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[JournalMultiPeriods.git] / article.tex

diff --git a/article.tex b/article.tex
index eb65d9f7c1527b940c95a255be12f4e8a91ea881..ed70b52271030df1fe6c5ade3585031169ab22c7 100644 (file)
--- 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.
 
 %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
   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}
 
 \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
   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,
 
 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           $T$
  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
   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}
 
 \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
 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.
 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 $t$.}
 
   extension of the one   formulated  in~\cite{idrees2015distributed},  variables  are  now  indexed  in
   addition with the number of round $t$.}
 


@@ -657,11 +657,11 @@ 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
 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}
   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
   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


@@ -669,7 +669,7 @@ the following  we have  set the number  of subregions  to~16 \textcolor{blue}{as
 
 \subsection{Metrics}
 
 
 \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
   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


@@ -752,7 +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),
 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)}.
   that the results
   presented in~\cite{idrees2015distributed}  correspond to Model-13  (13 primary
   points)}.


@@ -915,7 +915,7 @@ Figure~\ref{fig77} for different network sizes.
 
 \begin{figure}[ht!]
 \centering
 
 \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} 
 \caption{Execution Time (in seconds)}
 \label{fig77}
 \end{figure} 


@@ -963,9 +963,9 @@ metrics, MuDiLCO-5 seems to be the best suited method compared to MuDiLCO-7.
 \begin{figure}[t!]
   \centering
   \begin{tabular}{cl}
 \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+ + \\
     \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) $Lifetime_{50}$}
   \end{tabular}
   \caption{Network lifetime for (a) $Lifetime_{95}$ and 
     (b) $Lifetime_{50}$}