Update by Ali

[ThesisAli.git] / CHAPITRE_06.tex

diff --git a/CHAPITRE_06.tex b/CHAPITRE_06.tex
index 6ac91bfbf10a9c6ec9f206a86571d655c175301f..31e696c7a48eb1326567ace6ad3e0a5746b68608 100755 (executable)
--- a/CHAPITRE_06.tex
+++ b/CHAPITRE_06.tex
@@ -7,12 +7,13 @@
 \chapter{Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks}
 \label{ch6}
 
 \chapter{Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks}
 \label{ch6}
 

+\iffalse

 
 

-\section{summary}
+\section{Summary}

 \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


@@ -26,7 +27,34 @@ sensors' activities.  Extensive simulation experiments have been performed using
 OMNeT++, the  discrete event simulator, to  demonstrate that PeCO  can
 offer longer lifetime coverage for WSNs in comparison with some other protocols.
 
 OMNeT++, the  discrete event simulator, to  demonstrate that PeCO  can
 offer longer lifetime coverage for WSNs in comparison with some other protocols.
 

-\section{THE PeCO PROTOCOL DESCRIPTION}
+
+\fi
+
+
+\section{Introduction}
+\label{ch6:sec:01}
+
+The continuous progress in Micro Electro-Mechanical Systems (MEMS) and
+wireless communication hardware  has given rise to the opportunity to use large
+networks of tiny sensors, called Wireless Sensor Networks (WSN)~\cite{ref1,ref223}, to fulfill monitoring tasks. The features of a WSN made it suitable for a wide
+range of application  in areas such as business,  environment, health, industry,
+military, and so on~\cite{ref4}. These large number of applications have led to different design, management, and operational challenges in WSNs. The challenges become harder with considering into account the main limited capabilities of the sensor nodes such memory, processing, battery life,  bandwidth, and short radio ranges. One important feature that distinguish the WSN from the other types of wireless networks is the provision of the sensing capability for the sensor nodes \cite{ref224}.
+
+The sensor node consumes some energy both in performing the sensing task and in transmitting the sensed data to the sink. Therefore, it is required to activate as less number as possible of sensor nodes that can monitor the whole area of interest so as to reduce the data volume and extend the network lifetime. The sensing coverage is the most important task of the WSNs since sensing unit of the sensor node is responsible for measuring physical,  chemical, or  biological  phenomena in the sensing field. The main challenge of any sensing coverage problem is to discover the redundant sensor node and turn off those nodes in WSN \cite{ref225}. The redundant sensor node is a node whose sensing area is covered by its active neighbors. In previous works, several approaches are used to find out the redundant node such as Voronoi diagram method, sponsored sector, crossing coverage, and perimeter coverage. 
+
+In this chapter,  we propose such an approach called Perimeter-based Coverage Optimization
+protocol (PeCO). The PeCO protocol merges between two energy efficient mechanisms, which are used the main advantages of the centralized and distributed approaches and avoids the most of their disadvantages. An energy-efficient activity scheduling mechanism based new optimization model is performed by each leader in the subregions. This optimization model is based on the perimeter coverage model in order to producing the optimal cover set of active sensors, which are taken the responsibility of sensing during the current period. 
+
+
+The rest of the chapter is  organized as follows. The next section is devoted to the PeCO protocol description and section~\ref{ch6:sec:03} focuses on the
+coverage model formulation which is used  to schedule the activation  of sensor
+nodes based on perimeter coverage model.  Section~\ref{ch6:sec:04}  presents simulations
+results and discusses the comparison  with other approaches. Finally, concluding
+remarks   are  drawn in section~\ref{ch6:sec:05}.
+
+
+
+\section{The PeCO Protocol Description}

 \label{ch6:sec:02}
 
 \noindent  In  this  section,  we  describe in  details  our  Lifetime  Coverage
 \label{ch6:sec:02}
 
 \noindent  In  this  section,  we  describe in  details  our  Lifetime  Coverage


@@ -39,11 +67,11 @@ 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 +163,7 @@ 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!]


@@ -163,27 +185,13 @@ 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

-\includegraphics[width=95.5mm]{Figures/ch6/Model.pdf}  
+\includegraphics[scale=0.80]{Figures/ch6/Model.pdf}  

 \caption{PeCO protocol.}
 \label{fig2}
 \end{figure} 
 \caption{PeCO protocol.}
 \label{fig2}
 \end{figure} 


@@ -253,7 +261,7 @@ 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 +314,13 @@ 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 +343,7 @@ $\alpha^j_i$ 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


@@ -441,7 +448,7 @@ substantial increase of the coverage performance.
 \parskip 0pt    
 \begin{figure}[h!]
 \centering
 \parskip 0pt    
 \begin{figure}[h!]
 \centering

- \includegraphics[scale=0.5] {Figures/ch6/R/CR.eps} 
+ \includegraphics[scale=0.8] {Figures/ch6/R/CR.eps} 

 \caption{Coverage ratio for 200 deployed nodes.}
 \label{fig333}
 \end{figure} 
 \caption{Coverage ratio for 200 deployed nodes.}
 \label{fig333}
 \end{figure} 


@@ -463,7 +470,7 @@ Figure \ref{fig333}.
 
 \begin{figure}[h!]
 \centering
 
 \begin{figure}[h!]
 \centering

-\includegraphics[scale=0.5]{Figures/ch6/R/ASR.eps}  
+\includegraphics[scale=0.8]{Figures/ch6/R/ASR.eps}  

 \caption{Active sensors ratio for 200 deployed nodes.}
 \label{fig444}
 \end{figure} 
 \caption{Active sensors ratio for 200 deployed nodes.}
 \label{fig444}
 \end{figure} 


@@ -487,8 +494,8 @@ while keeping a good coverage level.
 \begin{figure}[h!]
   \centering
   \begin{tabular}{@{}cr@{}}
 \begin{figure}[h!]
   \centering
   \begin{tabular}{@{}cr@{}}

-    \includegraphics[scale=0.475]{Figures/ch6/R/EC95.eps} & \raisebox{2.75cm}{(a)} \\
-    \includegraphics[scale=0.475]{Figures/ch6/R/EC50.eps} & \raisebox{2.75cm}{(b)}
+    \includegraphics[scale=0.8]{Figures/ch6/R/EC95.eps} & \raisebox{4cm}{(a)} \\
+    \includegraphics[scale=0.8]{Figures/ch6/R/EC50.eps} & \raisebox{4cm}{(b)}

   \end{tabular}
   \caption{Energy consumption per period for (a)~$Lifetime_{95}$ and (b)~$Lifetime_{50}$.}
   \label{fig3EC}
   \end{tabular}
   \caption{Energy consumption per period for (a)~$Lifetime_{95}$ and (b)~$Lifetime_{50}$.}
   \label{fig3EC}


@@ -515,8 +522,8 @@ Figure~\ref{fig3LT}(a) because the gain induced  by our protocols increases with
 \begin{figure}[h!]
   \centering
   \begin{tabular}{@{}cr@{}}
 \begin{figure}[h!]
   \centering
   \begin{tabular}{@{}cr@{}}

-    \includegraphics[scale=0.475]{Figures/ch6/R/LT95.eps} & \raisebox{2.75cm}{(a)} \\  
-    \includegraphics[scale=0.475]{Figures/ch6/R/LT50.eps} & \raisebox{2.75cm}{(b)}
+    \includegraphics[scale=0.8]{Figures/ch6/R/LT95.eps} & \raisebox{4cm}{(a)} \\  
+    \includegraphics[scale=0.8]{Figures/ch6/R/LT50.eps} & \raisebox{4cm}{(b)}

   \end{tabular}
   \caption{Network Lifetime for (a)~$Lifetime_{95}$ and (b)~$Lifetime_{50}$.}
   \label{fig3LT}
   \end{tabular}
   \caption{Network Lifetime for (a)~$Lifetime_{95}$ and (b)~$Lifetime_{50}$.}
   \label{fig3LT}


@@ -535,7 +542,7 @@ size. DiLCO is better  for coverage ratios near 100\%, but in  that case PeCO is
 not ineffective for the smallest network sizes.
 
 \begin{figure}[h!]
 not ineffective for the smallest network sizes.
 
 \begin{figure}[h!]

-\centering \includegraphics[scale=0.5]{Figures/ch6/R/LTa.eps}
+\centering \includegraphics[scale=0.8]{Figures/ch6/R/LTa.eps}

 \caption{Network lifetime for different coverage ratios.}
 \label{figLTALL}
 \end{figure} 
 \caption{Network lifetime for different coverage ratios.}
 \label{figLTALL}
 \end{figure} 


@@ -543,7 +550,7 @@ not ineffective for the smallest network sizes.
 
 
 \section{Conclusion}
 
 
 \section{Conclusion}

-\label{ch6:sec:04}
+\label{ch6:sec:05}

 
 In this chapter, we have studied the problem of  Perimeter-based Coverage Optimization in
 WSNs. We have designed  a new protocol, called Perimeter-based  Coverage Optimization, which
 
 In this chapter, we have studied the problem of  Perimeter-based Coverage Optimization in
 WSNs. We have designed  a new protocol, called Perimeter-based  Coverage Optimization, which