X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/blobdiff_plain/1e354db9744c3493287fe10e62f12f5851bcf3ed..fe3d4f5c7954d683dbfbc1f26de2d724ff9889d7:/bare_conf.tex

diff --git a/bare_conf.tex b/bare_conf.tex
index 373f8b5..fd7001e 100755
--- a/bare_conf.tex
+++ b/bare_conf.tex
@@ -40,9 +40,9 @@
 % author names and affiliations
 % use a multiple column layout for up to three different
 % affiliations
-\author{\IEEEauthorblockN{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Raphael Couturier }
-\IEEEauthorblockA{FEMTO-ST Institute, UMR CNRS, University of Franche-Comte, Belfort, France \\
-Email:$\lbrace$ali.idness, karine.deschinkel, michel.salomon,raphael.couturier$\rbrace$@femto-st.fr}
+\author{\IEEEauthorblockN{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier }
+\IEEEauthorblockA{FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comte, Belfort, France \\
+Email: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}
 %\email{\{ali.idness, karine.deschinkel, michel.salomon, raphael.couturier\}@univ-fcomte.fr}
 %\and
 %\IEEEauthorblockN{Homer Simpson}
@@ -113,7 +113,7 @@ planned for  each subregion.  Our scheduling  scheme considers rounds,
 where a  round starts with  a discovery phase to  exchange information
 between  sensors of  the subregion,  in  order to  choose in  suitable
 manner a sensor  node to carry out a  coverage strategy. This coverage
-strategy involves the resolution  of an integer program which provides
+strategy involves the solving  of an integer program which provides
 the activation  of the  sensors for the  sensing phase of  the current
 round.
 
@@ -128,7 +128,7 @@ OMNET++  \cite{varga}. They  fully demonstrate  the usefulness  of the
 proposed  approach.   Finally, we  give  concluding  remarks and  some
 suggestions for future works in Section~\ref{sec:conclusion}.
 
-\section{\uppercase{Related work}}
+\section{\uppercase{Related works}}
 \label{rw}
 \noindent 
 This section  is dedicated to  the various approaches proposed  in the
@@ -183,7 +183,7 @@ is alive  until all  nodes have  been drained of  their energy  or the
 sensor network becomes disconnected, and we measure the coverage ratio
 during the WSN lifetime.  Network connectivity is important because an
 active sensor node without  connectivity towards a base station cannot
-transmit information on an event in the area that it monitor.
+transmit information on an event in the area that it monitors.
 
 {\bf Activity scheduling}
 
@@ -196,13 +196,13 @@ distributed, and localized algorithms, have been proposed for activity
 scheduling.  In  the distributed algorithms, each node  in the network
 autonomously makes decisions on whether  to turn on or turn off itself
 only using  local neighbor information. In  centralized algorithms, a
-central controller  (a node or  base station) informs every  sensor of
+central controller  (a node or  base station) informs every  sensors of
 the time intervals to be activated.
 
 {\bf Distributed approaches}
 
 Some      distributed     algorithms      have      been     developed
-in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02}.     Distributed
+in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02} to perform the schelduling.     Distributed
 algorithms typically operate in  rounds for predetermined duration. At
 the beginning  of each round,  a sensor exchange information  with its
 neighbors and makes a decision to  either remain turned on or to go to
@@ -222,7 +222,7 @@ of       the       area        is       no       longer       covered.
 \cite{Prasad:2007:DAL:1782174.1782218}  defines a model  for capturing
 the dependencies  between different cover sets  and proposes localized
 heuristic  based on this  dependency.  The  algorithm consists  of two
-phases, an initial setup phase during which each sensor calculates and
+phases, an initial setup phase during which each sensor computes and
 prioritize the  covers and  a sensing phase  during which  each sensor
 first decides  its on/off status, and  then remains on or  off for the
 rest  of the  duration.  Authors  in \cite{chin2007}  propose  a novel
@@ -262,10 +262,10 @@ these set covers successively.
 
 First algorithms  proposed in the  literature consider that  the cover
 sets  are  disjoint: a  sensor  node appears  in  exactly  one of  the
-generated  cover   sets.   For  instance   Slijepcevic  and  Potkonjak
+generated  cover   sets.   For  instance,   Slijepcevic  and  Potkonjak
 \cite{Slijepcevic01powerefficient}   propose    an   algorithm   which
 allocates sensor nodes in mutually independent sets to monitor an area
-divided into several fields. Their algorithm constructs a cover set by
+divided into several fields. Their algorithm builds a cover set by
 including in  priority the sensor  nodes which cover  critical fields,
 that  is to  say fields  that are  covered by  the smallest  number of
 sensors. The time complexity of  their heuristic is $O(n^2)$ where $n$
@@ -293,7 +293,7 @@ design a heuristic to compute  the final number of covers. The results
 show  a slight  performance  improvement  in terms  of  the number  of
 produced  DSC in comparison  to~\cite{Slijepcevic01powerefficient}, but
 it incurs  higher execution  time due to  the complexity of  the mixed
-integer      programming     resolution.       %Cardei      and     Du
+integer      programming     solving.       %Cardei      and     Du
 \cite{Cardei:2005:IWS:1160086.1160098} propose a method to efficiently
 compute the maximum  number of disjoint set covers  such that each set
 can  monitor all  targets. They  first  transform the  problem into  a
@@ -340,8 +340,8 @@ scheduling strategy. We  give a brief answer to  these three questions
 to describe our  approach before going into details  in the subsequent
 sections.
 \begin{itemize}
-\item {\bf  How must be  planned the phases for  information exchange,
-  decision  and sensing over  time?}  Our  algorithm divides  the time
+\item {\bf  How must  the phases for  information exchange,
+  decision  and sensing be planned over  time?}  Our  algorithm divides  the time
   line  into  a  number  of  rounds. Each  round  contains  4  phases:
   Information Exchange, Leader Election, Decision, and Sensing.
 
@@ -430,7 +430,7 @@ active mode.
 This  step includes choosing  the Wireless  Sensor Node  Leader (WSNL)
 which  will  be  responsible  of executing  coverage  algorithm.  Each
 subregion  in  the   area  of  interest  will  select   its  own  WSNL
-independently  for each  round.  All the  sensor  nodes cooperates  to
+independently  for each  round.  All the  sensor  nodes cooperate  to
 select WSNL.  The nodes in the  same subregion will  select the leader
 based on  the received  information from all  other nodes in  the same
 subregion.  The selection criteria  in order  of priority  are: larger
@@ -450,7 +450,7 @@ Active  sensors  in the  round  will  execute  their sensing  task  to
 preserve maximal  coverage in the  region of interest. We  will assume
 that the cost  of keeping a node awake (or sleep)  for sensing task is
 the same  for all wireless sensor  nodes in the  network.  Each sensor
-will  receive an  Active-Sleep packet  from WSNL  telling him  to stay
+will  receive an  Active-Sleep packet  from WSNL  informing it  to stay
 awake or  go sleep  for a time  equal to  the period of  sensing until
 starting a new round.
 
@@ -615,7 +615,7 @@ X_{j} \in \{0,1\}, &\forall j \in J
 \right.
 \end{equation}
 \begin{itemize}
-\item  $X_{j}$ :  indicates  whether  or not  sensor  $j$ is  actively
+\item  $X_{j}$ :  indicates  whether  or not  the sensor  $j$ is  actively
   sensing in the round (1 if yes and 0 if not);
 \item $\Theta_{p}$  : {\it overcoverage}, the number  of sensors minus
   one that are covering point $p$;
@@ -661,7 +661,7 @@ the maximum number of points are covered during each round.
 \section{\uppercase{Simulation Results}}
 \label{exp}
 
-In this section, we conducted a series of simulations, to evaluate the
+In this section, we conducted a series of simulations to evaluate the
 efficiency  and relevance of  our approach,  using the  discrete event
 simulator  OMNeT++  \cite{varga}. We  performed  simulations for  five
 different densities varying from 50 to 250~nodes. Experimental results
@@ -683,7 +683,7 @@ range 24-60~joules, and each sensor node will consume 0.2 watts during
 the sensing period which will have  a duration of 60 seconds. Thus, an
 active  node will  consume  12~joules during  sensing  phase, while  a
 sleeping  node will  use  0.002  joules.  Each  sensor  node will  not
-participate in the next round if it's remaining energy is less than 12
+participate in the next round if its remaining energy is less than 12
 joules.  In  all  experiments  the  parameters  are  set  as  follows:
 $R_s=5m$, $w_{\Theta}=1$, and $w_{U}=|P^2|$.
 
@@ -711,7 +711,7 @@ number of rounds on the  average coverage ratio for 150 deployed nodes
 for the  three approaches.  It can be  seen that the  three approaches
 give  similar  coverage  ratios  during  the first  rounds.  From  the
 9th~round the  coverage ratio  decreases continuously with  the simple
-heuristic, while the other two strategies provide superior coverage to
+heuristic, while the two other strategies provide superior coverage to
 $90\%$ for five more rounds.  Coverage ratio decreases when the number
 of rounds increases  due to dead nodes. Although  some nodes are dead,
 thanks to  strategy~1 or~2,  other nodes are  preserved to  ensure the
@@ -791,14 +791,14 @@ expected, the Strategy with One Leader is usually slightly better than
 the second  strategy, because the  global optimization permit  to turn
 off more  sensors. Indeed,  when there are  two subregions  more nodes
 remain awake  near the border shared  by them. Note that  again as the
-number of  rounds increase  the two leader  strategy becomes  the most
+number of  rounds increases  the two leader  strategy becomes  the most
 performing, since its takes longer  to have the two subregion networks
 simultaneously disconnected.
 
 \subsection{The Network Lifetime}
 
 We have defined the network lifetime  as the time until all nodes have
-been drained of their energy  or each sensor network monitoring a area
+been drained of their energy  or each sensor network monitoring an area
 becomes disconnected.  In figure~\ref{fig6}, the  network lifetime for
 different network sizes and for the three approaches is illustrated.
 
@@ -815,10 +815,10 @@ As  highlighted by figure~\ref{fig6},  the network  lifetime obviously
 increases when the  size of the network increase,  with our approaches
 that lead  to the larger  lifetime improvement.  By choosing  for each
 round the  well suited nodes  to cover the  region of interest  and by
-leaving sleep  the other ones  to be used  later in next  rounds, both
+letting  the other ones sleep in order to be used  later in next  rounds, both
 proposed strategies efficiently prolong the lifetime. Comparison shows
-that the larger the sensor  number, the more our strategies outperform
-the heuristic.   Strategy~2, which uses  two leaders, is the  best one
+that the larger the sensor  number is, the more our strategies outperform
+the simple heuristic.   Strategy~2, which uses  two leaders, is the  best one
 because it  is robust  to network disconnection  in one  subregion. It
 also  means   that  distributing  the  algorithm  in   each  node  and
 subdividing the sensing field  into many subregions, which are managed
