Update by Ali

diff --git a/CHAPITRE_06.tex b/CHAPITRE_06.tex
index d7479d2127758df71a13e664505142865924f4bc..047a372bcb8be2ad111e9fc5af347aa183706281 100755 (executable)
--- a/CHAPITRE_06.tex
+++ b/CHAPITRE_06.tex
@@ -12,7 +12,7 @@
 \label{ch6:sec:01}
 
 The most important problem in a Wireless Sensor Network (WSN) is to optimize the
 \label{ch6:sec:01}
 
 The most important problem in a Wireless Sensor Network (WSN) is to optimize the

-use of its limited energy provision, so that it can fulfill its monitoring task
+use of its limited energy provision so that it can fulfill its monitoring task

 as long as  possible. Among  known  available approaches  that can  be used  to
 improve  power  management,  lifetime coverage  optimization  provides  activity
 scheduling which ensures sensing coverage while minimizing the energy cost. In
 as long as  possible. Among  known  available approaches  that can  be used  to
 improve  power  management,  lifetime coverage  optimization  provides  activity
 scheduling which ensures sensing coverage while minimizing the energy cost. In


@@ -39,11 +39,11 @@ executed by each node.
 
 \subsection{Assumptions and Models}
 \label{ch6:sec:02:01}
 
 \subsection{Assumptions and Models}
 \label{ch6:sec:02:01}

-PeCO protocol uses the same assumptions and network model that presented in chapter 4, section \ref{ch4:sec:02:01}.
+The PeCO protocol uses the same assumptions and network model that presented in chapter 4, section \ref{ch4:sec:02:01}.

 
 The PeCO protocol  uses the  same perimeter-coverage  model as  Huang and
 Tseng in~\cite{ref133}. It  can be expressed as follows:  a sensor is
 
 The PeCO protocol  uses the  same perimeter-coverage  model as  Huang and
 Tseng in~\cite{ref133}. It  can be expressed as follows:  a sensor is

-said to be perimeter  covered if all the points on its  perimeter are covered by
+said to be a perimeter covered if all the points on its  perimeter are covered by

 at least  one sensor  other than  itself.  They  proved that  a network  area is
 $k$-covered if and only if each sensor in the network is $k$-perimeter-covered (perimeter covered by at least $k$ sensors).
   
 at least  one sensor  other than  itself.  They  proved that  a network  area is
 $k$-covered if and only if each sensor in the network is $k$-perimeter-covered (perimeter covered by at least $k$ sensors).
   


@@ -135,13 +135,7 @@ above is thus given by the sixth line of the table.
 \end{table}
 
 
 \end{table}
 
 

-In the PeCO  protocol, the scheduling of the sensor  nodes' activities is formulated  with an
-integer program  based on  coverage intervals. The  formulation of  the coverage
-optimization problem is  detailed in~section~\ref{ch6:sec:03}.  Note that  when a sensor
-node  has a  part of  its sensing  range outside  the WSN  sensing field,  as in
-Figure~\ref{ex4pcm}, the maximum coverage level for  this arc is set to $\infty$
-and  the  corresponding  interval  will  not   be  taken  into  account  by  the
-optimization algorithm.
+In the PeCO  protocol, the scheduling of the sensor  nodes' activities is formulated  as an integer program  based on  coverage intervals. The  formulation of  the coverage optimization problem is  detailed in~section~\ref{ch6:sec:03}.  Note that  when a sensor node  has a  part of  its sensing  range outside  the WSN  sensing field,  as in Figure~\ref{ex4pcm}, the maximum coverage level for  this arc is set to $\infty$ and  the  corresponding  interval  will  not   be  taken  into  account  by  the optimization algorithm.

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


@@ -163,23 +157,9 @@ homogeneous subregions  using a divide-and-conquer  algorithm. In a  second step
 our  protocol  will  be  executed  in a distributed way in each subregion
 simultaneously to schedule nodes' activities for one sensing period.
 
 our  protocol  will  be  executed  in a distributed way in each subregion
 simultaneously to schedule nodes' activities for one sensing period.
 

-As  shown in  Figure~\ref{fig2}, node  activity  scheduling is  produced by  our
-protocol in a periodic manner. Each period is divided into 4 stages: Information
-(INFO)  Exchange,  Leader Election,  Decision  (the  result of  an  optimization
-problem),  and  Sensing.   For  each  period there  is  exactly  one  set  cover
-responsible for  the sensing task.  Protocols  based on a periodic  scheme, like
-PeCO, are more  robust against an unexpected  node failure. On the  one hand, if
-a node failure is discovered before  taking the decision, the corresponding sensor
-node will  not be considered  by the optimization  algorithm. On  the other
-hand, if the sensor failure happens after  the decision, the sensing task of the
-network will be temporarily affected: only  during the period of sensing until a
-new period starts, since a new set cover will take charge of the sensing task in
-the next period. The energy consumption and some other constraints can easily be
-taken  into  account since  the  sensors  can  update  and then  exchange  their
-information (including their  residual energy) at the beginning  of each period.
-However, the pre-sensing  phases (INFO Exchange, Leader  Election, and Decision)
-are energy consuming, even for nodes that will not join the set cover to monitor
-the area.
+As  shown in  Figure~\ref{fig2}, node  activity  scheduling is  produced by  our protocol in a periodic manner. Each period is divided into 4 stages: Information (INFO)  Exchange,  Leader Election,  Decision  (the  result of  an  optimization problem),  and  Sensing.   For  each  period, there  is  exactly  one  set  cover responsible for  the sensing task.  Protocols  based on a periodic  scheme, like PeCO, are more  robust against an unexpected  node failure. On the  one hand, if a node failure is discovered before  taking the decision, the corresponding sensor
+node will  not be considered  by the optimization  algorithm. On  the other hand, if the sensor failure happens after  the decision, the sensing task of the network will be temporarily affected: only  during the period of sensing until a new period starts, since a new set cover will take charge of the sensing task in the next period. The energy consumption and some other constraints can easily be taken  into  account since  the  sensors  can  update  and then  exchange  their information (including their  residual energy) at the beginning  of each period. However, the pre-sensing  phases (INFO Exchange, Leader  Election, and Decision)
+are energy consuming, even for nodes that will not join the set cover to monitor the area.

 
 \begin{figure}[t!]
 \centering
 
 \begin{figure}[t!]
 \centering


@@ -253,7 +233,7 @@ embedded  GPS or a  location discovery  algorithm. After  that, all  the sensors
 collect position coordinates,  remaining energy, sensor node ID,  and the number
 of their  one-hop live  neighbors during the  information exchange.  The sensors
 inside a same region cooperate to elect a leader. The selection criteria for the
 collect position coordinates,  remaining energy, sensor node ID,  and the number
 of their  one-hop live  neighbors during the  information exchange.  The sensors
 inside a same region cooperate to elect a leader. The selection criteria for the

-leader, in order of priority,  are: larger numbers of neighbors, larger remaining
+leader, in order of priority,  are larger numbers of neighbors, larger remaining

 energy, and  then in case  of equality, larger  index.  Once chosen,  the leader
 collects information to formulate and  solve the integer program which allows to
 construct the set of active sensors in the sensing stage.
 energy, and  then in case  of equality, larger  index.  Once chosen,  the leader
 collects information to formulate and  solve the integer program which allows to
 construct the set of active sensors in the sensing stage.


@@ -306,14 +286,13 @@ sensor $j$  is given by  $\sum_{k \in A} a^j_{ik}  X_k$.  To extend  the network
 lifetime,  the objective  is to  activate a  minimal number  of sensors  in each
 period to  ensure the  desired coverage  level. As the  number of  alive sensors
 decreases, it becomes impossible to reach  the desired level of coverage for all
 lifetime,  the objective  is to  activate a  minimal number  of sensors  in each
 period to  ensure the  desired coverage  level. As the  number of  alive sensors
 decreases, it becomes impossible to reach  the desired level of coverage for all

-coverage intervals. Therefore we use variables  $M^j_i$ and $V^j_i$ as a measure
+coverage intervals. Therefore, we use variables  $M^j_i$ and $V^j_i$ as a measure

 of the  deviation between  the desired  number of active  sensors in  a coverage
 interval and  the effective  number. And  we try  to minimize  these deviations,
 first to  force the  activation of  a minimal  number of  sensors to  ensure the
 desired coverage level, and if the desired level cannot be completely satisfied,
 to reach a coverage level as close as possible to the desired one.
 
 of the  deviation between  the desired  number of active  sensors in  a coverage
 interval and  the effective  number. And  we try  to minimize  these deviations,
 first to  force the  activation of  a minimal  number of  sensors to  ensure the
 desired coverage level, and if the desired level cannot be completely satisfied,
 to reach a coverage level as close as possible to the desired one.
 

-

 Our coverage optimization problem can then be mathematically expressed as follows: 
 %Objective:
 \begin{equation} %\label{eq:ip2r}
 Our coverage optimization problem can then be mathematically expressed as follows: 
 %Objective:
 \begin{equation} %\label{eq:ip2r}


@@ -336,7 +315,7 @@ $\alpha^j_i$ and $\beta^j_i$  are nonnegative weights selected  according to the
 relative importance of satisfying the associated level of coverage. For example,
 weights associated with  coverage intervals of a specified part  of a region may
 be  given by a  relatively larger  magnitude than  weights associated  with another
 relative importance of satisfying the associated level of coverage. For example,
 weights associated with  coverage intervals of a specified part  of a region may
 be  given by a  relatively larger  magnitude than  weights associated  with another

-region. This  kind of integer program  is inspired from the  model developed for
+region. This kind of an integer program is inspired from the model developed for

 brachytherapy treatment planning  for optimizing dose  distribution
 \cite{0031-9155-44-1-012}. The integer  program must be solved by  the leader in
 each subregion at the beginning of  each sensing phase, whenever the environment
 brachytherapy treatment planning  for optimizing dose  distribution
 \cite{0031-9155-44-1-012}. The integer  program must be solved by  the leader in
 each subregion at the beginning of  each sensing phase, whenever the environment

diff --git a/INTRODUCTION.tex b/INTRODUCTION.tex
index 995fefaca16d702103ecd1eff535e5a95ea9417b..a5c141fe660e0ed461451516c6e167bc266a4086 100755 (executable)
--- a/INTRODUCTION.tex
+++ b/INTRODUCTION.tex
@@ -4,19 +4,19 @@
 
 %%-------------------------------------------------------------------------------------------------------%%
  \section{General Introduction}
 
 %%-------------------------------------------------------------------------------------------------------%%
  \section{General Introduction}

-The enormous development of wireless networks and the emergence of fourth and fifth-generation technology are leading to the provision of various services to customers around the world that make the Internet a more widely used everywhere. This kind of wireless networks may not be appropriate to be used in some sensitive areas that need to deploy a large number of wireless devices, which are capable of decide and communicate with each other in a distributed way so as to collect the sensed measurements directly from the physical dangerous environment such as volcanoes, nuclear reactors, forest fires, or military battles. Therefore, another type of wireless networks has been emerged to cope with these challenges, which is called Wireless Sensor Network (WSN). 
+The enormous development of wireless networks, and the emergence of fourth and fifth-generation technology are leading to the provision of various services to customers around the world that make the Internet a more widely used everywhere. This kind of wireless networks may not be appropriate to be used in some sensitive areas that need to deploy a large number of wireless devices, which are capable of decide and communicate with each other in a distributed way so as to collect the sensed measurements directly from the physical dangerous environment such as volcanoes, nuclear reactors, forest fires, or military battles. Therefore, another type of wireless networks has been emerged to cope with these challenges, which is called Wireless Sensor Network (WSN). 

 
 

-WSN is a special case of the ad hoc wireless networks and it consists of a large number of wireless cheap devices are called sensors, which are able to perform the communication, sensing, processing and storage with a limited capability. A WSN can be used by the human to monitor the physical phenomena remotely and without outside intervention. Inside a WSN, the wireless sensor nodes are self-contained units equipped  with a radio transceiver, a microcontroller, a small memory, and a power source, usually a battery. These sensor nodes are cooperating together autonomously to perform the assigned tasks without the intervention or control from outside. The distributed self-organization and self-configuration capabilities of wireless sensor nodes make the distributed WSNs to enable myriad applications for monitoring, sensing, and controlling the physical world.
+The WSN is a special case of the ad hoc wireless networks and it consists of a large number of wireless cheap devices are called sensors, which are able to perform the communication, sensing, processing and storage with a limited capability. A WSN can be used by the human to monitor the physical phenomena remotely and without outside intervention. Inside a WSN, the wireless sensor nodes are self-contained units equipped with a radio transceiver, a microcontroller, a small memory, and a power source, usually a battery. These sensor nodes are cooperating together autonomously to perform the assigned tasks without the intervention or control from outside. The distributed self-organization and self-configuration capabilities of wireless sensor nodes make the distributed WSNs enable myriad applications for monitoring, sensing, and controlling the physical world.

 
 

-The rapid advancement in Micro Electro-Mechanical Systems (MEMS), wireless communication hardware, digital electronics, and  system-on-chip has given rise to use large networks of tiny sensors are becoming cheaper and more and more commercially available. The sensor nodes have several limitations, such as: power source, processing capability, bandwidth, uncertainty of sensed data, and the vulnerability of sensor nodes to physical world. These limitations have been tackled by many researchers during the last years, and consequently, many solutions have been proposed that take these constraints into account on the sensors. Sensor nodes are battery-powered without means, of recharging or replacing, usually due to environmental (hostile or unpractical environments) or cost reasons. Since the batteries are the most important limited resource inside the sensor nodes, 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 the sensing phase to prolong the network lifetime.
+The rapid advancement in Micro Electro-Mechanical Systems (MEMS), wireless communication hardware, digital electronics, and  system-on-chip has given rise to use large networks of tiny sensors are becoming cheaper and more and more commercially available. The sensor nodes have several limitations, such as the power source, processing capability, bandwidth, uncertainty of sensed data, and the vulnerability of sensor nodes to the physical world. These limitations have been tackled by many researchers during the last years, and consequently, many solutions have been proposed that take these constraints into account on the sensors. Sensor nodes are battery-powered without means, of recharging or replacing, usually due to environmental (hostile or unpractical environments) or cost reasons. Since the batteries are the most important limited resource inside the sensor nodes, 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 the sensing phase to prolong the network lifetime.

 
 

-Since the network lifetime depends on sensor lifetime, the power depletion represents the most significant part during designing the WSN protocols because of the limited capacity of the sensor batteries.  The major goal is to extend the network lifetime, taking into consideration the energy source limitations. Several energy-efficient approaches have been suggested so as to minimize the energy consumption and extend the network lifetime during monitoring a certain area by WSN. For example, one of the ways is to turn off the redundant sensors and put them in sleep mode to maintain the energy, whilst the active sensors perform the sensing coverage task during their life. Specifically, the energy-efficient protocols, which are proposed in this dissertation focuses on the area coverage problem in WSNs. The major goal of the area coverage problem is to ensure a maximum area coverage ratio for the entire sensing field of the WSN and for a long time as possible. The area coverage problem is closely related to the performance of systems in many applications, such as, monitoring the battlefield, target detection, tracking, personal protection, animal habit monitoring, and homeland security.
+Since the network lifetime depends on sensor lifetime, the power depletion represents the most significant part during designing of the WSN protocols because of the limited capacity of the sensor batteries.  The major goal is to extend the network lifetime, taking into consideration the energy source limitations. Several energy-efficient approaches have been suggested so as to minimize the energy consumption and extend the network lifetime during monitoring a certain area by WSN. For example, one of the ways is to turn off the redundant sensors and put them in sleep mode to maintain the energy, whilst the active sensors perform the sensing coverage task during their life. Specifically, the energy-efficient protocols, which are proposed in this dissertation focuses on the area coverage problem in WSNs. The major goal of the area coverage problem is to ensure a maximum area coverage ratio for the entire sensing field of the WSN and for a long time as possible. The area coverage problem is closely related to the performance of systems in many applications, such as monitoring the battlefield, target detection, tracking, personal protection, animal habit monitoring, and homeland security.

 
 
 
 \section{Motivation of the Dissertation}
 One of the fundamental challenges in Wireless Sensor Networks (WSNs) is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area (or region) of interest. Since sensor nodes have limited battery life; since it is impossible to replace batteries, especially in remote and hostile
 
 
 
 \section{Motivation of the Dissertation}
 One of the fundamental challenges in Wireless Sensor Networks (WSNs) is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area (or region) of interest. Since sensor nodes have limited battery life; since it is impossible to replace batteries, especially in remote and hostile

-environments, it is desirable that a WSN should be deployed with high density because spatial redundancy can then be exploited to increase the lifetime of the network. In such a high density network, if all sensor nodes were to be activated at the same time, the lifetime would be reduced. To extend the lifetime of the network, the main idea is to take advantage of the overlapping sensing regions of some sensor nodes to save energy by turning off some of them during the sensing phase. Obviously, the deactivation of nodes is only relevant if the coverage of the monitored area is not affected.
+environments, it is desirable that a WSN should be deployed with high density because spatial redundancy can then be exploited to increase the lifetime of the network. In such a high-density network, if all sensor nodes were to be activated at the same time, the lifetime would be reduced. To extend the lifetime of the network, the main idea is to take advantage of the overlapping sensing regions of some sensor nodes to save energy by turning off some of them during the sensing phase. Obviously, the deactivation of nodes is only relevant if the coverage of the monitored area is not affected.

 
 Although many works have introduced in this area, there is still need for a protocol which can schedule sensor nodes in an efficient way with minimum number of sensors and a less communication overhead so as to maintain the coverage and extend the network lifetime as long as possible. The main question is how to reduce the redundancy while maintaining a good coverage with minimum energy consumption?
  
 
 Although many works have introduced in this area, there is still need for a protocol which can schedule sensor nodes in an efficient way with minimum number of sensors and a less communication overhead so as to maintain the coverage and extend the network lifetime as long as possible. The main question is how to reduce the redundancy while maintaining a good coverage with minimum energy consumption?
  


@@ -35,11 +35,11 @@ election and sensor activity scheduling based optimization, where the challenges
 The coverage problem in WSNs is becoming more and more important for many applications ranging from military applications such as battlefield surveillance to the civilian applications such as health-care surveillance and habitant monitoring. The main contributions in this dissertation concentrate on design a distributed optimization protocols so as to extend the lifetime of the WSNs.  We summarize the main contributions of our research as follows:
  
 \begin{enumerate} [i)]
 The coverage problem in WSNs is becoming more and more important for many applications ranging from military applications such as battlefield surveillance to the civilian applications such as health-care surveillance and habitant monitoring. The main contributions in this dissertation concentrate on design a distributed optimization protocols so as to extend the lifetime of the WSNs.  We summarize the main contributions of our research as follows:
  
 \begin{enumerate} [i)]

- \item We design a protocol that focuses on the area coverage problem with the objective of maximizing the network lifetime. Our proposition, the Distributed Lifetime Coverage Optimization (DILCO) protocol, maintains the coverage and improves the lifetime in WSNs. DILCO protocol presented in chapter 3 is an extension of our approach introduced in \cite{ref159}. In \cite{ref159}, the protocol is deployed over only two subregions. In DILCO protocol, the area of interest is first divided into subregions using a divide-and-conquer algorithm and an activity scheduling for sensor nodes is then planned by the elected leader in each subregion. In fact, the nodes in a subregion can be seen as a cluster where each node sends sensing data to the cluster head or the sink node. Furthermore, the activities in a subregion/cluster can continue even if another cluster stops due to too many node failures. DiLCO protocol considers periods, where a period starts with a discovery phase to exchange information between sensors of the same subregion, in order to choose in a suitable manner a sensor node (the leader) to carry out the coverage strategy. In each subregion the activation of the sensors for the sensing phase of the current period is obtained by solving an integer program. The resulting activation vector is broadcast by a leader to every node of its subregion.
+ \item We design a protocol that focuses on the area coverage problem with the objective of maximizing the network lifetime. Our proposition, the Distributed Lifetime Coverage Optimization (DILCO) protocol, maintains the coverage and improves the lifetime in WSNs. DILCO protocol presented in chapter 3 is an extension of our approach introduced in \cite{ref159}. In \cite{ref159}, the protocol is deployed over only two subregions. In DILCO protocol, the area of interest is first divided into subregions using a divide-and-conquer algorithm and an activity scheduling for sensor nodes is then planned by the elected leader in each subregion. In fact, the nodes in a subregion can be seen as a cluster where each node sends sensing data to the cluster head or the sink node. Furthermore, the activities in a subregion/cluster can continue even if another cluster stops due to too many node failures. DiLCO protocol considers periods, where a period starts with a discovery phase to exchange information between sensors of the same subregion, in order to choose in a suitable manner a sensor node (the leader) to carry out the coverage strategy. In each subregion, the activation of the sensors for the sensing phase of the current period is obtained by solving an integer program. The resulting activation vector is broadcast by a leader to every node of its subregion.

 
 

-\item We extend our work that explained in chapter 3 and present a generalized framework that can be applied to provide the cover sets of all rounds in each period. The MuDiLCO protocol (for Multiround Distributed Lifetime Coverage Optimization protocol) presented in chapter 4 is an extension of the approach introduced in chapter 3. In DiLCO protocol, the activity scheduling based optimization is planned for each subregion periodically only for one round. Whilst, we study the possibility of dividing the sensing phase into multiple rounds. In fact, we make a multiround optimization, while it was a single round optimization in our previous contribution.
+\item We extend our work that explained in chapter 3 and present a generalized framework that can be applied to provide the cover sets of all rounds in each period. The MuDiLCO protocol (for Multiround Distributed Lifetime Coverage Optimization protocol) presented in chapter 4 is an extension of the approach introduced in chapter 3. In DiLCO protocol, the activity scheduling based optimization is planned for each subregion periodically only for one round. Whilst, we study the possibility of dividing the sensing phase into multiple rounds. In fact, we make a multiround optimization while it was a single round optimization in our previous contribution.

 
 

-\item We devise a framework to schedule nodes to be activated alternatively such that the network lifetime is prolonged  while ensuring that a certain level of   coverage is preserved. A key idea in our framework is  to exploit spatial an temporal subdivision. On the one hand  the area of interest if divided into several smaller subregions and on the other hand the time line is divided into periods of equal length. In each subregion the sensor nodes will cooperatively choose a  leader which will schedule  nodes' activities, and this grouping of sensors is similar to typical cluster architecture. We  propose a new mathematical  optimization model. Instead of  trying to cover a set of specified points/targets as in most of the methods proposed in the literature, we formulate an integer program based on perimeter coverage of each sensor. The model involves integer variables to capture the deviations between the actual level of coverage and the required  level. So that an optimal scheduling  will be  obtained by  minimizing a  weighted sum  of these deviations. This contribution is demonstrated in Chapter 5.
+\item We devise a framework to schedule nodes to be activated alternatively such that the network lifetime is prolonged  while ensuring that a certain level of   coverage is preserved. A key idea in our framework is  to exploit the spatial-temporal subdivision. On the one hand, the area of interest is divided into several smaller subregions and, on the other hand, the timeline is divided into periods of equal length. In each subregion, the sensor nodes will cooperatively choose a  leader which will schedule  nodes' activities, and this grouping of sensors is similar to typical cluster architecture. We  propose a new mathematical  optimization model. Instead of  trying to cover a set of specified points/targets as in most of the methods proposed in the literature, we formulate an integer program based on perimeter coverage of each sensor. The model involves integer variables to capture the deviations between the actual level of coverage and the required  level. So that an optimal scheduling  will be  obtained by  minimizing a  weighted sum  of these deviations. This contribution is demonstrated in Chapter 5.

 
 \item We add an improved model of energy consumption to assess the efficiency of our protocols as well as we conducted extensive simulation experiments, using the discrete event simulator OMNeT++, to demonstrate the  efficiency of our protocols. We compared our proposed distributed optimization protocols to two approaches found in the literature: DESK~\cite{DESK} and GAF~\cite{GAF}, simulation results based on multiple criteria (energy consumption, coverage ratio, network lifetime and so on) show that the proposed protocols can prolong efficiently the network lifetime and improve the coverage performance.
 
 
 \item We add an improved model of energy consumption to assess the efficiency of our protocols as well as we conducted extensive simulation experiments, using the discrete event simulator OMNeT++, to demonstrate the  efficiency of our protocols. We compared our proposed distributed optimization protocols to two approaches found in the literature: DESK~\cite{DESK} and GAF~\cite{GAF}, simulation results based on multiple criteria (energy consumption, coverage ratio, network lifetime and so on) show that the proposed protocols can prolong efficiently the network lifetime and improve the coverage performance.