X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/blobdiff_plain/e56d6e1ea749e2e2e5a14854e6b82c16939dd51f..fe3d4f5c7954d683dbfbc1f26de2d724ff9889d7:/bare_conf.tex?ds=sidebyside

diff --git a/bare_conf.tex b/bare_conf.tex
index a6cc277..fd7001e 100755
--- a/bare_conf.tex
+++ b/bare_conf.tex
@@ -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
