X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/JournalMultiPeriods.git/blobdiff_plain/7552f68e916d710b2332b1621ac62abc24e0cafa..153bdb7b8dc4b2d64354aa33053bade4c93e86f4:/article.tex

diff --git a/article.tex b/article.tex
index 1c13bc7..cbb393c 100644
--- a/article.tex
+++ b/article.tex
@@ -208,7 +208,7 @@ network. Note that  centralized algorithms have the advantage  of requiring very
 low  processing  power  from  the  sensor  nodes,  which  usually  have  limited
 processing  capabilities. The  main drawback  of this  kind of  approach is  its
 higher cost in communications, since the  node that will make the decision needs
-information from  all the sensor  nodes.  \textcolor{blue} {Exact  or heuristics
+information from  all the sensor  nodes.  \textcolor{blue} {Exact  or heuristic
   approaches are designed to provide cover sets.
 %(Moreover, centralized approaches usually
 %suffer from the scalability problem, making them less competitive as the network
@@ -627,12 +627,11 @@ $X_{25}=( p_x + R_s * (\frac{1}{2}), p_y + R_s * (\frac{-\sqrt{3}}{2})) $.
 %smaller  areas,  called  subregions,  and  then our MuDiLCO  protocol will be
 %implemented in each subregion in a distributed way.
 
-\textcolor{blue}{The WSN  area of  interest is,  in a  first step,  divided into
-  regular  homogeneous subregions  using  a divide-and-conquer  algorithm. In  a
-  second  step our  protocol  will be  executed  in a  distributed  way in  each
+\textcolor{blue}{The WSN  area of  interest is,  at  first,  divided into
+  regular  homogeneous subregions  using  a divide-and-conquer  algorithm. Then, our  protocol  will be  executed  in a  distributed  way in  each
   subregion  simultaneously  to  schedule  nodes'  activities  for  one  sensing
   period. Sensor nodes are assumed to be deployed almost uniformly and with high
-  density over the region. The regular  subdivision is made such that the number
+  density over the region. The regular  subdivision is made so that the number
   of hops between any pairs of sensors  inside a subregion is less than or equal
   to 3.}
 
@@ -663,10 +662,10 @@ batteries running out of energy), because it works in periods.
  node will not  participate to this phase,  and, on the other hand,  if the node
  failure occurs  after the  decision, the  sensing task of  the network  will be
  temporarily affected:  only during  the period  of sensing  until a  new period
- starts.   \textcolor{blue}{The   duration   of  the   rounds   are   predefined
-   parameters. Round duration  should be long enough to hide  the system control
+ starts.   \textcolor{blue}{The   duration   of  the   rounds  is  a   predefined
+   parameter. Round duration  should be long enough to hide  the system control
    overhead and  short enough to minimize  the negative effects in  case of node
-   failure.}
+   failures.}
 
 %%RC so if there are at least one failure per period, the coverage is bad...
 %%MS if we want to be reliable against many node failures we need to have an
@@ -1174,13 +1173,13 @@ $W_{U}$ & $|P|^2$ \\
   sizes when $T=7$, using the following  respective values (in second): 0.03 for
   150~nodes, 0.06 for 200~nodes, and 0.08 for 250~nodes.
 % Table \ref{tl} shows time limit values.
-  These time limit threshold have been  set empirically. The basic idea consists
+  These time limit thresholds have been  set empirically. The basic idea consists
   in considering  the average execution  time to  solve the integer  programs to
-  optimality, then by  dividing this average time by three  to set the threshold
-  value.  After that,  this threshold value is increased if  necessary such that
+  optimality, then in  dividing this average time by three  to set the threshold
+  value.  After that,  this threshold value is increased if  necessary so that
   the solver is able  to deliver a feasible solution within  the time limit.  In
   fact, selecting the optimal values for the time limits will be investigated in
-  future.}
+  the future.}
 %In Table \ref{tl},  "NO" indicates  that  the  problem has  been  solved to  optimality without time limit.}
 
 %\begin{table}[ht]