X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/JournalMultiPeriods.git/blobdiff_plain/95a4de6bd78956216592f062ffe0337a222fa20a..refs/heads/master:/article.tex

diff --git a/article.tex b/article.tex
index b0a1878..ed70b52 100644
--- a/article.tex
+++ b/article.tex
@@ -149,7 +149,7 @@ in~\cite{idrees2015distributed}.
 %more  interesting  to  divide  the  area  into  several  subregions,  given  the
 %computation complexity.
 
-\textcolor{blue}{ Compared  to our  previous work~\cite{idrees2015distributed},
+\textcolor{black}{ Compared  to our  previous work~\cite{idrees2015distributed},
   in  this paper  we study  the  possibility of  dividing the  sensing phase  into
   multiple rounds.   We make a  multiround optimization,
   while previously it was a single round optimization.  The idea is to
@@ -291,7 +291,7 @@ Indeed, each sensor  maintains its own timer and its  wake-up time is randomized
 \subsection{Assumptions and primary points}
 \label{pp}
 
-\textcolor{blue}{The assumptions and the coverage model are identical to those presented
+\textcolor{black}{The assumptions and the coverage model are identical to those presented
   in~\cite{idrees2015distributed}. We  consider a  scenario in which  sensors are  deployed in  high
   density to  initially ensure a high coverage ratio of the interested area. Each
   sensor  has  a  predefined  sensing  range $R_s$,  an  initial  energy  supply
@@ -356,14 +356,14 @@ inside a subregion is less than or equal to 3.
 
 As can  be seen  in Figure~\ref{fig2},  our protocol  works in  periods fashion,
 where   each   period   is    divided   into   4~phases:   Information~Exchange,
-Leader~Election,  Decision, and  Sensing. \textcolor{blue}{Compared  to 
+Leader~Election,  Decision, and  Sensing. \textcolor{black}{Compared  to 
  the DiLCO protocol described in~\cite{idrees2015distributed},} each sensing phase is itself
 divided into $T$ rounds of equal duration 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 period after Information~Exchange
 and Leader~Election  phases, in order to  produce $T$ cover sets  that will take
 the           mission           of           sensing           for           $T$
-rounds. \textcolor{blue}{Algorithm~\ref{alg:MuDiLCO} is  executed by each sensor
+rounds. \textcolor{black}{Algorithm~\ref{alg:MuDiLCO} is  executed by each sensor
   node~$s_j$ (with enough remaining energy) at the beginning of a period.}
 \begin{figure}[t!]
 \centering \includegraphics[width=125mm]{Modelgeneral.pdf} % 70mm
@@ -400,7 +400,7 @@ rounds. \textcolor{blue}{Algorithm~\ref{alg:MuDiLCO} is  executed by each sensor
 \label{alg:MuDiLCO}
 \end{algorithm}
 
-\textcolor{blue}{As  already   described  in~\cite{idrees2015distributed}},  two
+\textcolor{black}{As  already   described  in~\cite{idrees2015distributed}},  two
 types of packets are used by the proposed protocol:
 \begin{enumerate}[(a)] 
 \item INFO  packet: such a packet  will be sent by  each sensor node to  all the
@@ -482,7 +482,7 @@ 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 sensors by $J$. Only sensors able to be
 alive  during  at  least  one  round   are  involved  in  the  integer  program.
-\textcolor{blue}{Note that the proposed integer  program is an
+\textcolor{black}{Note that the proposed integer  program is an
   extension of the one   formulated  in~\cite{idrees2015distributed},  variables  are  now  indexed  in
   addition with the number of round $t$.}
 
@@ -590,15 +590,6 @@ integer program contains  $A*T$ variables of type  $X_{t,j}$, $P*T$ overcoverage
 variables and $P*T$ undercoverage variables.  The number of constraints is equal
 to $P*T$ (for constraints (\ref{eq16})) $+$ $A$ (for constraints (\ref{eq144})).
 
-\iffalse
-\subsection{Sensing phase}
-
-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 sensor  node~$s_j$  at the  beginning  of a  period,
-explains how the Active-Sleep packet is obtained.
-\fi
 
 \section{Experimental framework}
 \label{exp}
@@ -666,78 +657,19 @@ lifetime. Moreover,  it makes  the MuDiLCO protocol  more robust  against random
 network  disconnection due  to node  failures.  However,  too many  subdivisions
 reduce the advantage  of the optimization.  In fact, there  is a balance between
 the benefit from the optimization and the  execution time needed to solve it. In
-the following  we have  set the number  of subregions  to~16 \textcolor{blue}{as
+the following  we have  set the number  of subregions  to~16 \textcolor{black}{as
   recommended in~\cite{idrees2015distributed}}.
 
 \subsection{Energy model}
-\textcolor{blue}{The      energy     consumption      model     is      detailed
+\textcolor{black}{The      energy     consumption      model     is      detailed
   in~\cite{raghunathan2002energy}.   It  is   based   on   the  model   proposed
   by~\cite{ChinhVu}. We refer to the sensor  node Medusa~II which uses an Atmels
   AVR ATmega103L  microcontroller~\cite{raghunathan2002energy} to  use numerical
   values.}  
 
-\iffalse
-\subsection{Energy model}
-
-We  use an  energy consumption  model  proposed by~\cite{ChinhVu}  and based  on
-\cite{raghunathan2002energy} with slight  modifications.  The energy consumption
-for  sending/receiving the packets  is added,  whereas the  part related  to the
-sensing range is removed because we consider a fixed sensing range.
-
-For our  energy consumption model, we  refer to the sensor  node Medusa~II which
-uses an Atmels  AVR ATmega103L microcontroller~\cite{raghunathan2002energy}. The
-typical  architecture  of a  sensor  is composed  of  four  subsystems: the  MCU
-subsystem which is capable of computation, communication subsystem (radio) which
-is responsible  for transmitting/receiving messages, the  sensing subsystem that
-collects  data, and  the  power supply  which  powers the  complete sensor  node
-\cite{raghunathan2002energy}. Each  of the first three subsystems  can be turned
-on or  off depending on  the current status  of the sensor.   Energy consumption
-(expressed in  milliWatt per second) for  the different status of  the sensor is
-summarized in Table~\ref{table4}.
-
-\begin{table}[ht]
-\caption{The Energy Consumption Model}
-\centering
-\begin{tabular}{|c|c|c|c|c|}
-  \hline
-Sensor status & MCU & Radio & Sensing & Power (mW) \\ [0.5ex]
-\hline
-LISTENING & on & on & on & 20.05 \\
-\hline
-ACTIVE & on & off & on & 9.72 \\
-\hline
-SLEEP & off & off & off & 0.02 \\
-\hline
-COMPUTATION & on & on & on & 26.83 \\
-\hline
-\end{tabular}
-
-\label{table4}
-\end{table}
-
-For the sake of simplicity we ignore the  energy needed to turn on the radio, to
-start up the sensor node, to move from one status to another, etc.
-Thus, when a sensor becomes active (i.e.,  it has already chosen its status), it
-can turn its radio  off to save battery.  MuDiLCO uses two  types of packets for
-communication. The size of the INFO  packet and Active-Sleep packet are 112~bits
-and 24~bits  respectively.  The value  of energy  spent to send  a 1-bit-content
-message is  obtained by using  the equation in  ~\cite{raghunathan2002energy} to
-calculate the  energy cost  for transmitting  messages and  we propose  the same
-value for receiving  the packets. The energy  needed to send or  receive a 1-bit
-packet is equal to 0.2575~mW.
-
-The initial energy of each node is  randomly set in the interval $[500;700]$.  A
-sensor node will  not participate in the  next round if its  remaining energy is
-less than  $E_{R}=36~\mbox{Joules}$, the minimum  energy needed for the  node to
-stay alive  during one round.  This  value has been computed  by multiplying the
-energy consumed in  active state (9.72 mW)  by the time in second  for one round
-(3600 seconds).   According to the interval  of initial energy, a  sensor may be
-alive during at most 20 rounds.
-\fi
-
 \subsection{Metrics}
 
-\textcolor{blue}{To evaluate  our approach  we consider the  performance metrics
+\textcolor{black}{To evaluate  our approach  we consider the  performance metrics
   detailed in~\cite{idrees2015distributed},  which are: Coverage  Ratio, Network
   Lifetime  and  Energy  Consumption.   Compared to  the  previous  definitions,
   formulations of  Coverage Ratio and  Energy Consumption are enriched  with the
@@ -806,21 +738,7 @@ indicate the energy consumed by the whole network in round $t$.
 %nodes  have  been drained  of  their  energy  or each  sensor  network monitoring  an area has become  disconnected.
 \end{enumerate}
 
-\iffalse
-\begin{enumerate}
- \setcounter{5}
-\item {{\bf  Execution Time}:}  a sensor node  has limited energy  resources and
-  computing power, therefore it is important that the proposed algorithm has the
-  shortest possible execution  time. The energy of a sensor  node must be mainly
-  used for the sensing phase, not for the pre-sensing ones.
-  
-\item {{\bf Stopped simulation runs}:} a simulation ends when the sensor network
-  becomes disconnected (some nodes are dead and are not able to send information
-  to the base station). We report the number of simulations that are stopped due
-  to network disconnections and for which round it occurs.
 
-\end{enumerate}
-\fi
 
 \section{Experimental results and analysis}
 \label{analysis}
@@ -834,7 +752,7 @@ points. The  objective of this  comparison is to  select the suitable  number of
 primary points to be used by  a MuDiLCO protocol.  In this comparison, MuDiLCO-1
 protocol is used  with five primary point models, each  model corresponding to a
 number of primary  points, which are called Model-5 (it  uses 5 primary points),
-Model-9, Model-13,  Model-17, and  Model-21. \textcolor{blue}{Note
+Model-9, Model-13,  Model-17, and  Model-21. \textcolor{black}{Note
   that the results
   presented in~\cite{idrees2015distributed}  correspond to Model-13  (13 primary
   points)}.
@@ -852,7 +770,7 @@ because it offers a good coverage ratio for a larger number of periods.
 
 \begin{figure}[t!]
 \centering
- \includegraphics[scale=0.5] {R2/CR.pdf} 
+ \includegraphics[scale=0.5] {R2CR.pdf} 
 \caption{Coverage ratio for 150 deployed nodes}
 \label{Figures/ch4/R2/CR}
 \end{figure} 
@@ -868,9 +786,9 @@ lifetime improvement.
 \begin{figure}[h!]
 \centering
 \centering
-\includegraphics[scale=0.5]{R2/LT95.pdf}\\~ ~ ~ ~ ~(a) \\
+\includegraphics[scale=0.5]{R2LT95.pdf}\\~ ~ ~ ~ ~(a) \\
 
-\includegraphics[scale=0.5]{R2/LT50.pdf}\\~ ~ ~ ~ ~(b)
+\includegraphics[scale=0.5]{R2LT50.pdf}\\~ ~ ~ ~ ~(b)
 
 \caption{Network lifetime for (a) $Lifetime_{95}$ and (b) $Lifetime_{50}$}
   \label{Figures/ch4/R2/LT}
@@ -901,7 +819,7 @@ the best coverage ratio up to round~80, after that MuDiLCO-5 is slightly better.
 
 \begin{figure}[ht!]
 \centering
- \includegraphics[scale=0.5] {F/CR.pdf} 
+ \includegraphics[scale=0.5] {FCR.pdf} 
 \caption{Average coverage ratio for 150 deployed nodes}
 \label{fig3}
 \end{figure} 
@@ -920,7 +838,7 @@ efficient manner.
 
 \begin{figure}[ht!]
 \centering
-\includegraphics[scale=0.5]{F/ASR.pdf}  
+\includegraphics[scale=0.5]{FASR.pdf}  
 \caption{Active sensors ratio for 150 deployed nodes}
 \label{fig4}
 \end{figure} 
@@ -940,7 +858,7 @@ in a subregion is still connected.
 
 \begin{figure}[ht!]
 \centering
-\includegraphics[scale=0.5]{F/SR.pdf} 
+\includegraphics[scale=0.5]{FSR.pdf} 
 \caption{Cumulative percentage of stopped simulation runs for 150 deployed nodes}
 \label{fig6}
 \end{figure} 
@@ -956,9 +874,9 @@ network sizes, for $Lifetime_{95}$ and $Lifetime_{50}$.
 \begin{figure}[h!]
   \centering
   \begin{tabular}{cl}
-    \parbox{9.5cm}{\includegraphics[scale=0.5]{F/EC95.pdf}} & (a) \\
+    \parbox{9.5cm}{\includegraphics[scale=0.5]{FEC95.pdf}} & (a) \\
     \verb+ + \\
-    \parbox{9.5cm}{\includegraphics[scale=0.5]{F/EC50.pdf}} & (b)
+    \parbox{9.5cm}{\includegraphics[scale=0.5]{FEC50.pdf}} & (b)
   \end{tabular}
   \caption{Energy consumption for (a) $Lifetime_{95}$ and 
     (b) $Lifetime_{50}$}
@@ -997,7 +915,7 @@ Figure~\ref{fig77} for different network sizes.
 
 \begin{figure}[ht!]
 \centering
-\includegraphics[scale=0.5]{F/T.pdf}  
+\includegraphics[scale=0.5]{FT.pdf}  
 \caption{Execution Time (in seconds)}
 \label{fig77}
 \end{figure} 
@@ -1045,9 +963,9 @@ metrics, MuDiLCO-5 seems to be the best suited method compared to MuDiLCO-7.
 \begin{figure}[t!]
   \centering
   \begin{tabular}{cl}
-    \parbox{9.5cm}{\includegraphics[scale=0.5125]{F/LT95.pdf}} & (a) \\
+    \parbox{9.5cm}{\includegraphics[scale=0.5125]{FLT95.pdf}} & (a) \\
     \verb+ + \\
-    \parbox{9.5cm}{\includegraphics[scale=0.5125]{F/LT50.pdf}} & (b)
+    \parbox{9.5cm}{\includegraphics[scale=0.5125]{FLT50.pdf}} & (b)
   \end{tabular}
   \caption{Network lifetime for (a) $Lifetime_{95}$ and 
     (b) $Lifetime_{50}$}
