as to satisfy their requirements. This large number of the sensor nodes may be prone to the failure and this will affect on the quality of service provided by the application. However, it is important to build mechanisms inside the protocols so as to avoid the failure of some sensor nodes during the network operation and to increase the robustness of the proposed protocol in WSNs.

 
 

-\item \textbf{Topology Control:} The maintenance and repair of the network topology is a challenging task because the large number of inaccessible sensor nodes that are prone to failure. Therefore, some schemes need to used to deal with the dynamic changing of topology and the failure of some sensor nodes due to energy depletion or malfunction.
+\item \textbf{Topology Control:} The maintenance and repair of the network topology is a challenging task because there is a large number of inaccessible sensor nodes that are prone to failure. Therefore, some schemes need to be used to deal with the dynamic changing of topology and the failure of some sensor nodes due to energy depletion or malfunction.

 
 

-\item \textbf{Heterogeneity:} One essential challenge is to provide a WSN protocol that deals with different sensor node capabilities such as communication, processing, sensing, and energy. The future of WSNs will be heterogeneous with a large number of sensor nodes. These WSNs may be reflect different tasks and can be integrated into one big network. Therefore, it is necessary to take the heterogeneity into consideration during constructing the protocols in WSNs. 
+\item \textbf{Heterogeneity:} One essential challenge is to provide a WSN protocol that deals with different sensor node capabilities such as communication, processing, sensing, and energy. The future of WSNs will be heterogeneous with a large number of sensor nodes. These WSNs may be reflect different tasks and can be integrated into one big network. Therefore, it is necessary to take the heterogeneity into consideration for constructing the protocols in WSNs. 

 
 

-\item \textbf{Wireless Networking:} The networking and wireless communication represent another important challenge in WSNs. The communication range of the signals can be attenuated or faded during the signal propagation across the communication media or during passing through obstacles. The increasing distance between the sensor nodes and the sink requires increased transmission power; However, the long distance can be divided into several small distances using multi-hop communication. The multi-hop communication poses another challenge is how to find the more energy efficient route to transmit the information from the source to the destination, where the sensor nodes should be cooperated to find this route; and to serve as relays.
+\item \textbf{Wireless Networking:} The networking and wireless communication represent another important challenge in WSNs. The communication range of the signals can be attenuated or faded during the signal propagation across the communication media or during passing through obstacles. The increasing distance between the sensor nodes and the sink requires increased transmission power; However, the long distance can be divided into several small distances using multi-hop communication. The multi-hop communication generates another challenge: how to find the more energy efficient route to transmit the information from the source to the destination, where the sensor nodes should be cooperated to find this route; and to serve as relays.

 
 

-\item \textbf{Data Management:} Represents one of the challenges that contributes in depleting the energy of the sensor nodes in WSNs. The main task of the WSN after deploying the sensor nodes in target environment that need to be monitored is to collect the sensed data from this physical environment and then transmit it to the base station. Since there are many sensor nodes in WSN; and since every sensor node want to transmit its sensed data to the base station; It will be a large amount of data that need to be managed, processed and routed, to the sink that represents a real challenge in WSNs.
+\item \textbf{Data Management:} Represents one of the challenges that contributes in depleting the energy of the sensor nodes in WSNs. The main task of the WSN after deploying the sensor nodes in target environment that need to be monitored is to collect the sensed data from this physical environment and then transmit it to the base station. Since there are many sensor nodes in WSN; and since every sensor node want to transmit its sensed data to the base station; there is a large amount of data that need to be managed, processed and routed, to the sink and it represents a real challenge in WSNs.

 
 \item \textbf{Security:} The sensitivity of the information collected by WSNs represents the final challenge that should be faced in WSNs. This information is susceptible to malicious intrusions and hacker attacks; however, it is necessary to provide energy efficient schemes by WSNs to protect this information during the operation of WSNs.
 
 
 \item \textbf{Security:} The sensitivity of the information collected by WSNs represents the final challenge that should be faced in WSNs. This information is susceptible to malicious intrusions and hacker attacks; however, it is necessary to provide energy efficient schemes by WSNs to protect this information during the operation of WSNs.
 


@@ -194,12 +195,12 @@ nodes in order to achieve their tasks efficiently.
 
 \subsubsection{Routing Metric based on Residual Energy:} Lifetime maximization can be achieved by using the residual power of wireless sensor node as a routing metric that should be taken into account during executing the routing protocol in WSNs. The routing protocols should be concentrated on the remaining power of sensor nodes during taking the decision to select the next hop toward the destination. They should not only depend on the shortest path solution.  They prioritize routes on the basis of an energy metric (sometimes with other routing metrics), therefore,  it is called energy-aware routing protocols.~\cite{ref45,ref46}.
 
 
 \subsubsection{Routing Metric based on Residual Energy:} Lifetime maximization can be achieved by using the residual power of wireless sensor node as a routing metric that should be taken into account during executing the routing protocol in WSNs. The routing protocols should be concentrated on the remaining power of sensor nodes during taking the decision to select the next hop toward the destination. They should not only depend on the shortest path solution.  They prioritize routes on the basis of an energy metric (sometimes with other routing metrics), therefore,  it is called energy-aware routing protocols.~\cite{ref45,ref46}.
 

-\subsubsection{Multipath Routing:} Efficient strategy that provides reliability, security, and load balancing in order to forward packets in a limited energy and constrained resources(computation, communication, and storage) networks like WSNs~\cite{ref50}. The single path routing is simple and scalable but it is not efficient for energy constrained networks such as WSNs. Many multipath routing protocol are summarized in~\cite{ref50,ref51}.
+\subsubsection{Multipath Routing:} Efficient strategy that provides reliability, security, and load balancing in order to forward packets in a limited energy and constrained resources (computation, communication, and storage) networks like WSNs~\cite{ref50}. The single path routing is simple and scalable but it is not efficient for energy constrained networks such as WSNs. Many multipath routing protocol summarized in~\cite{ref50,ref51}.

 
 
 \subsection{Cluster Architectures:}
 
 
 
 \subsection{Cluster Architectures:}
 

-\indent In this strategy, the wireless sensor nodes are grouped into several groups that called clusters, each group of wireless sensor nodes are managed by a single sensor node, which is called cluster head. The cluster head takes the responsibility of manging the activities of the wireless sensor nodes with the cluster and it communicates and coordinates with other cluster heads or the base station in the WSN. This mechanism conserves the energy in WSNs by means of~\cite{ref43,ref22}:
+\indent In this strategy, the wireless sensor nodes are grouped into several groups that called clusters, each group of wireless sensor nodes are managed by a single sensor node, which is called cluster head. The cluster head takes the responsibility of managing the activities of the wireless sensor nodes with the cluster and it communicates and coordinates with other cluster heads or with the base station in the WSN. This mechanism conserves the energy in WSNs by means of~\cite{ref43,ref22}:

 
 \begin{enumerate}[(a)]
 \item Grouping the wireless sensor nodes into clusters is led to decrease the communication range within the cluster and therefore minimize the energy needed to communication among the nodes inside the cluster.
 
 \begin{enumerate}[(a)]
 \item Grouping the wireless sensor nodes into clusters is led to decrease the communication range within the cluster and therefore minimize the energy needed to communication among the nodes inside the cluster.


@@ -215,7 +216,7 @@ nodes in order to achieve their tasks efficiently.
 
 \subsection{Scheduling Schemes:}
 
 
 \subsection{Scheduling Schemes:}
 

-\indent Many scheduling schemes have been suggested so as to decrease the energy depletion and improve the lifetime of WSNs~\cite{ref58,ref59}. These schemes have dealt with scheduling the states of wireless sensor nodes and putting the idle sensor nodes into sleep mode (i.e, turn off the radio unit) to save the energy. Figure~\ref{wsns} summarizes the Scheduling Schemes in WSNs. In this figure, the scheduling schemes are classified into two main branches~\cite{ref56,ref57}: (i) wake up scheduling aims to manage the wireless sensor node states (sleep/wake up) in WSN by selecting a set of time intervals for a sensor nodes to be awake from continuous time duration. and (ii) topology control in which a set of a wireless sensor nodes are chosen to be awake from a given sensor nodes in WSN. 
+\indent Many scheduling schemes have been suggested so as to decrease the energy depletion and improve the lifetime of WSNs~\cite{ref58,ref59}. These schemes deal with scheduling the states of wireless sensor nodes and putting the idle sensor nodes into sleep mode (i.e, turn off the radio unit) to save the energy. Figure~\ref{wsns} summarizes the scheduling schemes in WSNs. In this figure, the scheduling schemes are classified into two main branches~\cite{ref56,ref57}: (i) wake up scheduling aims to manage the wireless sensor node states (sleep/wake up) in WSN by selecting a set of time intervals for a sensor nodes to be awake from continuous time duration. and (ii) topology control in which a set of a wireless sensor nodes are chosen to be awake from a given sensor nodes in WSN. 

 \begin{figure}[h!]
 \centering
 \includegraphics[scale=0.5]{Figures/ch1/WSN-S.pdf} 
 \begin{figure}[h!]
 \centering
 \includegraphics[scale=0.5]{Figures/ch1/WSN-S.pdf} 


@@ -226,7 +227,7 @@ nodes in order to achieve their tasks efficiently.
 
 \subsubsection{Wake up Scheduling Schemes:}
 
 
 \subsubsection{Wake up Scheduling Schemes:}
 

-\indent This section demonstrates the scheduling schemes from point of view of schedule composition process and the framework of the wake up schedule. In these scheduling schemes, the wake up interval refers to the period of time at which the radio unit is turned on so as to sends or receives the packets. Whilst, the sleep interval refers to a period of time at which the radio unit is turned off so as to retain the energy of wireless sensor node. Some schemes divide the time into equal length durations of time that called slotted schemes; on the other hand, the other schemes works with the time in continuous way that called unslotted schemes. The sleep and wake up intervals are defined for the unslotted schemes, whilst for the slotted schemes, these intervals are represented as multiples of slots. The wake up schedule represents a set of a wake up and sleep intervals, which are produced for one period. This schedule replicates to each period and it can be changed by the wake up scheduling scheme during the different periods of time. The final goal of this wake up schedule is to permit to exchange the data among the wireless sensor nodes in WSN during the wake up interval. As shown in figure~\ref{wsns}, the requirement for synchronization has been categorized the wake up scheduling into three categories~\cite{ref57}:
+\indent This section demonstrates the scheduling schemes from point of view of schedule composition process and the framework of the wake up schedule. In these scheduling schemes, the wake up interval refers to the period of time at which the radio unit is turned on so as to send or receive the packets. Whilst, the sleep interval refers to a period of time at which the radio unit is turned off so as to retain the energy of wireless sensor node. Some schemes divide the time into equal length durations of time and are called slotted schemes; on the other hand, the other schemes work with the time in continuous way and are called unslotted schemes. The sleep and wake up intervals are defined for the unslotted schemes, whilst for the slotted schemes, these intervals are represented as multiple slots. The wake up schedule represents a set of a wake up and sleep intervals, which are produced for one period. This schedule replicates to each period and it can be changed by the wake up scheduling scheme during the different periods of time. The final goal of this wake up schedule is to permit to exchange the data among the wireless sensor nodes in WSN during the wake up interval. As shown in figure~\ref{wsns}, the requirement for synchronization has been categorized the wake up scheduling into three categories~\cite{ref57}:

 
 \begin{enumerate} [(I)]
 
 
 \begin{enumerate} [(I)]
 


@@ -295,7 +296,7 @@ Data driven schemes are classified into two main approaches~\cite{ref59,ref22}:
 
 \subsection{Battery Repletion:}
 
 
 \subsection{Battery Repletion:}
 

-\indent In the last years, extensive researches have been focused on energy harvesting and wireless charging techniques. These solutions are representing alternate energy sources to recharge wireless sensor batteries without human intervention and instead of depending on the limited power supplied by a typical batteries~\cite{ref91,ref59}.
+\indent In the last years, extensive researches have been focused on energy harvesting and wireless charging techniques. These solutions represent alternate energy sources to recharge wireless sensor batteries without human intervention and instead of depending on the limited power supplied by a typical batteries~\cite{ref91,ref59}.

 
 \subsubsection{Energy Harvesting:} In energy harvesting, several sources of environmental energy have been developed so as to enable the wireless sensors to acquire energy from the surrounding environment like solar, wind energy, vibration based energy harvesting, radio signals for scavenging RF power, Thermoelectric generators, and shoe-mounted piezoelectric generator to power artificial organs~\cite{ref59}. 
 
 
 \subsubsection{Energy Harvesting:} In energy harvesting, several sources of environmental energy have been developed so as to enable the wireless sensors to acquire energy from the surrounding environment like solar, wind energy, vibration based energy harvesting, radio signals for scavenging RF power, Thermoelectric generators, and shoe-mounted piezoelectric generator to power artificial organs~\cite{ref59}. 
 


@@ -312,10 +313,10 @@ direction; and cognitive radio and Cooperative communications schemes~\cite{ref2
 \indent The relay nodes placement and the mobility of the sink can be considered as energy-efficient strategies, which are used to minimize the consumption of the energy and extend the lifetime of WSNs.
 %\begin{enumerate} [(I)]
 \subsubsection{Relay node placement:} 
 \indent The relay nodes placement and the mobility of the sink can be considered as energy-efficient strategies, which are used to minimize the consumption of the energy and extend the lifetime of WSNs.
 %\begin{enumerate} [(I)]
 \subsubsection{Relay node placement:} 

-In WSN, some wireless sensor nodes in a certain region may be died and this will lead to create a hole in the WSN. This problem can be solved by placing the wireless sensor nodes in sensing field using optimal distribution or by deploying a small number of relay wireless sensor nodes with a powerful capabilities whose major goal is the communication with other wireless sensor nodes or relay nodes~\cite{ref52}. This solution can enhance the power balancing and avoiding the overloaded wireless sensor nodes in a particular region in WSN.
+In WSN, some wireless sensor nodes in a certain region may be died and this creates a hole in the WSN. This problem can be solved by placing the wireless sensor nodes in sensing field using optimal distribution or by deploying a small number of relay wireless sensor nodes with a powerful capabilities whose major goal is the communication with other wireless sensor nodes or relay nodes~\cite{ref52}. This solution can enhance the power balancing and avoiding the overloaded wireless sensor nodes in a particular region in WSN.

 
 \subsubsection{Sink Mobility:} 
 
 \subsubsection{Sink Mobility:} 

-In WSNs that included a static sink, the wireless sensor nodes, which are near the sink drain their power more rapidly compared with other sensor nodes that lead to WSN disconnection and limited network lifetime~\cite{ref53}. This is happening due to sending all the data in WSN to the sink that maximizes the overload on the wireless sensor nodes close to sink. In order to overcome this problem and prolong the network lifetime; it is necessary to use a mobile sink to move within the area of WSN so as to collect the sensory data from the static sensor nodes over a single hop communication.  The mobile sink avoids the multi-hop communication and conserves the energy at the static sensor nodes close the base station, extending the lifetime of WSN~\cite{ref54,ref55}.
+In WSNs includes a static sink, the wireless sensor nodes, which are near the sink drain their power more rapidly compared with other sensor nodes that lead to WSN disconnection and limited network lifetime~\cite{ref53}. Sending all the data in WSN to the sink maximizes the overload on the wireless sensor nodes close to sink. In order to overcome this problem and prolong the network lifetime, it is necessary to use a mobile sink to move within the area of WSN so as to collect the sensory data from the static sensor nodes over a single hop communication.  The mobile sink avoids the multi-hop communication and conserves the energy at the static sensor nodes close to the base station, extending the lifetime of WSN~\cite{ref54,ref55}.

 
 %\end{enumerate}
 
 
 %\end{enumerate}
 


@@ -324,27 +325,22 @@ In WSNs that included a static sink, the wireless sensor nodes, which are near t
 
 \section{Network Lifetime in Wireless Sensor Networks}  
 \label{ch1:sec:07}
 
 \section{Network Lifetime in Wireless Sensor Networks}  
 \label{ch1:sec:07}

-\indent The limited resources in WSNs have been addressed, and one of the main challenges in WSNs is the limited power resource. For this reason, there are extensive researches have been proposed in order to prolong the network lifetime by means of designing and implementing energy-efficient protocols. The reason for these large number of proposed protocols to maximize the network lifetime is the difficulty and sometime impossibility to replace or recharge the batteries of wireless sensor nodes especially in the large WSN and hostile environment. 
+\indent The limited resources in WSNs have been addressed, and one of the main challenges in WSNs is the limited power resource. For this reason, extensive researches have been proposed in order to prolong the network lifetime by means of designing and implementing energy-efficient protocols. The reason for these large number of proposed protocols to maximize the network lifetime is the difficulty and sometime impossibility to replace or recharge the batteries of wireless sensor nodes especially in the large WSN and hostile environment. 

 
 

-\indent The authors have been defined the network lifetime in different contexts and use it as a metric to evaluate the performance of their protocols. Based on the previous proposed works in prolonging the network lifetime; Various definitions are exist for the lifetime of a sensor network~\cite{ref92,ref93} such as:~\textbf{(i)} is the time spent by WSN until the death of the first wireless sensor node ( or cluster head ) in the network due to its energy depletion.~\textbf{(ii)} is the time spent by WSN and has at least a specific set $\beta$ of alive sensor nodes in WSN.~\textbf{(iii)} is the time spent by WSN until the death of all wireless sensor nodes in WSN because they have been depleted their energy.~\textbf{(iv)} for  k-coverage is the time spent by WSN in covering the area of interest by at least $k$ sensor nodes.~\textbf{(v)} for 100 $\%$ coverage is the time spent by WSN in covering each target or the whole area by at least one sensor node.~\textbf{(vi)} for $\alpha$-coverage: the total time by which at least $\alpha$ part of the sensing field is covered by at least one node; or is the time spent by WSN until the coverage ratio becomes less than a predetermined threshold $\alpha$.~\textbf{(vii)} the working time spent by the system before either the coverage ratio or delivery ratio become less than a predetermined threshold.~\textbf{(viii)} the number of the successful data gathering trips.~\textbf{(ix)} the number of sent packets.~\textbf{(x)} the percentage of wireless sensor nodes that have a route to the sink.~\textbf{(xi)} the prediction of the total period of time during which the probability of ensuring the connectivity and k-coverage concurrently is at least $\alpha$.~\textbf{(xii)} the time spent by WSN until loosing the connectivity or the coverage.~\textbf{(xiii)} the time spent by WSN until acceptable event detection ratio is not acceptable in the network.~\textbf{(xiv)} the time spent by WSN and the application requirement has been met.
+\indent The authors have defined the network lifetime in different contexts and use it as a metric to evaluate the performance of their protocols. Based on the previous proposed works in prolonging the network lifetime, Various definitions are exist for the lifetime of a sensor network~\cite{ref92,ref93} such as:~\textbf{(i)} is the time spent by WSN until the death of the first wireless sensor node ( or cluster head ) in the network due to its energy depletion.~\textbf{(ii)} is the time spent by WSN and has at least a specific set $\beta$ of alive sensor nodes in WSN.~\textbf{(iii)} is the time spent by WSN until the death of all wireless sensor nodes in WSN because they have been depleted their energy.~\textbf{(iv)} for  k-coverage is the time spent by WSN in covering the area of interest by at least $k$ sensor nodes.~\textbf{(v)} for 100 $\%$ coverage is the time spent by WSN in covering each target or the whole area by at least one sensor node.~\textbf{(vi)} for $\alpha$-coverage: the total time by which at least $\alpha$ part of the sensing field is covered by at least one node; or is the time spent by WSN until the coverage ratio becomes less than a predetermined threshold $\alpha$.~\textbf{(vii)} the working time spent by the system before either the coverage ratio or delivery ratio become less than a predetermined threshold.~\textbf{(viii)} the number of the successful data gathering trips.~\textbf{(ix)} the number of sent packets.~\textbf{(x)} the percentage of wireless sensor nodes that have a route to the sink.~\textbf{(xi)} the prediction of the total period of time during which the probability of ensuring the connectivity and k-coverage concurrently is at least $\alpha$.~\textbf{(xii)} the time spent by WSN until loosing the connectivity or the coverage.~\textbf{(xiii)} the time spent by WSN until acceptable event detection ratio is not acceptable in the network.~\textbf{(xiv)} the time spent by WSN and the application requirement has been met.

 
 

-\indent According to the above definitions for network lifetime, There is no universal definition reflects the requirements of each application and the effects of the environment. In real WSN, the network lifetime reflects a set of a particular circumstances of the environment. Accordingly, the current definitions are applicable for the WSNs that meet a particular conditions. However, many more parameters, which are affecting on the network lifetime of WSN such as~\cite{ref92}:  heterogeneity, node mobility, topology changes, application characteristics, quality of service, and completeness.
+\indent According to the above definitions for network lifetime, there is no universal definition reflects the requirements of each application and the effects of the environment. In real WSN, the network lifetime reflects a set of a particular circumstances of the environment. Accordingly, the current definitions are applicable for the WSNs that meet a particular conditions. However, many more parameters, which are affecting on the network lifetime of WSN such as~\cite{ref92}:  heterogeneity, node mobility, topology changes, application characteristics, quality of service, and completeness.

 
 The network lifetime has been defined in this dissertation as the time spent by WSN until the coverage ratio becomes less than a predetermined threshold $\alpha$.
 
 
 \section{Coverage in Wireless Sensor Networks } 
 \label{ch1:sec:8}
 
 The network lifetime has been defined in this dissertation as the time spent by WSN until the coverage ratio becomes less than a predetermined threshold $\alpha$.
 
 
 \section{Coverage in Wireless Sensor Networks } 
 \label{ch1:sec:8}

-\indent Energy efficiency is  a crucial issue in wireless sensor networks since sensory
-consumption, in  order to  maximize the network lifetime, represents  the major
-difficulty when designing WSNs. As a consequence, one of the scientific research
-challenges in  WSNs, which has  been addressed by a large amount of literature
-during the  last few  years, is  the design of  energy efficient  approaches for
-coverage and connectivity~\cite{ref94,ref101}. Coverage reflects how well a
-sensor  field is  monitored. On the one hand, we want to monitor the  area of
+%\indent Energy efficiency is  a crucial issue in wireless sensor networks since sensory consumption, in  order to  maximize the network lifetime, represents  the major difficulty when designing WSNs. As a consequence, one of the scientific research challenges in  WSNs, which has  been addressed by a large amount of literature during the  last few  years, is  the design of  energy efficient  approaches for coverage and connectivity~\cite{ref94,ref101}. 
+Coverage reflects how well a sensor  field is  monitored. On the one hand, we want to monitor the  area of

 interest in the most efficient way~\cite{ref95}.  On the other hand, we want to
 interest in the most efficient way~\cite{ref95}.  On the other hand, we want to

-use as little energy as possible. Sensor nodes are battery-powered  with no
-means  of recharging  or replacing,  usually  due to  environmental (hostile  or
+use few energy as possible. Sensor nodes are battery-powered  with no
+mean  of recharging  or replacing,  usually  due to  environmental (hostile  or

 unpractical environments)  or cost reasons.   Therefore, it is desired  that the
 WSNs are deployed  with high densities so as to  exploit the overlapping sensing
 regions of some sensor  nodes to save energy by turning off  some of them during
 unpractical environments)  or cost reasons.   Therefore, it is desired  that the
 WSNs are deployed  with high densities so as to  exploit the overlapping sensing
 regions of some sensor  nodes to save energy by turning off  some of them during


@@ -356,7 +352,7 @@ The most discussed coverage problems in literature can be classified into three
 \item \textbf{Barrier coverage}~\cite{ref99,ref100} to prevent intruders from entering into the region of interest. 
 \end{enumerate}
 
 \item \textbf{Barrier coverage}~\cite{ref99,ref100} to prevent intruders from entering into the region of interest. 
 \end{enumerate}
 

-\indent The sensing quality and capability can be assessed by a sensing coverage models due to discovering the mathematical relationship between the point and the sensor node in the sensing field. In the real world, there are sometimes an obstacles in the environment that affect on the sensing range~\cite{ref104}, so there are several sensing coverage models have been suggested according to application requirements and physical working environment such as~\cite{ref103}: boolean sector coverage, boolean disk coverage, attenuated disk coverage, truncated attenuated disk, detection coverage, and estimation coverage Models. However, There are two common sensing coverage models have been used for simulating the performance of wireless sensors~\cite{ref104,ref105,ref106}:
+\indent The sensing quality and capability can be assessed by a sensing coverage models due to discovering the mathematical relationship between the point and the sensor node in the sensing field. In the real world, there are sometimes an obstacles in the environment that affect on the sensing range~\cite{ref104}, so several sensing coverage models have been suggested according to application requirements and physical working environment such as~\cite{ref103}: boolean sector coverage, boolean disk coverage, attenuated disk coverage, truncated attenuated disk, detection coverage, and estimation coverage Models. However, two main sensing coverage models have been used for simulating the performance of wireless sensors~\cite{ref104,ref105,ref106}:

 
 \begin{enumerate}[(A)] 
 \item \textbf{The Binary Disc Sensing Model:}
 
 \begin{enumerate}[(A)] 
 \item \textbf{The Binary Disc Sensing Model:}


@@ -390,7 +386,9 @@ where $R_u$ is a measure of the uncertainty in sensor detection, $\alpha = d(s_i
 
 \end{enumerate}
 
 
 \end{enumerate}
 

-The coverage protocols that proposed in this dissertation have been used the binary disc sensing model as a sensing coverage model for each wireless sensor node in WSN.
+The coverage protocols proposed in this dissertation use the binary disc sensing model as a sensing coverage model for each wireless sensor node in WSN.
+
+

 
 
 \section{Design Issues for Coverage Problems:}
 
 
 \section{Design Issues for Coverage Problems:}


@@ -400,13 +398,14 @@ The coverage protocols that proposed in this dissertation have been used the bin
 \begin{enumerate}[(i)]
 \item $\textbf{Coverage Type}$ refers to determining what is it exactly that you are trying to cover. Typically, it may be required to monitor a whole area, observe a set of targets, or look for a breach among a barrier. 
   
 \begin{enumerate}[(i)]
 \item $\textbf{Coverage Type}$ refers to determining what is it exactly that you are trying to cover. Typically, it may be required to monitor a whole area, observe a set of targets, or look for a breach among a barrier. 
   

-\item $\textbf{Deployment Method}$ refers to the way by which the wireless sensor nodes are deployed over the target sensing field in order to build the wireless sensor network. Generally, the sensor nodes can be placed either deterministically or randomly in the target sensing field so as to construct the wireless sensor network~\cite{ref107}. The method of placing the sensor nodes can be selected based on the type of sensors, application, and the environment in which the wireless sensor nodes will work. In the deterministic placing, the deployment can be achieved in case of small number of sensor nodes and in friendly environment, whilst for a large number of sensor nodes; or the area of interest is Inaccessible or hostile, a random placing is the choice. The sensor network can be either dense or sparse. the dense deployment is preferred when it is important to detect the event or when it is required that the area covered by more than one sensor node. On the other hand, the sparse deployment is used when the dense deployment is expensive or when the maximum coverage is performed by a less number of sensor nodes.
+\item $\textbf{Deployment Method}$ refers to the way by which the wireless sensor nodes are deployed over the target sensing field in order to build the wireless sensor network. Generally, the sensor nodes can be placed either deterministically or randomly in the target sensing field so as to construct the wireless sensor network~\cite{ref107}. The method of placement the sensor nodes can be selected based on the type of sensors, application, and the environment. In the deterministic placing, the deployment can be achieved in case of small number of sensor nodes and in friendly environment, whilst for a large number of sensor nodes or where the area of interest is inaccessible or hostile, a random placing is the choice. The sensor network can be either dense or sparse. The dense deployment is preferred when the robust security is important or when it is required that the area is covered by more than one sensor node. On the other hand, the sparse deployment is used when the dense deployment is expensive or when the maximum coverage is performed by a less number of sensor nodes.

  
  

-\item $\textbf{Coverage Degree}$ refers to how many sensor nodes are required to cover a target or an area. This can be described as K-coverage in which the point in the sensing field is covered by at least K sensor nodes. Some applications need a high reliability to achieve their tasks, therefore, the sensing field is deployed densely so as to perform a K-coverage for this field. The simple coverage problem consists of a coverage degree equal to one (i.e., K=1), where every point in the sensing field is covered by only one sensor.
+\item $\textbf{Coverage Degree}$ refers to how many sensor nodes are required to cover a target or an area. This K-coverage mean the point in the sensing field is covered by at least K sensor nodes. Some applications need a high reliability to achieve their tasks, therefore, the sensing field is deployed densely so as to perform a K-coverage for this field. The simple coverage problem consists of a coverage degree equal to one (i.e., K=1), where every point in the sensing field is covered by at least one sensor.

 
 

-\item $\textbf{Coverage Ratio}$ is the percentage of the area of sensing field that fulfill the coverage degree of the application. If all the points in the sensing field are covered, the coverage ratio is $100\%$ and it can be called a complete coverage, otherwise it can be called as partial coverage.
+\item $\textbf{Coverage Ratio}$ is the percentage of the area of sensing field that fulfills the coverage degree of the application. If all the points in the sensing field are covered, the coverage ratio is $100\%$ and it can be called a complete coverage, otherwise it can be called as partial coverage.

 
 

-\item $\textbf{Network Connectivity}$ is to ensure the existence a path from any sensor node in WSN to the sink. The connected WSN refers to guarantee sending the sensed data from one sensor node to another sensor node directly toward the sink. It is necessary to consider the communication range of wireless sensor node is at least twice that of the sensing range ($R_c \geqslant 2R_s$) so as to imply connectivity among the sensor nodes during covering the sensing field~\cite{ref108}.
+\item $\textbf{Network Connectivity}$ is to ensure the existence of a path from any sensor node in WSN to the sink. The connected WSN refers to guarantee sending the sensed data from one sensor node to another sensor node directly toward the sink. 
+%It is necessary to consider the communication range of wireless sensor node is at least twice that of the sensing range ($R_c \geqslant 2R_s$) so as to imply connectivity among the sensor nodes during covering the sensing field~\cite{ref108}.

 
 \item $\textbf{Activity based Scheduling}$ is to schedule the activation and deactivation of sensor nodes during the network lifetime. The basic objective is to decide which sensors are in what states (active or sleeping mode) and for how long, so that the application coverage requirement can be
 guaranteed and the network lifetime can be prolonged. Various approaches, including centralized, distributed, and localized algorithms, have been proposed for activity scheduling. In distributed algorithms, each node in the network autonomously makes decisions on whether to turn on or turn off itself only using local neighbor information. In centralized algorithms, a central controller (a node or base station) informs every sensors of the time intervals to be activated.
 
 \item $\textbf{Activity based Scheduling}$ is to schedule the activation and deactivation of sensor nodes during the network lifetime. The basic objective is to decide which sensors are in what states (active or sleeping mode) and for how long, so that the application coverage requirement can be
 guaranteed and the network lifetime can be prolonged. Various approaches, including centralized, distributed, and localized algorithms, have been proposed for activity scheduling. In distributed algorithms, each node in the network autonomously makes decisions on whether to turn on or turn off itself only using local neighbor information. In centralized algorithms, a central controller (a node or base station) informs every sensors of the time intervals to be activated.


@@ -415,7 +414,7 @@ guaranteed and the network lifetime can be prolonged. Various approaches, includ
 
 \section{Energy Consumption Modeling:} 
 \label{ch1:sec:9}
 
 \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:
+\indent The WSNs have been received a lot of interest because their low energy consumption sensor nodes.  Since sensor node has a limited power battery, therefore, one of the most critical issues in WSNs is 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 are used~\cite{ref140}:  transmission, reception, listening, and sleeping; in addition, two states should be taken into account: computation and sensed data acquisition. The main tasks of each of these states include:

 
 \begin{enumerate}[(i)]
 
 
 \begin{enumerate}[(i)]
 


@@ -446,7 +445,8 @@ guaranteed and the network lifetime can be prolonged. Various approaches, includ
 \label{RDM}
 \end{figure}
 
 \label{RDM}
 \end{figure}
 

-\indent In this model, the radio consumes an energy to execute the transmitter and the power amplifier, and receiver circuitry consumes an energy to run the radio electronics, as described in figure~\ref{RDM}.  The channel model can be either free space ( power loss) or multipath fading ( power loss), based on the distance between the transmitter and receiver. This power loss can be controlled by setting the power amplifier so that if the distance is less than a threshold , the free space ($\varepsilon_{fs}$) model is used; otherwise, the multipath ($C$) model is used. Therefore, to transmit an k-bit packet with a distance d, the radio expends:
+\indent In this model, the radio consumes an energy to execute the transmitter and the power amplifier, and receiver circuitry consumes an energy to run the radio electronics, as described in figure~\ref{RDM}.  The channel model can be either free space ($d^2$ power loss) or multipath fading ($d^4$ power loss), based on the distance between the transmitter and receiver. This power loss can be controlled by setting the power amplifier so that if the distance is less than a threshold , the free space ($\varepsilon_{fs}$) model is used (i.e., $\varepsilon_{amp}$ = $\varepsilon_{fs}$); otherwise, the multipath ($\varepsilon_{mp}$) model is used (i.e., $\varepsilon_{amp}$ = $\varepsilon_{mp}$). Therefore, to transmit an k-bit packet with a distance d, the radio expends:
+

 
 \begin{equation}
 E_{tx}\left(k,d \right)  = \left \{ 
 
 \begin{equation}
 E_{tx}\left(k,d \right)  = \left \{ 


@@ -464,7 +464,7 @@ As well as to receive an k-bit packet, the radio expends
 \label{eq4-ch1} 
 \end{equation}
 
 \label{eq4-ch1} 
 \end{equation}
 

-The typical parameters are set as: $E_{elec}$ = 50 nJ/bit, $\varepsilon_{fs}$ = 10 pJ/bit/$m^2$, $\varepsilon_{fs}$ = 0.0013 pJ/bit/$m^4$. In addition, the energy for data aggregation is set as $E_{DA}$ = 5 nJ/bit.
+The typical parameters are set as: $E_{elec}$ = 50 nJ/bit, $\varepsilon_{fs}$ = 10 pJ/bit/$m^2$, $\varepsilon_{mp}$ = 0.0013 pJ/bit/$m^4$. In addition, the energy for data aggregation is set as $E_{DA}$ = 5 nJ/bit.

 
 \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. 
 
 
 \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. 
 

diff --git a/CHAPITRE_03.tex b/CHAPITRE_03.tex
index 68b15be8c95f20a96ef93ae76e3451b1ced7fa01..0d9bea91d3d700d503d0db6b007cc012e881465f 100644 (file)
--- a/CHAPITRE_03.tex
+++ b/CHAPITRE_03.tex
@@ -27,7 +27,7 @@ some existing protocols, simulation results  show that the proposed protocol can
 prolong the network lifetime and improve the coverage performance effectively.
 
 
 prolong the network lifetime and improve the coverage performance effectively.
 
 

-\section{DESCRIPTION OF THE DILCO PROTOCOL}
+\section{Description of the DiLCO Protocol}

 \label{ch3:sec:02}
 
 \noindent In this section, we introduce the DiLCO protocol which is distributed on  each subregion  in  the area  of interest.   It  is based  on two  efficient
 \label{ch3:sec:02}
 
 \noindent In this section, we introduce the DiLCO protocol which is distributed on  each subregion  in  the area  of interest.   It  is based  on two  efficient


@@ -242,7 +242,7 @@ Active-Sleep packet to know its state for the coming sensing phase.
 
 
 
 
 
 

-\section{COVERAGE PROBLEM FORMULATION}
+\section{Primary Points based Coverage Problem Formulation}

 \label{ch3:sec:03}
 \indent Our model is based on the model proposed by \cite{ref156} where the
 objective is  to find a  maximum number of  disjoint cover sets.   To accomplish
 \label{ch3:sec:03}
 \indent Our model is based on the model proposed by \cite{ref156} where the
 objective is  to find a  maximum number of  disjoint cover sets.   To accomplish


@@ -504,7 +504,9 @@ Where: $A_r$ is the number of active sensors in the subregion $r$ during current
 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.
 
 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.
 

-\subsubsection{Coverage Ratio} 
+\begin{enumerate}[i)]
+\item {{\bf Coverage Ratio}}
+%\subsubsection{Coverage Ratio} 

 %\label{ch3:sec:04:02:01}
 In this experiment, Figure~\ref{Figures/ch3/R1/CR} shows the average coverage ratio for 150 deployed nodes.  
 \parskip 0pt    
 %\label{ch3:sec:04:02:01}
 In this experiment, Figure~\ref{Figures/ch3/R1/CR} shows the average coverage ratio for 150 deployed nodes.  
 \parskip 0pt    


@@ -518,7 +520,8 @@ It can be seen that DiLCO protocol (with 4, 8, 16 and 32 subregions) gives nearl
 DiLCO-2 protocol gives near similar coverage ratio with other ones for first 10 rounds and then decreased until the died of the network in the round $18^{th}$ because it consumes more energy with the effect of the network disconnection. 
 As shown in the figure ~\ref{Figures/ch3/R1/CR}, as the number of subregions increases,  the coverage preservation for area of interest increases for a larger number of rounds. Coverage ratio decreases when the number of rounds increases due to dead nodes. Although some nodes are dead, thanks to  DiLCO-8,  DiLCO-16 and  DiLCO-32 protocols,  other nodes are  preserved to ensure the coverage. Moreover, when we have a dense sensor network, it leads to maintain the  coverage for a larger number of rounds. DiLCO-8,  DiLCO-16 and  DiLCO-32 protocols are slightly more efficient than other protocols, because they subdivide the area of interest into 8, 16 and 32~subregions; if one of the subregions becomes disconnected, the coverage may be still ensured in the remaining subregions.
 
 DiLCO-2 protocol gives near similar coverage ratio with other ones for first 10 rounds and then decreased until the died of the network in the round $18^{th}$ because it consumes more energy with the effect of the network disconnection. 
 As shown in the figure ~\ref{Figures/ch3/R1/CR}, as the number of subregions increases,  the coverage preservation for area of interest increases for a larger number of rounds. Coverage ratio decreases when the number of rounds increases due to dead nodes. Although some nodes are dead, thanks to  DiLCO-8,  DiLCO-16 and  DiLCO-32 protocols,  other nodes are  preserved to ensure the coverage. Moreover, when we have a dense sensor network, it leads to maintain the  coverage for a larger number of rounds. DiLCO-8,  DiLCO-16 and  DiLCO-32 protocols are slightly more efficient than other protocols, because they subdivide the area of interest into 8, 16 and 32~subregions; if one of the subregions becomes disconnected, the coverage may be still ensured in the remaining subregions.
 

-\subsubsection{Active Sensors Ratio} 
+\item {{\bf Active Sensors Ratio}}
+%\subsubsection{Active Sensors Ratio} 

  Figure~\ref{Figures/ch3/R1/ASR} shows the average active nodes ratio for 150 deployed nodes.
 \begin{figure}[h!]
 \centering
  Figure~\ref{Figures/ch3/R1/ASR} shows the average active nodes ratio for 150 deployed nodes.
 \begin{figure}[h!]
 \centering


@@ -528,7 +531,8 @@ As shown in the figure ~\ref{Figures/ch3/R1/CR}, as the number of subregions inc
 \end{figure} 
 The results presented in figure~\ref{Figures/ch3/R1/ASR} show the increase in the number of subregions led to increase in the number of active nodes. The DiLCO-16 and DiLCO-32 protocols have a larger number of active nodes but it preserve the coverage for a larger number of rounds. The advantage of the DiLCO-16 and DiLCO-32 protocols are that even if a network is disconnected in one subregion, the other ones usually continues the optimization process, and this extends the lifetime of the network.
 
 \end{figure} 
 The results presented in figure~\ref{Figures/ch3/R1/ASR} show the increase in the number of subregions led to increase in the number of active nodes. The DiLCO-16 and DiLCO-32 protocols have a larger number of active nodes but it preserve the coverage for a larger number of rounds. The advantage of the DiLCO-16 and DiLCO-32 protocols are that even if a network is disconnected in one subregion, the other ones usually continues the optimization process, and this extends the lifetime of the network.
 

-\subsubsection{The percentage of stopped simulation runs}
+\item {{\bf The percentage of stopped simulation runs}}
+%\subsubsection{The percentage of stopped simulation runs}

 Figure~\ref{Figures/ch3/R1/SR} illustrates the percentage of stopped simulation runs per round for 150 deployed nodes. 
 \begin{figure}[h!]
 \centering
 Figure~\ref{Figures/ch3/R1/SR} illustrates the percentage of stopped simulation runs per round for 150 deployed nodes. 
 \begin{figure}[h!]
 \centering


@@ -540,7 +544,8 @@ Figure~\ref{Figures/ch3/R1/SR} illustrates the percentage of stopped simulation
 It can be observed that the DiLCO-2  is the approach which stops first because it applied the optimization on only two subregions for the area of interest that is why it is first exhibits network disconnections.
 Thus, as explained previously, in case of the DiLCO-16 and DiLCO-32 with several subregions, the optimization effectively continues as long as a network in a subregion is still connected. This longer partial coverage optimization participates in extending the network lifetime. 
 
 It can be observed that the DiLCO-2  is the approach which stops first because it applied the optimization on only two subregions for the area of interest that is why it is first exhibits network disconnections.
 Thus, as explained previously, in case of the DiLCO-16 and DiLCO-32 with several subregions, the optimization effectively continues as long as a network in a subregion is still connected. This longer partial coverage optimization participates in extending the network lifetime. 
 

-\subsubsection{The Energy Consumption}
+\item {{\bf The Energy Consumption}}
+%\subsubsection{The Energy Consumption}

 We measure the energy consumed by the sensors during the communication, listening, computation, active, and sleep modes for different network densities and compare it for different subregions.  Figures~\ref{Figures/ch3/R1/EC95} and ~\ref{Figures/ch3/R1/EC50} illustrate the energy consumption for different network sizes for $Lifetime95$ and $Lifetime50$. 
 
 \begin{figure}[h!]
 We measure the energy consumed by the sensors during the communication, listening, computation, active, and sleep modes for different network densities and compare it for different subregions.  Figures~\ref{Figures/ch3/R1/EC95} and ~\ref{Figures/ch3/R1/EC50} illustrate the energy consumption for different network sizes for $Lifetime95$ and $Lifetime50$. 
 
 \begin{figure}[h!]


@@ -561,7 +566,8 @@ As shown in Figures~\ref{Figures/ch3/R1/EC95} and ~\ref{Figures/ch3/R1/EC50}, Di
 \end{figure} 
 In fact,  a distributed method on the subregions greatly reduces the number of communications, the time of listening and computation so thanks to the partitioning of the initial network in several independent subnetworks. 
 
 \end{figure} 
 In fact,  a distributed method on the subregions greatly reduces the number of communications, the time of listening and computation so thanks to the partitioning of the initial network in several independent subnetworks. 
 

-\subsubsection{Execution Time}
+\item {{\bf Execution Time}}
+%\subsubsection{Execution Time}

 In this experiment, the execution time of the our distributed optimization approach has been studied. Figure~\ref{Figures/ch3/R1/T} gives the average execution times in seconds for the decision phase (solving of the optimization problem) during one round. They are given for the different approaches and various numbers of sensors. The original execution time is computed as described in section \ref{ch3:sec:04:02}.
 %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 $\left( \frac{35330}{2} \times 6\right)$ and reported on Figure~\ref{fig8} for different network sizes.
 
 In this experiment, the execution time of the our distributed optimization approach has been studied. Figure~\ref{Figures/ch3/R1/T} gives the average execution times in seconds for the decision phase (solving of the optimization problem) during one round. They are given for the different approaches and various numbers of sensors. The original execution time is computed as described in section \ref{ch3:sec:04:02}.
 %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 $\left( \frac{35330}{2} \times 6\right)$ and reported on Figure~\ref{fig8} for different network sizes.
 


@@ -576,8 +582,8 @@ We can see from figure~\ref{Figures/ch3/R1/T}, that the DiLCO-32 has very low ex
 
 The DiLCO-32 protocol has more suitable times at the same time it turn on redundant nodes more.  We think that in distributed fashion the solving of the  optimization problem in a subregion can be tackled by sensor nodes. Overall, to be able to deal with very large networks,  a distributed method is clearly required.
 
 
 The DiLCO-32 protocol has more suitable times at the same time it turn on redundant nodes more.  We think that in distributed fashion the solving of the  optimization problem in a subregion can be tackled by sensor nodes. Overall, to be able to deal with very large networks,  a distributed method is clearly required.
 

-
-\subsubsection{The Network Lifetime}
+\item {{\bf The Network Lifetime}}
+%\subsubsection{The Network Lifetime}

 In figure~\ref{Figures/ch3/R1/LT95} and \ref{Figures/ch3/R1/LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes. 
 
 \begin{figure}[h!]
 In figure~\ref{Figures/ch3/R1/LT95} and \ref{Figures/ch3/R1/LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes. 
 
 \begin{figure}[h!]


@@ -599,7 +605,7 @@ Comparison shows that DiLCO-16 protocol, which uses 16 leaders, is the best one
 \label{Figures/ch3/R1/LT50}
 \end{figure} 
 
 \label{Figures/ch3/R1/LT50}
 \end{figure} 
 

-
+\end{enumerate}

 
 \subsection{Performance Analysis for Primary Point Models}
 \label{ch3:sec:04:06}
 
 \subsection{Performance Analysis for Primary Point Models}
 \label{ch3:sec:04:06}


@@ -607,7 +613,12 @@ Comparison shows that DiLCO-16 protocol, which uses 16 leaders, is the best one
 In this section, we are studied the performance of DiLCO~16 approach for a different primary point models. The objective of this comparison is to select the suitable primary point model to be used by DiLCO protocol. 
 
 In this comparisons, DiLCO-16 protocol are used with five models which are called Model~1( With 5 Primary Points), Model~2 ( With 9 Primary Points), Model~3 ( With 13 Primary Points), Model~4 ( With 17 Primary Points), and Model~5 ( With 21 Primary Points). 
 In this section, we are studied the performance of DiLCO~16 approach for a different primary point models. The objective of this comparison is to select the suitable primary point model to be used by DiLCO protocol. 
 
 In this comparisons, DiLCO-16 protocol are used with five models which are called Model~1( With 5 Primary Points), Model~2 ( With 9 Primary Points), Model~3 ( With 13 Primary Points), Model~4 ( With 17 Primary Points), and Model~5 ( With 21 Primary Points). 

-\subsubsection{Coverage Ratio} 
+
+
+\begin{enumerate}[i)]
+
+\item {{\bf Coverage Ratio}}
+%\subsubsection{Coverage Ratio} 

 In this experiment, we Figure~\ref{Figures/ch3/R2/CR} shows the average coverage ratio for 150 deployed nodes.  
 \parskip 0pt    
 \begin{figure}[h!]
 In this experiment, we Figure~\ref{Figures/ch3/R2/CR} shows the average coverage ratio for 150 deployed nodes.  
 \parskip 0pt    
 \begin{figure}[h!]


@@ -619,7 +630,8 @@ In this experiment, we Figure~\ref{Figures/ch3/R2/CR} shows the average coverage
 It is shown that all models provide a very near coverage ratios during the network lifetime, with very small superiority for the models with higher number of primary points. Moreover, when the number of rounds increases, coverage ratio produced by Model~3, Model~4, and Model~5 decreases in comparison with Model~1 and Model~2 due to the high energy consumption during the listening to take the decision after finishing optimization process for larger number of primary points. As shown in figure ~\ref{Figures/ch3/R2/CR}, Coverage ratio decreases when the number of rounds increases due to dead nodes. Although  some nodes are dead,
 thanks to  Model~2, which is slightly more efficient than other Models, because it is balanced between the number of rounds and the better coverage ratio in comparison with other Models.
 
 It is shown that all models provide a very near coverage ratios during the network lifetime, with very small superiority for the models with higher number of primary points. Moreover, when the number of rounds increases, coverage ratio produced by Model~3, Model~4, and Model~5 decreases in comparison with Model~1 and Model~2 due to the high energy consumption during the listening to take the decision after finishing optimization process for larger number of primary points. As shown in figure ~\ref{Figures/ch3/R2/CR}, Coverage ratio decreases when the number of rounds increases due to dead nodes. Although  some nodes are dead,
 thanks to  Model~2, which is slightly more efficient than other Models, because it is balanced between the number of rounds and the better coverage ratio in comparison with other Models.
 

-\subsubsection{Active Sensors Ratio} 
+\item {{\bf Active Sensors Ratio}}
+%\subsubsection{Active Sensors Ratio} 

  Figure~\ref{Figures/ch3/R2/ASR} shows the average active nodes ratio for 150 deployed nodes.
 \begin{figure}[h!]
 \centering
  Figure~\ref{Figures/ch3/R2/ASR} shows the average active nodes ratio for 150 deployed nodes.
 \begin{figure}[h!]
 \centering


@@ -632,7 +644,8 @@ The results presented in figure~\ref{Figures/ch3/R2/ASR} show the superiority of
 model with less number of primary points uses less active nodes than the other models, which uses a more number of primary points to represent the area of the sensor. According to the results that presented in figure~\ref{Figures/ch3/R2/CR}, we observe that although the Model~1 continue to a larger number of rounds, but it has less coverage ratio compared with other models. The advantage of the Model~2 approach is to use less number of active nodes for each round compared with Model~3,  Model~4, and Model~5; and this led to continue for a larger number of rounds with extending the network lifetime. Model~2 has a better coverage ratio compared to Model~1 and acceptable number of rounds.
 
 
 model with less number of primary points uses less active nodes than the other models, which uses a more number of primary points to represent the area of the sensor. According to the results that presented in figure~\ref{Figures/ch3/R2/CR}, we observe that although the Model~1 continue to a larger number of rounds, but it has less coverage ratio compared with other models. The advantage of the Model~2 approach is to use less number of active nodes for each round compared with Model~3,  Model~4, and Model~5; and this led to continue for a larger number of rounds with extending the network lifetime. Model~2 has a better coverage ratio compared to Model~1 and acceptable number of rounds.
 
 

-\subsubsection{The percentage of stopped simulation runs}
+\item {{\bf he percentage of stopped simulation runs}}
+%\subsubsection{The percentage of stopped simulation runs}

 In this study, we want to show the effect of increasing the primary points on the number of stopped simulation runs for each round. Figure~\ref{Figures/ch3/R2/SR} illustrates the percentage of stopped simulation runs per round for 150 deployed nodes. 
 
 \begin{figure}[h!]
 In this study, we want to show the effect of increasing the primary points on the number of stopped simulation runs for each round. Figure~\ref{Figures/ch3/R2/SR} illustrates the percentage of stopped simulation runs per round for 150 deployed nodes. 
 
 \begin{figure}[h!]


@@ -645,7 +658,8 @@ In this study, we want to show the effect of increasing the primary points on th
 As shown in Figure~\ref{Figures/ch3/R2/SR}, when the number of primary points are increased, the percentage of the stopped simulation runs per round is increased. The reason behind the increase is the increase in the sensors dead when the primary points increases. We are observed that the Model~1 is a better than other models because it conserve more energy by turn on less number of sensors during the sensing phase, but in the same time it preserve the coverage with a less coverage ratio in comparison with other models. Model~2 seems to be more suitable to be used in wireless sensor networks.
 
 
 As shown in Figure~\ref{Figures/ch3/R2/SR}, when the number of primary points are increased, the percentage of the stopped simulation runs per round is increased. The reason behind the increase is the increase in the sensors dead when the primary points increases. We are observed that the Model~1 is a better than other models because it conserve more energy by turn on less number of sensors during the sensing phase, but in the same time it preserve the coverage with a less coverage ratio in comparison with other models. Model~2 seems to be more suitable to be used in wireless sensor networks.
 
 

-\subsubsection{The Energy Consumption}
+\item {{\bf The Energy Consumption}}
+%\subsubsection{The Energy Consumption}

 In this experiment, we study the effect of increasing the primary points to represent the area of the sensor on the energy consumed by the wireless sensor network for different network densities.  Figures~\ref{Figures/ch3/R2/EC95} and ~\ref{Figures/ch3/R2/EC50} illustrate the energy consumption for different network sizes for $Lifetime95$ and $Lifetime50$.
 \begin{figure}[h!]
 \centering
 In this experiment, we study the effect of increasing the primary points to represent the area of the sensor on the energy consumed by the wireless sensor network for different network densities.  Figures~\ref{Figures/ch3/R2/EC95} and ~\ref{Figures/ch3/R2/EC50} illustrate the energy consumption for different network sizes for $Lifetime95$ and $Lifetime50$.
 \begin{figure}[h!]
 \centering


@@ -663,8 +677,8 @@ In this experiment, we study the effect of increasing the primary points to repr
 
 We see from the results presented in Figures~\ref{Figures/ch3/R2/EC95} and \ref{Figures/ch3/R2/EC50}, The energy consumed by the network for each round increases when the primary points increases, because the decision for optimization process will takes more time leads to consume more energy during the listening mode. The results show that Model~1 is the most competitive from the energy consumption point of view but the worst one from coverage ratio point of view. The other Models have a high energy consumption  due  to the increase in the primary points, which are led to increase the energy consumption during the listening mode before producing the solution by solving the optimization process. In fact, we see that Model~2 is a good candidate to be used by wireless sensor network because it preserve a good coverage ratio and a suitable energy consumption in comparison with other models. 
 
 
 We see from the results presented in Figures~\ref{Figures/ch3/R2/EC95} and \ref{Figures/ch3/R2/EC50}, The energy consumed by the network for each round increases when the primary points increases, because the decision for optimization process will takes more time leads to consume more energy during the listening mode. The results show that Model~1 is the most competitive from the energy consumption point of view but the worst one from coverage ratio point of view. The other Models have a high energy consumption  due  to the increase in the primary points, which are led to increase the energy consumption during the listening mode before producing the solution by solving the optimization process. In fact, we see that Model~2 is a good candidate to be used by wireless sensor network because it preserve a good coverage ratio and a suitable energy consumption in comparison with other models. 
 

-
-\subsubsection{Execution Time}
+\item {{\bf Execution Time}}
+%\subsubsection{Execution Time}

 In this experiment, we have studied the impact of the increase in primary points on the execution time of DiLCO protocol. Figure~\ref{Figures/ch3/R2/T} gives the average execution times in seconds for the decision phase (solving of the optimization problem) during one round. The original execution time is computed as described in section \ref{ch3:sec:04:02}. 
 
 \begin{figure}[h!]
 In this experiment, we have studied the impact of the increase in primary points on the execution time of DiLCO protocol. Figure~\ref{Figures/ch3/R2/T} gives the average execution times in seconds for the decision phase (solving of the optimization problem) during one round. The original execution time is computed as described in section \ref{ch3:sec:04:02}. 
 
 \begin{figure}[h!]


@@ -677,7 +691,8 @@ In this experiment, we have studied the impact of the increase in primary points
 They are given for the different primary point models and various numbers of sensors. We can see from Figure~\ref{Figures/ch3/R2/T}, that Model~1 has lower execution time in comparison with other Models, because it used smaller number of primary points to represent the area of the sensor.  Conversely, the other primary point models  have been presented  a higher execution times.
 Moreover, Model~2 has more suitable times and coverage ratio that lead to continue for a larger number of rounds extending the network lifetime. We  think that a good primary point model, this one that balances between the coverage ratio and the number of rounds during the lifetime of the network.
 
 They are given for the different primary point models and various numbers of sensors. We can see from Figure~\ref{Figures/ch3/R2/T}, that Model~1 has lower execution time in comparison with other Models, because it used smaller number of primary points to represent the area of the sensor.  Conversely, the other primary point models  have been presented  a higher execution times.
 Moreover, Model~2 has more suitable times and coverage ratio that lead to continue for a larger number of rounds extending the network lifetime. We  think that a good primary point model, this one that balances between the coverage ratio and the number of rounds during the lifetime of the network.
 

-\subsubsection{The Network Lifetime}
+\item {{\bf The Network Lifetime}}
+%\subsubsection{The Network Lifetime}

 Finally, we will study the effect of increasing the primary points on the lifetime of the network. In Figure~\ref{Figures/ch3/R2/LT95} and in Figure~\ref{Figures/ch3/R2/LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes. 
 
 \begin{figure}[h!]
 Finally, we will study the effect of increasing the primary points on the lifetime of the network. In Figure~\ref{Figures/ch3/R2/LT95} and in Figure~\ref{Figures/ch3/R2/LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes. 
 
 \begin{figure}[h!]


@@ -699,13 +714,16 @@ Finally, we will study the effect of increasing the primary points on the lifeti
 As highlighted by figures~\ref{Figures/ch3/R2/LT95} and \ref{Figures/ch3/R2/LT50}, the network lifetime obviously increases when the size of the network increases, with  Model~1 that leads to the larger lifetime improvement.
 Comparison shows that the Model~1, which uses less number of primary points, is the best one because it is less energy consumption during the network lifetime. It is also the worst one from the point of view of coverage ratio. Our proposed Model~2 efficiently prolongs the network lifetime with a good coverage ratio in comparison with other models.
  
 As highlighted by figures~\ref{Figures/ch3/R2/LT95} and \ref{Figures/ch3/R2/LT50}, the network lifetime obviously increases when the size of the network increases, with  Model~1 that leads to the larger lifetime improvement.
 Comparison shows that the Model~1, which uses less number of primary points, is the best one because it is less energy consumption during the network lifetime. It is also the worst one from the point of view of coverage ratio. Our proposed Model~2 efficiently prolongs the network lifetime with a good coverage ratio in comparison with other models.
  

-
+\end{enumerate}

 
 \subsection{Performance Comparison with other Approaches}
 \label{ch3:sec:04:07}
 Based on the results, which are conducted from previous two subsections, \ref{ch3:sec:04:02} and \ref{ch3:sec:04:03}, we have found that DiLCO-16 protocol and DiLCO-32 protocol with Model~2 are the best candidates to be compared with other two approaches. The first approach, called DESK that proposed by ~\cite{DESK}, which is a full distributed coverage algorithm. The second approach, called GAF~\cite{GAF}, consists in dividing the region into fixed squares.   During the decision phase, in each square, one sensor is chosen to remain on during the sensing phase time. 
 
 
 \subsection{Performance Comparison with other Approaches}
 \label{ch3:sec:04:07}
 Based on the results, which are conducted from previous two subsections, \ref{ch3:sec:04:02} and \ref{ch3:sec:04:03}, we have found that DiLCO-16 protocol and DiLCO-32 protocol with Model~2 are the best candidates to be compared with other two approaches. The first approach, called DESK that proposed by ~\cite{DESK}, which is a full distributed coverage algorithm. The second approach, called GAF~\cite{GAF}, consists in dividing the region into fixed squares.   During the decision phase, in each square, one sensor is chosen to remain on during the sensing phase time. 
 

-\subsubsection{Coverage Ratio} 
+\begin{enumerate}[i)]
+
+\item {{\bf Coverage Ratio}}
+%\subsubsection{Coverage Ratio} 

 In this experiment, the average coverage ratio for 150 deployed nodes has been demonstrated figure~\ref{Figures/ch3/R3/CR}. 
  
 \parskip 0pt    
 In this experiment, the average coverage ratio for 150 deployed nodes has been demonstrated figure~\ref{Figures/ch3/R3/CR}. 
  
 \parskip 0pt    


@@ -720,7 +738,8 @@ It has been shown that DESK and GAF provide a little better coverage ratio with
 
 Moreover, when the number of rounds increases, coverage ratio produced by DESK and GAF protocols decreases. This is due to dead nodes. However, DiLCO-16 protocol and DiLCO-32 protocol maintain almost a good coverage. This is because they optimized the coverage and the lifetime in wireless sensor network by selecting the best representative sensor nodes to take the responsibility of coverage during the sensing phase and this will leads to continue for a larger number of rounds and prolonging the network lifetime; although some nodes are dead, sensor activity scheduling of our protocol chooses other nodes to ensure the coverage of the area of interest. 
 
 
 Moreover, when the number of rounds increases, coverage ratio produced by DESK and GAF protocols decreases. This is due to dead nodes. However, DiLCO-16 protocol and DiLCO-32 protocol maintain almost a good coverage. This is because they optimized the coverage and the lifetime in wireless sensor network by selecting the best representative sensor nodes to take the responsibility of coverage during the sensing phase and this will leads to continue for a larger number of rounds and prolonging the network lifetime; although some nodes are dead, sensor activity scheduling of our protocol chooses other nodes to ensure the coverage of the area of interest. 
 

-\subsubsection{Active Sensors Ratio} 
+\item {{\bf Active Sensors Ratio}}
+%\subsubsection{Active Sensors Ratio} 

 It is important to have as few active nodes as possible in each round, in  order to  minimize the energy consumption and maximize the network lifetime. Figure~\ref{Figures/ch3/R3/ASR} shows the average active nodes ratio for 150 deployed nodes. 
 
 \begin{figure}[h!]
 It is important to have as few active nodes as possible in each round, in  order to  minimize the energy consumption and maximize the network lifetime. Figure~\ref{Figures/ch3/R3/ASR} shows the average active nodes ratio for 150 deployed nodes. 
 
 \begin{figure}[h!]


@@ -732,7 +751,9 @@ It is important to have as few active nodes as possible in each round, in  order
 
 The results presented in figure~\ref{Figures/ch3/R3/ASR} show the superiority of the proposed DiLCO-16 protocol and DiLCO-32 protocol, in comparison with the other approaches.  We have observed that DESK and GAF have 37.5 \% and 44.5 \% active nodes and DiLCO-16 protocol and DiLCO-32 protocol compete perfectly with only 17.4 \%, 24.8 \% and 26.8 \%  active nodes for the first 14 rounds. Then as the number of rounds increases DiLCO-16 protocol and DiLCO-32 protocol have larger number of active nodes in comparison with DESK and GAF, especially from round $35^{th}$ 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 rounds $35^{th}$ and $32^{th}$ because there are many nodes are died due to the high energy consumption by the redundant nodes during the sensing phase. 
 
 
 The results presented in figure~\ref{Figures/ch3/R3/ASR} show the superiority of the proposed DiLCO-16 protocol and DiLCO-32 protocol, in comparison with the other approaches.  We have observed that DESK and GAF have 37.5 \% and 44.5 \% active nodes and DiLCO-16 protocol and DiLCO-32 protocol compete perfectly with only 17.4 \%, 24.8 \% and 26.8 \%  active nodes for the first 14 rounds. Then as the number of rounds increases DiLCO-16 protocol and DiLCO-32 protocol have larger number of active nodes in comparison with DESK and GAF, especially from round $35^{th}$ 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 rounds $35^{th}$ and $32^{th}$ because there are many nodes are died due to the high energy consumption by the redundant nodes during the sensing phase. 
 

-\subsubsection{The percentage of stopped simulation runs}
+
+\item {{\bf The percentage of stopped simulation runs}}
+%\subsubsection{The percentage of stopped simulation runs}

 The results presented in this experiment, is to show the comparison of DiLCO-16 protocol and DiLCO-32 protocol with other two approaches from point of view of stopped simulation runs per round.
 Figure~\ref{Figures/ch3/R3/SR} illustrates the percentage of stopped simulation
 runs per round for 150 deployed nodes. 
 The results presented in this experiment, is to show the comparison of DiLCO-16 protocol and DiLCO-32 protocol with other two approaches from point of view of stopped simulation runs per round.
 Figure~\ref{Figures/ch3/R3/SR} illustrates the percentage of stopped simulation
 runs per round for 150 deployed nodes. 


@@ -744,7 +765,9 @@ runs per round for 150 deployed nodes.
 \end{figure} 
 It has been observed that DESK is the approach, which stops first because it consumes more energy for communication as well as it turn on a large number of redundant nodes during the sensing phase. On the other  hand DiLCO-16 protocol and DiLCO-32 protocol have less stopped simulation runs in comparison with DESK and GAF because it distributed the optimization on several subregions in order to optimizes the coverage and the lifetime of the network by activating a less number of nodes during the sensing phase leading to extend the network lifetime and coverage preservation. The optimization effectively continues as long as a network in a subregion is still connected.
 
 \end{figure} 
 It has been observed that DESK is the approach, which stops first because it consumes more energy for communication as well as it turn on a large number of redundant nodes during the sensing phase. On the other  hand DiLCO-16 protocol and DiLCO-32 protocol have less stopped simulation runs in comparison with DESK and GAF because it distributed the optimization on several subregions in order to optimizes the coverage and the lifetime of the network by activating a less number of nodes during the sensing phase leading to extend the network lifetime and coverage preservation. The optimization effectively continues as long as a network in a subregion is still connected.
 

-\subsubsection{The Energy Consumption}
+
+\item {{\bf The Energy Consumption}}
+%\subsubsection{The Energy Consumption}

 In this experiment, we have studied the effect of the energy consumed by the wireless sensor network during the communication, computation, listening, active, and sleep modes for different network densities and compare it with other approaches. Figures~\ref{Figures/ch3/R3/EC95} and ~\ref{Figures/ch3/R3/EC50} illustrate the energy consumption for different network sizes for $Lifetime95$ and $Lifetime50$. 
 
 \begin{figure}[h!]
 In this experiment, we have studied the effect of the energy consumed by the wireless sensor network during the communication, computation, listening, active, and sleep modes for different network densities and compare it with other approaches. Figures~\ref{Figures/ch3/R3/EC95} and ~\ref{Figures/ch3/R3/EC50} illustrate the energy consumption for different network sizes for $Lifetime95$ and $Lifetime50$. 
 
 \begin{figure}[h!]


@@ -763,7 +786,9 @@ In this experiment, we have studied the effect of the energy consumed by the wir
 
 The results show that 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,  a distributed method on the subregions greatly reduces the number of communications and the time of listening so thanks to the partitioning of the initial network into several independent subnetworks. 
 
 
 The results show that 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,  a distributed method on the subregions greatly reduces the number of communications and the time of listening so thanks to the partitioning of the initial network into several independent subnetworks. 
 

-\subsubsection{The Network Lifetime}
+
+\item {{\bf The 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. In figures~\ref{Figures/ch3/R3/LT95} and \ref{Figures/ch3/R3/LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes.  
 
 \begin{figure}[h!]
 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. In figures~\ref{Figures/ch3/R3/LT95} and \ref{Figures/ch3/R3/LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes.  
 
 \begin{figure}[h!]


@@ -786,6 +811,8 @@ By choosing the best suited nodes, for each round, by optimizing the coverage an
 Comparison shows that DiLCO-16 protocol and DiLCO-32 protocol, which are used distributed optimization over the subregions, is the best one because it is robust to network disconnection during the network lifetime as well as it consumes 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.
 
 
 Comparison shows that DiLCO-16 protocol and DiLCO-32 protocol, which are used distributed optimization over the subregions, is the best one because it is robust to network disconnection during the network lifetime as well as it consumes 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.
 
 

+\end{enumerate}
+

 \section{Conclusion}
 \label{ch3:sec:05}
 A crucial problem in WSN is to schedule the sensing activities of the different nodes  in order to  ensure both  coverage of  the area  of interest  and longer
 \section{Conclusion}
 \label{ch3:sec:05}
 A crucial problem in WSN is to schedule the sensing activities of the different nodes  in order to  ensure both  coverage of  the area  of interest  and longer

diff --git a/CHAPITRE_04.tex b/CHAPITRE_04.tex
index 9e478cc8ba8d30c39e06a8df123b0c1fdc079a2a..d09e8b34eb46ae66635ff6f3244e868c8d993141 100644 (file)
--- a/CHAPITRE_04.tex
+++ b/CHAPITRE_04.tex
@@ -17,13 +17,13 @@ lifetime of  WSN. The decision process is  carried out by a  leader node, which
 during the rounds  of the sensing phase. Compared  with some existing protocols, simulation  results based  on  multiple criteria  (energy consumption,  coverage
 ratio, and  so on) show that  the proposed protocol can  prolong efficiently the network lifetime and improve the coverage performance.
 
 during the rounds  of the sensing phase. Compared  with some existing protocols, simulation  results based  on  multiple criteria  (energy consumption,  coverage
 ratio, and  so on) show that  the proposed protocol can  prolong efficiently the network lifetime and improve the coverage performance.
 

-\section{MuDiLCO protocol description}
+\section{MuDiLCO Protocol Description}

 \label{ch4:sec:02}
 \noindent In this section, we introduce the MuDiLCO protocol which is distributed on each subregion in the area of interest. It is based on two energy-efficient
 mechanisms: subdividing the area of interest into several subregions (like cluster architecture) using divide and conquer method, where the sensor nodes cooperate within each subregion as independent group in order to achieve a network leader election; and sensor activity scheduling for maintaining the coverage and prolonging the network lifetime, which are applied periodically. MuDiLCO protocol uses the same assumptions and network model that presented in chapter 3, section \ref{ch3:sec:02:01} and it has been used the primary point coverage model which is described in the same chapter, section \ref{ch3:sec:02:02}.
 
  
 \label{ch4:sec:02}
 \noindent In this section, we introduce the MuDiLCO protocol which is distributed on each subregion in the area of interest. It is based on two energy-efficient
 mechanisms: subdividing the area of interest into several subregions (like cluster architecture) using divide and conquer method, where the sensor nodes cooperate within each subregion as independent group in order to achieve a network leader election; and sensor activity scheduling for maintaining the coverage and prolonging the network lifetime, which are applied periodically. MuDiLCO protocol uses the same assumptions and network model that presented in chapter 3, section \ref{ch3:sec:02:01} and it has been used the primary point coverage model which is described in the same chapter, section \ref{ch3:sec:02:02}.
 
  

-\subsection{Background Idea}
+\subsection{Background Idea and Algorithm}

 \label{ch4:sec:02:02}
 The area of  interest can be divided using  the divide-and-conquer strategy into
 smaller  areas,  called  subregions,  and  then our MuDiLCO  protocol will be
 \label{ch4:sec:02:02}
 The area of  interest can be divided using  the divide-and-conquer strategy into
 smaller  areas,  called  subregions,  and  then our MuDiLCO  protocol will be


@@ -31,9 +31,15 @@ implemented in each subregion in a distributed way.
 
 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,
 
 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
+Decision, and Sensing. The information exchange among wireless sensor nodes is described in chapter 3, section \ref{ch3:sec:02:03:01}. The leader election in each subregion is explained in chapter 3, section \ref{ch3:sec:02:03:02}, but the difference in that the elected leader in each subregion is for each period. In decision phase, 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 on the algorithm's results, indicating if the sensor should be active or not in
+each round  of the  sensing phase. 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
 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. Each sensor node in the subregion will
+receive an Active-Sleep packet from WSNL, 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
+Active-Sleep 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.
 \begin{figure}[ht!]
 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!]


@@ -43,36 +49,66 @@ produce $T$ cover sets that will take the mission of sensing for $T$ rounds.
 \end{figure} 
 
 
 \end{figure} 
 
 

-This protocol minimizes the impact of unexpected node failure (not due to batteries
-running out of energy), because it works in periods. 
+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.
 

+ 
+
+\begin{algorithm}[h!]                
+ % \KwIn{all the parameters related to information exchange}
+%  \KwOut{$winer-node$ (: the id of the winner sensor node, which is the leader of current round)}
+  \BlankLine
+  %\emph{Initialize the sensor node and determine it's position and subregion} \; 
+  
+  \If{ $RE_j \geq E_{R}$ }{
+      \emph{$s_j.status$ = COMMUNICATION}\;
+      \emph{Send $INFO()$ packet to other nodes in the subregion}\;
+      \emph{Wait $INFO()$ packet from other nodes in the subregion}\; 
+      %\emph{UPDATE $RE_j$ for every sent or received INFO Packet}\;
+      %\emph{ Collect information and construct the list L for all nodes in the subregion}\;
+      
+      %\If{ the received INFO Packet = No. of nodes in it's subregion -1  }{
+      \emph{LeaderID = Leader election}\;
+      \If{$ s_j.ID = LeaderID $}{
+        \emph{$s_j.status$ = COMPUTATION}\;
+        \emph{$\left\{\left(X_{1,k},\dots,X_{T,k}\right)\right\}_{k \in J}$ =
+          Execute Integer Program Algorithm($T,J$)}\;
+        \emph{$s_j.status$ = COMMUNICATION}\;
+        \emph{Send $ActiveSleep()$ to each node $k$ in subregion a packet \\
+          with vector of activity scheduling $(X_{1,k},\dots,X_{T,k})$}\;
+        \emph{Update $RE_j $}\;
+      }          
+      \Else{
+        \emph{$s_j.status$ = LISTENING}\;
+        \emph{Wait $ActiveSleep()$ packet from the Leader}\;
+        % \emph{After receiving Packet, Retrieve the schedule and the $T$ rounds}\;
+        \emph{Update $RE_j $}\;
+      }  
+      %  }
+  }
+  \Else { Exclude $s_j$ from entering in the current sensing phase}
+  
+ %   \emph{return X} \;
+\caption{MuDiLCO($s_j$)}
+\label{alg:MuDiLCO}
+
+\end{algorithm}
+
+

 
 

-These phases can be described in more details as follow:
-\subsection{Information Exchange Phase}
-\label{ch4:sec:02:02:01}
-The information exchange among the wireless sensor nodes is similar to that one which is described in chapter 3, sections \ref{ch3:sec:02:03:01}.

 
 

-\subsection{Leader Election phase}
-\label{ch4:sec:02:02:02}
-The leader election in each subregion is similar to that one which is described in chapter 3, sections\ref{ch3:sec:02:03:02}, but the difference in that the elected leader in each subregion is for each period. 
+\subsection{Primary Points based Multiround Coverage Problem Formulation}
+%\label{ch4:sec:02:02}

 
 

-\subsection{Decision phase}
-\label{ch4:sec:02:02:03}
-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
-on the algorithm's results, indicating if  the sensor should be active or not in
-each round  of the  sensing phase.  The  integer program  is based on  the model
+According to our algorithm~\ref{alg:MuDiLCO}, the integer program is based on the model

 proposed by  \cite{ref156} with some modifications, where  the objective is
 proposed by  \cite{ref156} with some modifications, where  the objective is

-to find  a maximum  number of disjoint  cover sets.   To fulfill this  goal, the
+to find  a maximum number of disjoint cover sets. To fulfill this  goal, the

 authors proposed an integer  program which forces undercoverage and overcoverage
 of  targets to  become minimal  at  the same  time.  They  use binary  variables
 authors proposed an integer  program which forces undercoverage and overcoverage
 of  targets to  become minimal  at  the same  time.  They  use binary  variables

-$x_{jl}$ to indicate if  sensor $j$ belongs to cover set $l$.   In our model, we
+$x_{jl}$ to indicate if  sensor $j$ belongs to cover set $l$. In our model, we

 consider binary  variables $X_{t,j}$ to determine the  possibility of activating
 sensor $j$ during round $t$ of  a given sensing phase.  We also consider primary
 points as targets.  The  set of primary points is denoted by  $P$ and the set of
 consider binary  variables $X_{t,j}$ to determine the  possibility of activating
 sensor $j$ during round $t$ of  a given sensing phase.  We also consider primary
 points as targets.  The  set of primary points is denoted by  $P$ and the set of


@@ -180,56 +216,9 @@ to guarantee that the maximum number of points are covered during each round.
 In our simulations priority is given  to the coverage by choosing $W_{U}$ very
 large compared to $W_{\theta}$.
 
 In our simulations priority is given  to the coverage by choosing $W_{U}$ very
 large compared to $W_{\theta}$.
 

-\subsection{Sensing phase}
-\label{ch4:sec:02:02:04}
-The sensing phase consists of $T$ rounds. Each sensor node in the subregion will
-receive an Active-Sleep packet from WSNL, 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
-Active-Sleep packet is obtained.
-
-\begin{algorithm}[h!]                
- % \KwIn{all the parameters related to information exchange}
-%  \KwOut{$winer-node$ (: the id of the winner sensor node, which is the leader of current round)}
-  \BlankLine
-  %\emph{Initialize the sensor node and determine it's position and subregion} \; 
-  
-  \If{ $RE_j \geq E_{R}$ }{
-      \emph{$s_j.status$ = COMMUNICATION}\;
-      \emph{Send $INFO()$ packet to other nodes in the subregion}\;
-      \emph{Wait $INFO()$ packet from other nodes in the subregion}\; 
-      %\emph{UPDATE $RE_j$ for every sent or received INFO Packet}\;
-      %\emph{ Collect information and construct the list L for all nodes in the subregion}\;
-      
-      %\If{ the received INFO Packet = No. of nodes in it's subregion -1  }{
-      \emph{LeaderID = Leader election}\;
-      \If{$ s_j.ID = LeaderID $}{
-        \emph{$s_j.status$ = COMPUTATION}\;
-        \emph{$\left\{\left(X_{1,k},\dots,X_{T,k}\right)\right\}_{k \in J}$ =
-          Execute Integer Program Algorithm($T,J$)}\;
-        \emph{$s_j.status$ = COMMUNICATION}\;
-        \emph{Send $ActiveSleep()$ to each node $k$ in subregion a packet \\
-          with vector of activity scheduling $(X_{1,k},\dots,X_{T,k})$}\;
-        \emph{Update $RE_j $}\;
-      }          
-      \Else{
-        \emph{$s_j.status$ = LISTENING}\;
-        \emph{Wait $ActiveSleep()$ packet from the Leader}\;
-        % \emph{After receiving Packet, Retrieve the schedule and the $T$ rounds}\;
-        \emph{Update $RE_j $}\;
-      }  
-      %  }
-  }
-  \Else { Exclude $s_j$ from entering in the current sensing phase}
-  
- %   \emph{return X} \;
-\caption{MuDiLCO($s_j$)}
-\label{alg:MuDiLCO}
-
-\end{algorithm}
-
-

 
 

+ 
+ 

 
 \section{Experimental Study and Analysis}
 \label{ch4:sec:03}
 
 \section{Experimental Study and Analysis}
 \label{ch4:sec:03}


@@ -237,10 +226,8 @@ Active-Sleep packet is obtained.
 \subsection{Simulation Setup}
 \label{ch4:sec:03:01}
 We  conducted  a  series of  simulations  to  evaluate  the efficiency  and  the
 \subsection{Simulation Setup}
 \label{ch4:sec:03:01}
 We  conducted  a  series of  simulations  to  evaluate  the efficiency  and  the

-relevance  of   our  approach,  using  the  discrete   event  simulator  OMNeT++
-\cite{ref158}.     The simulation     parameters    are     summarized     in
-Table~\ref{table3}.  Each experiment  for  a network  is  run over  25~different
-random topologies and  the results presented hereafter are  the average of these
+relevance  of our  approach,  using  the  discrete   event  simulator  OMNeT++
+\cite{ref158}. The simulation  parameters are summarized in Table~\ref{table3}.  Each experiment  for  a network  is  run over  25~different random topologies and  the results presented hereafter are  the average of these

 25 runs.
 %Based on the results of our proposed work in~\cite{idrees2014coverage}, we found as the region of interest are divided into larger subregions as the network lifetime increased. In this simulation, the network are divided into 16 subregions. 
 We  performed  simulations for  five  different  densities  varying from  50  to
 25 runs.
 %Based on the results of our proposed work in~\cite{idrees2014coverage}, we found as the region of interest are divided into larger subregions as the network lifetime increased. In this simulation, the network are divided into 16 subregions. 
 We  performed  simulations for  five  different  densities  varying from  50  to


@@ -377,11 +364,16 @@ indicate the energy consumed by the whole network in round $t$.
 
 
 
 
 
 

-\subsection{Results analysis and Comparison }
+\subsection{Results Analysis and Comparison }

 \label{ch4:sec:03:02}
 
 \label{ch4:sec:03:02}
 

-\subsection{Coverage ratio} 
-\label{ch4:sec:03:02:01}
+
+\begin{enumerate}[(i)]
+
+\item {{\bf Coverage Ratio}}
+%\subsection{Coverage ratio} 
+%\label{ch4:sec:03:02:01}
+

 Figure~\ref{fig3} shows  the average coverage  ratio for 150 deployed  nodes. We
 can notice that for the first thirty rounds both DESK and GAF provide a coverage
 which is a little bit better than the one of MuDiLCO.  
 Figure~\ref{fig3} shows  the average coverage  ratio for 150 deployed  nodes. We
 can notice that for the first thirty rounds both DESK and GAF provide a coverage
 which is a little bit better than the one of MuDiLCO.  


@@ -399,13 +391,16 @@ rounds, and thus should extend the network lifetime.
 
 \begin{figure}[ht!]
 \centering
 
 \begin{figure}[ht!]
 \centering

- \includegraphics[scale=0.5] {Figures/ch4/R1/CR.pdf}   
+ \includegraphics[scale=0.6] {Figures/ch4/R1/CR.pdf}   

 \caption{Average coverage ratio for 150 deployed nodes}
 \label{fig3}
 \end{figure} 
 
 \caption{Average coverage ratio for 150 deployed nodes}
 \label{fig3}
 \end{figure} 
 

-\subsection{Active sensors ratio} 
-\label{ch4:sec:03:02:02}
+
+\item {{\bf Active sensors ratio}}
+%\subsection{Active sensors ratio} 
+%\label{ch4:sec:03:02:02}
+

 It is crucial to have as few active nodes as possible in each round, in order to 
 minimize    the    communication    overhead    and   maximize    the    network
 lifetime. Figure~\ref{fig4}  presents the active  sensor ratio for  150 deployed
 It is crucial to have as few active nodes as possible in each round, in order to 
 minimize    the    communication    overhead    and   maximize    the    network
 lifetime. Figure~\ref{fig4}  presents the active  sensor ratio for  150 deployed


@@ -420,13 +415,15 @@ nodes in a more efficient manner.
 
 \begin{figure}[ht!]
 \centering
 
 \begin{figure}[ht!]
 \centering

-\includegraphics[scale=0.5]{Figures/ch4/R1/ASR.pdf}  
+\includegraphics[scale=0.6]{Figures/ch4/R1/ASR.pdf}  

 \caption{Active sensors ratio for 150 deployed nodes}
 \label{fig4}
 \end{figure} 
 
 \caption{Active sensors ratio for 150 deployed nodes}
 \label{fig4}
 \end{figure} 
 

-\subsection{Stopped simulation runs}
-\label{ch4:sec:03:02:03}
+\item {{\bf Stopped simulation runs}}
+%\subsection{Stopped simulation runs}
+%\label{ch4: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
 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


@@ -439,30 +436,37 @@ still connected.
 
 \begin{figure}[ht!]
 \centering
 
 \begin{figure}[ht!]
 \centering

-\includegraphics[scale=0.5]{Figures/ch4/R1/SR.pdf} 
+\includegraphics[scale=0.6]{Figures/ch4/R1/SR.pdf} 

 \caption{Cumulative percentage of stopped simulation runs for 150 deployed nodes }
 \label{fig6}
 \end{figure} 
 
 \caption{Cumulative percentage of stopped simulation runs for 150 deployed nodes }
 \label{fig6}
 \end{figure} 
 

-\subsection{Energy consumption} \label{subsec:EC}
-\label{ch4:sec:03:02:04}
+
+ 
+\item {{\bf Energy consumption}} \label{subsec:EC} 
+%\subsection{Energy consumption} 
+%\label{ch4:sec:03:02:04}
+

 We  measure  the  energy  consumed  by the  sensors  during  the  communication,
 listening, computation, active, and sleep status for different network densities
 We  measure  the  energy  consumed  by the  sensors  during  the  communication,
 listening, computation, active, and sleep status for different network densities

-and   compare   it   with   the  two   other   methods.    Figures~\ref{fig7}(a)
+and   compare   it   with   the  two   other   methods.  Figures~\ref{fig7}(a)

 and~\ref{fig7}(b)  illustrate  the  energy  consumption,  considering  different
 network sizes, for $Lifetime_{95}$ and $Lifetime_{50}$.
 
 and~\ref{fig7}(b)  illustrate  the  energy  consumption,  considering  different
 network sizes, for $Lifetime_{95}$ and $Lifetime_{50}$.
 

+ 

 \begin{figure}[h!]
 \begin{figure}[h!]

-  \centering
-  \begin{tabular}{cl}
-    \parbox{9.5cm}{\includegraphics[scale=0.5]{Figures/ch4/R1/EC95.pdf}} & (a) \\
-    \verb+ + \\
-    \parbox{9.5cm}{\includegraphics[scale=0.5]{Figures/ch4/R1/EC50.pdf}} & (b)
-  \end{tabular}
-  \caption{Energy consumption for (a) $Lifetime_{95}$ and 
-    (b) $Lifetime_{50}$}
-  \label{fig7}
-\end{figure} 
+\centering
+ %\begin{multicols}{1}
+\centering
+\includegraphics[scale=0.6]{Figures/ch4/R1/EC95.pdf}\\~ ~ ~ ~ ~(a) \\
+%\vfill
+\includegraphics[scale=0.6]{Figures/ch4/R1/EC50.pdf}\\~ ~ ~ ~ ~(b)
+
+%\end{multicols} 
+\caption{Energy consumption for (a) $Lifetime_{95}$ and (b) $Lifetime_{50}$}
+\label{fig7}
+\end{figure}
+

 
 The  results  show  that  MuDiLCO  is  the  most  competitive  from  the  energy
 consumption point of view.  The  other approaches have a high energy consumption
 
 The  results  show  that  MuDiLCO  is  the  most  competitive  from  the  energy
 consumption point of view.  The  other approaches have a high energy consumption


@@ -476,8 +480,11 @@ MuDiLCO-7, we  should increase the  number of subregions  in order to  have less
 sensors to consider in the integer program.
 
 
 sensors to consider in the integer program.
 
 

-\subsection{Execution time}
-\label{ch4:sec:03:02:05}
+
+ \item {{\bf Execution time}}
+%\subsection{Execution time}
+%\label{ch4:sec:03:02:05}
+

 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 3, section \ref{ch3:sec:04:02}.
 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 3, section \ref{ch3:sec:04:02}.


@@ -486,7 +493,7 @@ seconds (needed to solve optimization problem) for different values of $T$. The
 
 \begin{figure}[ht!]
 \centering
 
 \begin{figure}[ht!]
 \centering

-\includegraphics[scale=0.5]{Figures/ch4/R1/T.pdf}  
+\includegraphics[scale=0.6]{Figures/ch4/R1/T.pdf}  

 \caption{Execution Time (in seconds)}
 \label{fig77}
 \end{figure} 
 \caption{Execution Time (in seconds)}
 \label{fig77}
 \end{figure} 


@@ -502,8 +509,12 @@ 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.
 
 hand  a leader  node may  waste a  considerable amount  of energy  to  solve the
 optimization problem.
 

-\subsection{Network lifetime}
-\label{ch4:sec:03:02:06}
+
+
+\item {{\bf Network lifetime}}
+%\subsection{Network lifetime}
+%\label{ch4:sec:03:02:06}
+

 The next  two figures,  Figures~\ref{fig8}(a) and \ref{fig8}(b),  illustrate the
 network lifetime  for different network sizes,  respectively for $Lifetime_{95}$
 and  $Lifetime_{50}$.  Both  figures show  that the  network  lifetime increases
 The next  two figures,  Figures~\ref{fig8}(a) and \ref{fig8}(b),  illustrate the
 network lifetime  for different network sizes,  respectively for $Lifetime_{95}$
 and  $Lifetime_{50}$.  Both  figures show  that the  network  lifetime increases


@@ -515,21 +526,27 @@ lifetime for a  coverage over 95\% is greater than 38\%  when switching from GAF
 to MuDiLCO-3.  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.
 to MuDiLCO-3.  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 subsection  \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.
 
 energy consumption,  since network lifetime and energy  consumption are directly
 linked.
 

-\begin{figure}[t!]
-  \centering
-  \begin{tabular}{cl}
-    \parbox{9.5cm}{\includegraphics[scale=0.5]{Figures/ch4/R1/LT95.pdf}} & (a) \\
-    \verb+ + \\
-    \parbox{9.5cm}{\includegraphics[scale=0.5]{Figures/ch4/R1/LT50.pdf}} & (b)
-  \end{tabular}
-  \caption{Network lifetime for (a) $Lifetime_{95}$ and 
-    (b) $Lifetime_{50}$}
+
+\begin{figure}[h!]
+\centering
+% \begin{multicols}{0}
+\centering
+\includegraphics[scale=0.6]{Figures/ch4/R1/LT95.pdf}\\~ ~ ~ ~ ~(a) \\
+%\hfill 
+\includegraphics[scale=0.6]{Figures/ch4/R1/LT50.pdf}\\~ ~ ~ ~ ~(b)
+
+%\end{multicols} 
+\caption{Network lifetime for (a) $Lifetime_{95}$ and (b) $Lifetime_{50}$}

   \label{fig8}
   \label{fig8}

-\end{figure} 
+\end{figure}
+
+
+
+\end{enumerate}

 
 
 \section{Conclusion}
 
 
 \section{Conclusion}


@@ -559,3 +576,8 @@ subregion, that can be solved  more easily. Nevertheless, results also show that
 it is not possible to plan the activity of sensors over too many rounds, because
 the resulting optimization problem leads to too high resolution times and thus to
 an excessive energy consumption.
 it is not possible to plan the activity of sensors over too many rounds, because
 the resulting optimization problem leads to too high resolution times and thus to
 an excessive energy consumption.

+
+
+
+
+ 
\ No newline at end of file

diff --git a/Thesis.toc b/Thesis.toc
index a32a98b66f8612478f8c02611d9c28dc9925203b..3d8b5dfc245f47f84b3979b38d0f471d96d213ac 100644 (file)
--- a/Thesis.toc
+++ b/Thesis.toc
@@ -18,7 +18,7 @@
 \contentsline {section}{\numberline {1.5}The Main Challenges in Wireless Sensor Networks}{25}{section.1.5}
 \contentsline {section}{\numberline {1.6}Energy-Efficient Mechanisms in Wireless Sensor Networks}{27}{section.1.6}
 \contentsline {subsection}{\numberline {1.6.1}Energy-Efficient Routing:}{27}{subsection.1.6.1}
 \contentsline {section}{\numberline {1.5}The Main Challenges in Wireless Sensor Networks}{25}{section.1.5}
 \contentsline {section}{\numberline {1.6}Energy-Efficient Mechanisms in Wireless Sensor Networks}{27}{section.1.6}
 \contentsline {subsection}{\numberline {1.6.1}Energy-Efficient Routing:}{27}{subsection.1.6.1}

-\contentsline {subsubsection}{\numberline {1.6.1.1}Routing Metric based on Residual Energy:}{28}{subsubsection.1.6.1.1}
+\contentsline {subsubsection}{\numberline {1.6.1.1}Routing Metric based on Residual Energy:}{27}{subsubsection.1.6.1.1}

 \contentsline {subsubsection}{\numberline {1.6.1.2}Multipath Routing:}{28}{subsubsection.1.6.1.2}
 \contentsline {subsection}{\numberline {1.6.2}Cluster Architectures:}{28}{subsection.1.6.2}
 \contentsline {subsection}{\numberline {1.6.3}Scheduling Schemes:}{29}{subsection.1.6.3}
 \contentsline {subsubsection}{\numberline {1.6.1.2}Multipath Routing:}{28}{subsubsection.1.6.1.2}
 \contentsline {subsection}{\numberline {1.6.2}Cluster Architectures:}{28}{subsection.1.6.2}
 \contentsline {subsection}{\numberline {1.6.3}Scheduling Schemes:}{29}{subsection.1.6.3}


@@ -48,7 +48,7 @@
 \contentsline {part}{II\hspace {1em}Contributions}{51}{part.2}
 \contentsline {chapter}{\numberline {3}Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}{53}{chapter.3}
 \contentsline {section}{\numberline {3.1}Summary}{53}{section.3.1}
 \contentsline {part}{II\hspace {1em}Contributions}{51}{part.2}
 \contentsline {chapter}{\numberline {3}Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}{53}{chapter.3}
 \contentsline {section}{\numberline {3.1}Summary}{53}{section.3.1}

-\contentsline {section}{\numberline {3.2}DESCRIPTION OF THE DILCO PROTOCOL}{53}{section.3.2}
+\contentsline {section}{\numberline {3.2}Description of the DiLCO Protocol}{53}{section.3.2}

 \contentsline {subsection}{\numberline {3.2.1}Assumptions and Network Model}{53}{subsection.3.2.1}
 \contentsline {subsection}{\numberline {3.2.2}Primary Point Coverage Model}{54}{subsection.3.2.2}
 \contentsline {subsection}{\numberline {3.2.3}Main Idea}{55}{subsection.3.2.3}
 \contentsline {subsection}{\numberline {3.2.1}Assumptions and Network Model}{53}{subsection.3.2.1}
 \contentsline {subsection}{\numberline {3.2.2}Primary Point Coverage Model}{54}{subsection.3.2.2}
 \contentsline {subsection}{\numberline {3.2.3}Main Idea}{55}{subsection.3.2.3}


@@ -56,51 +56,25 @@
 \contentsline {subsubsection}{\numberline {3.2.3.2}Leader Election Phase}{57}{subsubsection.3.2.3.2}
 \contentsline {subsubsection}{\numberline {3.2.3.3}Decision phase}{57}{subsubsection.3.2.3.3}
 \contentsline {subsubsection}{\numberline {3.2.3.4}Sensing phase}{57}{subsubsection.3.2.3.4}
 \contentsline {subsubsection}{\numberline {3.2.3.2}Leader Election Phase}{57}{subsubsection.3.2.3.2}
 \contentsline {subsubsection}{\numberline {3.2.3.3}Decision phase}{57}{subsubsection.3.2.3.3}
 \contentsline {subsubsection}{\numberline {3.2.3.4}Sensing phase}{57}{subsubsection.3.2.3.4}

-\contentsline {section}{\numberline {3.3}COVERAGE PROBLEM FORMULATION}{57}{section.3.3}
+\contentsline {section}{\numberline {3.3}Primary Points based Coverage Problem Formulation}{57}{section.3.3}

 \contentsline {section}{\numberline {3.4}Simulation Results and Analysis}{59}{section.3.4}
 \contentsline {subsection}{\numberline {3.4.1}Simulation Framework}{59}{subsection.3.4.1}
 \contentsline {subsection}{\numberline {3.4.2}Modeling Language and Optimization Solver}{60}{subsection.3.4.2}
 \contentsline {subsection}{\numberline {3.4.3}Energy Consumption Model}{60}{subsection.3.4.3}
 \contentsline {subsection}{\numberline {3.4.4}Performance Metrics}{61}{subsection.3.4.4}
 \contentsline {subsection}{\numberline {3.4.5}Performance Analysis for Different Subregions}{62}{subsection.3.4.5}
 \contentsline {section}{\numberline {3.4}Simulation Results and Analysis}{59}{section.3.4}
 \contentsline {subsection}{\numberline {3.4.1}Simulation Framework}{59}{subsection.3.4.1}
 \contentsline {subsection}{\numberline {3.4.2}Modeling Language and Optimization Solver}{60}{subsection.3.4.2}
 \contentsline {subsection}{\numberline {3.4.3}Energy Consumption Model}{60}{subsection.3.4.3}
 \contentsline {subsection}{\numberline {3.4.4}Performance Metrics}{61}{subsection.3.4.4}
 \contentsline {subsection}{\numberline {3.4.5}Performance Analysis for Different Subregions}{62}{subsection.3.4.5}

-\contentsline {subsubsection}{\numberline {3.4.5.1}Coverage Ratio}{62}{subsubsection.3.4.5.1}
-\contentsline {subsubsection}{\numberline {3.4.5.2}Active Sensors Ratio}{62}{subsubsection.3.4.5.2}
-\contentsline {subsubsection}{\numberline {3.4.5.3}The percentage of stopped simulation runs}{63}{subsubsection.3.4.5.3}
-\contentsline {subsubsection}{\numberline {3.4.5.4}The Energy Consumption}{64}{subsubsection.3.4.5.4}
-\contentsline {subsubsection}{\numberline {3.4.5.5}Execution Time}{65}{subsubsection.3.4.5.5}
-\contentsline {subsubsection}{\numberline {3.4.5.6}The Network Lifetime}{66}{subsubsection.3.4.5.6}

 \contentsline {subsection}{\numberline {3.4.6}Performance Analysis for Primary Point Models}{67}{subsection.3.4.6}
 \contentsline {subsection}{\numberline {3.4.6}Performance Analysis for Primary Point Models}{67}{subsection.3.4.6}

-\contentsline {subsubsection}{\numberline {3.4.6.1}Coverage Ratio}{67}{subsubsection.3.4.6.1}
-\contentsline {subsubsection}{\numberline {3.4.6.2}Active Sensors Ratio}{68}{subsubsection.3.4.6.2}
-\contentsline {subsubsection}{\numberline {3.4.6.3}The percentage of stopped simulation runs}{69}{subsubsection.3.4.6.3}
-\contentsline {subsubsection}{\numberline {3.4.6.4}The Energy Consumption}{69}{subsubsection.3.4.6.4}
-\contentsline {subsubsection}{\numberline {3.4.6.5}Execution Time}{70}{subsubsection.3.4.6.5}
-\contentsline {subsubsection}{\numberline {3.4.6.6}The Network Lifetime}{71}{subsubsection.3.4.6.6}

 \contentsline {subsection}{\numberline {3.4.7}Performance Comparison with other Approaches}{71}{subsection.3.4.7}
 \contentsline {subsection}{\numberline {3.4.7}Performance Comparison with other Approaches}{71}{subsection.3.4.7}

-\contentsline {subsubsection}{\numberline {3.4.7.1}Coverage Ratio}{72}{subsubsection.3.4.7.1}
-\contentsline {subsubsection}{\numberline {3.4.7.2}Active Sensors Ratio}{73}{subsubsection.3.4.7.2}
-\contentsline {subsubsection}{\numberline {3.4.7.3}The percentage of stopped simulation runs}{74}{subsubsection.3.4.7.3}
-\contentsline {subsubsection}{\numberline {3.4.7.4}The Energy Consumption}{74}{subsubsection.3.4.7.4}
-\contentsline {subsubsection}{\numberline {3.4.7.5}The Network Lifetime}{76}{subsubsection.3.4.7.5}
-\contentsline {section}{\numberline {3.5}Conclusion}{77}{section.3.5}
+\contentsline {section}{\numberline {3.5}Conclusion}{76}{section.3.5}

 \contentsline {chapter}{\numberline {4}Multiround Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}{79}{chapter.4}
 \contentsline {section}{\numberline {4.1}Summary}{79}{section.4.1}
 \contentsline {chapter}{\numberline {4}Multiround Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}{79}{chapter.4}
 \contentsline {section}{\numberline {4.1}Summary}{79}{section.4.1}

-\contentsline {section}{\numberline {4.2}MuDiLCO protocol description}{79}{section.4.2}
-\contentsline {subsection}{\numberline {4.2.1}Background Idea}{79}{subsection.4.2.1}
-\contentsline {subsection}{\numberline {4.2.2}Information Exchange Phase}{80}{subsection.4.2.2}
-\contentsline {subsection}{\numberline {4.2.3}Leader Election phase}{80}{subsection.4.2.3}
-\contentsline {subsection}{\numberline {4.2.4}Decision phase}{81}{subsection.4.2.4}
-\contentsline {subsection}{\numberline {4.2.5}Sensing phase}{82}{subsection.4.2.5}
-\contentsline {section}{\numberline {4.3}Experimental Study and Analysis}{82}{section.4.3}
-\contentsline {subsection}{\numberline {4.3.1}Simulation Setup}{82}{subsection.4.3.1}
-\contentsline {subsection}{\numberline {4.3.2}Metrics}{83}{subsection.4.3.2}
-\contentsline {subsection}{\numberline {4.3.3}Results analysis and Comparison }{85}{subsection.4.3.3}
-\contentsline {subsection}{\numberline {4.3.4}Coverage ratio}{85}{subsection.4.3.4}
-\contentsline {subsection}{\numberline {4.3.5}Active sensors ratio}{85}{subsection.4.3.5}
-\contentsline {subsection}{\numberline {4.3.6}Stopped simulation runs}{86}{subsection.4.3.6}
-\contentsline {subsection}{\numberline {4.3.7}Energy consumption}{87}{subsection.4.3.7}
-\contentsline {subsection}{\numberline {4.3.8}Execution time}{87}{subsection.4.3.8}
-\contentsline {subsection}{\numberline {4.3.9}Network lifetime}{88}{subsection.4.3.9}
+\contentsline {section}{\numberline {4.2}MuDiLCO Protocol Description}{79}{section.4.2}
+\contentsline {subsection}{\numberline {4.2.1}Background Idea and Algorithm}{80}{subsection.4.2.1}
+\contentsline {subsection}{\numberline {4.2.2}Primary Points based Multiround Coverage Problem Formulation}{81}{subsection.4.2.2}
+\contentsline {section}{\numberline {4.3}Experimental Study and Analysis}{83}{section.4.3}
+\contentsline {subsection}{\numberline {4.3.1}Simulation Setup}{83}{subsection.4.3.1}
+\contentsline {subsection}{\numberline {4.3.2}Metrics}{84}{subsection.4.3.2}
+\contentsline {subsection}{\numberline {4.3.3}Results Analysis and Comparison }{85}{subsection.4.3.3}

 \contentsline {section}{\numberline {4.4}Conclusion}{88}{section.4.4}
 \contentsline {chapter}{\numberline {5}Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks}{91}{chapter.5}
 \contentsline {section}{\numberline {5.1}summary}{91}{section.5.1}
 \contentsline {section}{\numberline {4.4}Conclusion}{88}{section.4.4}
 \contentsline {chapter}{\numberline {5}Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks}{91}{chapter.5}
 \contentsline {section}{\numberline {5.1}summary}{91}{section.5.1}


@@ -111,11 +85,11 @@
 \contentsline {section}{\numberline {5.3}Perimeter-based Coverage Problem Formulation}{96}{section.5.3}
 \contentsline {section}{\numberline {5.4}Performance Evaluation and Analysis}{98}{section.5.4}
 \contentsline {subsection}{\numberline {5.4.1}Simulation Settings}{98}{subsection.5.4.1}
 \contentsline {section}{\numberline {5.3}Perimeter-based Coverage Problem Formulation}{96}{section.5.3}
 \contentsline {section}{\numberline {5.4}Performance Evaluation and Analysis}{98}{section.5.4}
 \contentsline {subsection}{\numberline {5.4.1}Simulation Settings}{98}{subsection.5.4.1}

-\contentsline {subsection}{\numberline {5.4.2}Simulation Results}{98}{subsection.5.4.2}
+\contentsline {subsection}{\numberline {5.4.2}Simulation Results}{99}{subsection.5.4.2}

 \contentsline {subsubsection}{\numberline {5.4.2.1}Coverage Ratio}{99}{subsubsection.5.4.2.1}
 \contentsline {subsubsection}{\numberline {5.4.2.1}Coverage Ratio}{99}{subsubsection.5.4.2.1}

-\contentsline {subsubsection}{\numberline {5.4.2.2}Active Sensors Ratio}{99}{subsubsection.5.4.2.2}
+\contentsline {subsubsection}{\numberline {5.4.2.2}Active Sensors Ratio}{100}{subsubsection.5.4.2.2}

 \contentsline {subsubsection}{\numberline {5.4.2.3}The Energy Consumption}{100}{subsubsection.5.4.2.3}
 \contentsline {subsubsection}{\numberline {5.4.2.4}The Network Lifetime}{100}{subsubsection.5.4.2.4}
 \contentsline {section}{\numberline {5.5}Conclusion}{101}{section.5.5}
 \contentsline {subsubsection}{\numberline {5.4.2.3}The Energy Consumption}{100}{subsubsection.5.4.2.3}
 \contentsline {subsubsection}{\numberline {5.4.2.4}The Network Lifetime}{100}{subsubsection.5.4.2.4}
 \contentsline {section}{\numberline {5.5}Conclusion}{101}{section.5.5}

-\contentsline {part}{III\hspace {1em}Conclusion and Perspectives}{103}{part.3}
-\contentsline {part}{Bibliographie}{114}{chapter*.6}
+\contentsline {part}{III\hspace {1em}Conclusion and Perspectives}{105}{part.3}
+\contentsline {part}{Bibliographie}{115}{chapter*.6}

diff --git a/entete.tex b/entete.tex
index 7add51482177cf13933a70bb4c4d76c5b2823e09..0170bc4c130a80a4bb0f2ffe28665095a2f88ddb 100644 (file)
--- a/entete.tex
+++ b/entete.tex
@@ -145,6 +145,8 @@
 \usepackage[english]{algorithme}
 \usepackage{subfigure}
 \usepackage{listings}
 \usepackage[english]{algorithme}
 \usepackage{subfigure}
 \usepackage{listings}

+
+

 %%--------------------
 %% boxedverbatim
 %\usepackage{moreverb}
 %%--------------------
 %% boxedverbatim
 %\usepackage{moreverb}