X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/blobdiff_plain/8122e0cee2d1c24eb33b2646bb579111dd9abcbb..a686d52f555483fdac37117da5d318bfe646506d:/bare_conf.tex?ds=sidebyside

diff --git a/bare_conf.tex b/bare_conf.tex
index a38a894..bbe85c3 100644
--- a/bare_conf.tex
+++ b/bare_conf.tex
@@ -67,7 +67,7 @@ subregions using  a divide-and-conquer method and  then the scheduling
 of sensor node  activity is planned for each  subregion.  The proposed
 scheduling  considers rounds  during which  a small  number  of nodes,
 remaining active  for sensing, is  selected to ensure  coverage.  Each
-round consists  of four phases:  (i)~Information Exchange, (ii)~Leader
+round consists  in four phases:  (i)~Information Exchange, (ii)~Leader
 Election, (iii)~Decision,  and (iv)~Sensing.  The  decision process is
 carried  out  by a  leader  node,  which  solves an  integer  program.
 Simulation  results show that  the proposed  approach can  prolong the
@@ -120,7 +120,7 @@ the network  lifetime, while  achieving acceptable quality  of service
 for  applications. Indeed,  sensors  nodes have  limited resources  in
 terms of memory, energy and computational power.
 
-Since sensor nodes have limited battery life and without being able to
+Since sensor nodes have limited battery life and since it is impossible to
 replace batteries,  especially in remote and  hostile environments, it
 is desirable that  a WSN should be deployed  with high density because
 spatial redundancy can  then be exploited to increase  the lifetime of
@@ -179,7 +179,7 @@ from  both temporal and  spatial correlations  between data  sensed by
 different sensors.
 
 The  works  presented  in  \cite{Bang,  Zhixin, Zhang}  focus  on  the
-definition  of  a  coverage-aware,  distributed  energy-efficient  and
+definition  of  coverage-aware,  distributed  energy-efficient  and
 distributed clustering  methods respectively.  They aim  to extend the
 network  lifetime  while ensuring  the  coverage.   S.   Misra et  al.
 \cite{Misra05} proposed a localized algorithm which conserves energy and
@@ -197,8 +197,7 @@ to build a scheduling strategy.\\
 %\begin{itemize}
 %\item 
 {\indent \bf  How must the  phases for information  exchange, decision
-  and sensing be  planned over time?}  Our algorithm  divides the time
-line into rounds.  Each round contains 4 phases: Information Exchange,
+  and sensing be  planned over time?}  Our algorithm  divides the timeline into rounds.  Each round contains 4 phases: Information Exchange,
 Leader Election, Decision, and Sensing.
 
 %\item 
@@ -688,7 +687,7 @@ nodes.   Overall,  to  be  able to  deal  with  very  large  networks,  a
 distributed method is clearly required.
 
 \begin{table}[ht]
-\caption{EXEC. TIME(S) VS. NUMBER OF SENSORS}
+\caption{EXECUTION TIME(S) VS. NUMBER OF SENSORS}
 % title of Table
 \centering
 
@@ -777,8 +776,8 @@ propose  allows   to  reduce  the   difficulty  of  a   single  global
 optimization problem by partitioning  it in many smaller problems, one
 per subregion,  that can  be solved more  easily.  In future  work, we
 plan to  study a  coverage protocol which  computes all  active sensor
-schedules  in one  time,  using optimization  methods  such as  swarms
-optimization or evolutionary algorithms.
+schedules in only one step for many rounds,  using optimization  methods
+such as  swarms optimization or evolutionary algorithms.
 % use section* for acknowledgement
 %\section*{Acknowledgment}