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[Sensornets15.git] / Example.tex

diff --git a/Example.tex b/Example.tex
index cef7ad19651e2c29f103418593f019f6d227f03a..3b522661430b3a17e87f6845d1e45241ea42fa74 100644 (file)
--- a/Example.tex
+++ b/Example.tex
@@ -91,7 +91,7 @@ means  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

-the sensing phase to prolong the network lifetime. \textcolor{blue}{A WSN can use various types of sensors such as \cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar. These sensors are capable of observing  different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. Consequently, a wide range of WSN applications such as~\cite{ref22}: health-care, environment, agriculture, public safety, military, transportation systems, and industry applications.}
+the sensing phase to prolong the network lifetime. \textcolor{blue}{A WSN can use various types of sensors such as \cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar. These sensors are capable of observing  different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. Consequently, there is a wide range of WSN applications such as~\cite{ref22}: health-care, environment, agriculture, public safety, military, transportation systems, and industry applications.}

 
 In this  paper we design  a protocol that  focuses on the area  coverage problem
 with  the objective  of maximizing  the network  lifetime. Our  proposition, the
 
 In this  paper we design  a protocol that  focuses on the area  coverage problem
 with  the objective  of maximizing  the network  lifetime. Our  proposition, the


@@ -116,8 +116,7 @@ framework of the  DiLCO approach and the coverage problem  formulation.  In this
 paper  we   made  more  realistic   simulations  by  taking  into   account  the
 characteristics of  a Medusa II sensor  ~\cite{raghunathan2002energy} to measure
 the energy consumption and the computation  time.  We have implemented two other
 paper  we   made  more  realistic   simulations  by  taking  into   account  the
 characteristics of  a Medusa II sensor  ~\cite{raghunathan2002energy} to measure
 the energy consumption and the computation  time.  We have implemented two other

-existing approaches (a distributed one,  DESK ~\cite{ChinhVu}, and a centralized
-one called GAF  ~\cite{xu2001geography}) in order to  compare their performances
+existing \textcolor{blue}{and distributed approaches}(DESK ~\cite{ChinhVu}, and GAF  ~\cite{xu2001geography}) in order to  compare their performances

 with our approach.  We also focus on performance analysis based on the number of
 subregions. 
 % MODIF - END
 with our approach.  We also focus on performance analysis based on the number of
 subregions. 
 % MODIF - END


@@ -244,7 +243,8 @@ less accurate according to the number of primary points.
 \label{main_idea}
 \noindent We start  by applying a divide-and-conquer algorithm  to partition the
 area of interest  into smaller areas called subregions and  then our protocol is
 \label{main_idea}
 \noindent We start  by applying a divide-and-conquer algorithm  to partition the
 area of interest  into smaller areas called subregions and  then our protocol is

-executed   simultaneously  in   each   subregion.
+executed   simultaneously  in   each   subregion. \textcolor{blue}{Sensor nodes  are assumed to
+be deployed  almost uniformly over the  region and the subdivision of the area of interest is regular.}

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


@@ -257,7 +257,9 @@ As  shown  in Figure~\ref{fig2},  the  proposed  DiLCO  protocol is  a  periodic
 protocol where  each period is  decomposed into 4~phases:  Information Exchange,
 Leader Election,  Decision, and Sensing. For  each period there  will be exactly
 one  cover  set  in charge  of  the  sensing  task.   A periodic  scheduling  is
 protocol where  each period is  decomposed into 4~phases:  Information Exchange,
 Leader Election,  Decision, and Sensing. For  each period there  will be exactly
 one  cover  set  in charge  of  the  sensing  task.   A periodic  scheduling  is

-interesting  because it  enhances the  robustness  of the  network against  node failures. \textcolor{blue}{Many WSN applications have communication requirements that are periodic and known previously such as collecting temperature statistics at regular intervals. This periodic nature can be used to provide a regular schedule to sensor nodes and thus avoid a sensor failure. If the period time increases, the reliability and energy consumption are decreased and vice versa}. First,  a node  that has not  enough energy  to complete a  period, or
+interesting  because it  enhances the  robustness  of the  network against  node failures.
+% \textcolor{blue}{Many WSN applications have communication requirements that are periodic and known previously such as collecting temperature statistics at regular intervals. This periodic nature can be used to provide a regular schedule to sensor nodes and thus avoid a sensor failure. If the period time increases, the reliability and energy consumption are decreased and vice versa}. 
+First,  a node  that has not  enough energy  to complete a  period, or

 which fails before  the decision is taken, will be  excluded from the scheduling
 process. Second,  if a node  fails later, whereas  it was supposed to  sense the
 region of  interest, it will only affect  the quality of the  coverage until the
 which fails before  the decision is taken, will be  excluded from the scheduling
 process. Second,  if a node  fails later, whereas  it was supposed to  sense the
 region of  interest, it will only affect  the quality of the  coverage until the


@@ -291,19 +293,21 @@ and each sensor node will have five possible status in the network:
 An outline of the  protocol implementation is given by Algorithm~\ref{alg:DiLCO}
 which describes  the execution of  a period  by a node  (denoted by $s_j$  for a
 sensor  node indexed by  $j$). At  the beginning  a node  checks whether  it has
 An outline of the  protocol implementation is given by Algorithm~\ref{alg:DiLCO}
 which describes  the execution of  a period  by a node  (denoted by $s_j$  for a
 sensor  node indexed by  $j$). At  the beginning  a node  checks whether  it has

-enough energy to stay active during the next sensing phase. If yes, it exchanges
+enough energy \textcolor{blue}{(its energy should be greater than a fixed treshold $E_{th}$)} to stay active during the next sensing phase. If yes, it exchanges

 information  with  all the  other  nodes belonging  to  the  same subregion:  it
 collects from each node its position coordinates, remaining energy ($RE_j$), ID,
 information  with  all the  other  nodes belonging  to  the  same subregion:  it
 collects from each node its position coordinates, remaining energy ($RE_j$), ID,

-and  the number  of  one-hop neighbors  still  alive. Once  the  first phase  is
+and  the number  of  one-hop neighbors  still  alive. \textcolor{blue}{INFO packet contains two parts: header and data payload. The sensor ID is included in the header, where the header size is 8 bits. The data part includes position coordinates (64 bits), remaining energy (32 bits), and the number of one-hop live neighbors (8 bits). Therefore the size of the INFO packet is 112 bits.} Once  the  first phase  is

 completed, the nodes  of a subregion choose a leader to  take the decision based
 on  the  following  criteria   with  decreasing  importance:  larger  number  of
 neighbors, larger remaining energy, and  then in case of equality, larger index.
 completed, the nodes  of a subregion choose a leader to  take the decision based
 on  the  following  criteria   with  decreasing  importance:  larger  number  of
 neighbors, larger remaining energy, and  then in case of equality, larger index.

-After that,  if the sensor node is  leader, it will execute  the integer program
-algorithm (see Section~\ref{cp})  which provides a set of  sensors planned to be
-active in the next sensing phase. As leader, it will send an Active-Sleep packet
+After that,  if the sensor node is  leader, it will solve  an integer program 
+(see Section~\ref{cp}). \textcolor{blue}{This integer program contains boolean variables $X_j$  where ($X_j=1$) means that sensor $j$ will be active in the next sensing phase. Only sensors with enough remaining energy are involved in the integer program ($J$ is the set of all sensors involved). As the leader consumes energy (computation energy, denoted by $E^{comp}$) to solve the optimization problem, it will be included in the integer program only if it has enough energy to achieve the computation and to stay alive during the next sensing phase, that is to say if $RE_j > E^{comp}+E_{th}$. Once the optimization problem is solved, each leader will send an Active-Sleep packet

 to each sensor  in the same subregion to  indicate it if it has to  be active or
 to each sensor  in the same subregion to  indicate it if it has to  be active or

-not.  Alternately, if  the  sensor  is not  the  leader, it  will  wait for  the
-Active-Sleep packet to know its state for the coming sensing phase.
+not. Otherwise, if  the  sensor  is not  the  leader, it  will  wait for  the
+Active-Sleep packet to know its state for the coming sensing phase.}
+%which provides a set of  sensors planned to be
+%active in the next sensing phase.
+

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


@@ -352,7 +356,7 @@ We formulate the coverage optimization problem with an integer program.
 The objective function consists in minimizing the undercoverage and the overcoverage of the area as suggested in \cite{pedraza2006}. 
 The area coverage problem is expressed as the coverage of a fraction of points called primary points. 
 Details on the choice and the number of primary points can be found in \cite{idrees2014coverage}. The set of primary points is denoted by $P$
 The objective function consists in minimizing the undercoverage and the overcoverage of the area as suggested in \cite{pedraza2006}. 
 The area coverage problem is expressed as the coverage of a fraction of points called primary points. 
 Details on the choice and the number of primary points can be found in \cite{idrees2014coverage}. The set of primary points is denoted by $P$

-and the set of sensors by $J$. As we consider a boolean disk coverage model, we use the boolean indicator $\alpha_{jp}$ which is equal to 1 if the primary point $p$ is in the sensing range of the sensor $j$. The binary variable $X_j$ represents the activation or not of the sensor $j$. So we can express the number of  active sensors  that cover  the primary  point $p$ by $\sum_{j \in J} \alpha_{jp} * X_{j}$. We deduce the overcoverage denoted by $\Theta_p$ of the primary point $p$ :
+and the set of alive sensors by $J$. As we consider a boolean disk coverage model, we use the boolean indicator $\alpha_{jp}$ which is equal to 1 if the primary point $p$ is in the sensing range of the sensor $j$. The binary variable $X_j$ represents the activation or not of the sensor $j$. So we can express the number of  active sensors  that cover  the primary  point $p$ by $\sum_{j \in J} \alpha_{jp} * X_{j}$. We deduce the overcoverage denoted by $\Theta_p$ of the primary point $p$ :

 \begin{equation}
  \Theta_{p} = \left \{ 
 \begin{array}{l l}
 \begin{equation}
  \Theta_{p} = \left \{ 
 \begin{array}{l l}


@@ -396,9 +400,14 @@ X_{j} \in \{0,1\}, &\forall j \in J
 \end{array}
 \right.
 \end{equation}
 \end{array}
 \right.
 \end{equation}

-The objective function is a weighted sum of overcoverage and undercoverage. The goal is to limit the overcoverage in order to activate a minimal number of sensors while simultaneously preventing undercoverage. Both  weights $w_\theta$  and $w_U$ must  be carefully  chosen in
-order to  guarantee that the  maximum number of  points are covered  during each
-period.
+The objective function is a weighted sum of overcoverage and undercoverage. The goal is to limit the overcoverage in order to activate a minimal number of sensors while simultaneously preventing undercoverage.  \textcolor{blue}{ By
+    choosing  $w_{U}$ much  larger than $w_{\theta}$,  the coverage  of a
+    maximum of  primary points  is ensured.  Then for the  same number  of covered
+    primary points,  the solution  with a  minimal number  of active  sensors is
+    preferred. }
+%Both  weights $w_\theta$  and $w_U$ must  be carefully  chosen in
+%order to  guarantee that the  maximum number of  points are covered  during each
+%period.

 % MODIF - END
 
 
 % MODIF - END