X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/LiCO.git/blobdiff_plain/21a06a72c2eade0f4f4f89586ab50c1ce9569025..a6b75d539ec83b4958277b957744cb0eb7349aea:/LiCO_Journal.tex?ds=sidebyside

diff --git a/LiCO_Journal.tex b/LiCO_Journal.tex
index 39df581..051f111 100644
--- a/LiCO_Journal.tex
+++ b/LiCO_Journal.tex
@@ -56,12 +56,12 @@
 
 \begin{abstract}
 The most important problem in a Wireless Sensor Network (WSN) is to optimize the
-use of its limited energy provision, so  that it can fulfill its monitoring task
-as  long as  possible. Among  known  available approaches  that can  be used  to
+use of its limited energy provision, so that it can fulfill its monitoring task
+as long as  possible. Among  known  available approaches  that can  be used  to
 improve  power  management,  lifetime coverage  optimization  provides  activity
 scheduling which ensures sensing coverage while minimizing the energy cost. In
 this paper,  we propose such an approach called Perimeter-based Coverage Optimization
-protocol (PeCO).   It is a  hybrid of centralized and distributed methods: the
+protocol (PeCO). It is a  hybrid of centralized and distributed methods: the
 region of interest is first subdivided into subregions and our protocol is then
 distributed among sensor nodes in each  subregion.
 % A  sensor node  which runs  LiCO  protocol repeats  periodically four  stages:
@@ -69,7 +69,7 @@ distributed among sensor nodes in each  subregion.
 %More precisely, the scheduling of nodes' activities (sleep/wake up duty cycles)
 %is achieved  in each subregion by  a leader selected  after cooperation between
 %nodes within the same subregion.
-The  novelty of our approach lies essentially in the formulation of a new
+The novelty of our approach lies essentially in the formulation of a new
 mathematical optimization  model based on the  perimeter coverage level  to schedule
 sensors' activities.  Extensive simulation experiments have been performed using
 OMNeT++, the  discrete event simulator, to  demonstrate that PeCO  can
@@ -78,7 +78,7 @@ offer longer lifetime coverage for WSNs in comparison with some other protocols.
 
 % Note that keywords are not normally used for peerreview papers.
 \begin{IEEEkeywords}
-Wireless Sensor Networks, Area Coverage, Network lifetime, Optimization, Scheduling.
+Wireless Sensor Networks, Area Coverage, Network Lifetime, Optimization, Scheduling.
 \end{IEEEkeywords}
 
 \IEEEpeerreviewmaketitle
@@ -286,7 +286,7 @@ used~\cite{castano2013column,rossi2012exact,deschinkel2012column}. {\it  In the
 \section{ The PeCO Protocol Description}
 \label{sec:The PeCO Protocol Description}
 
-\noindent  In  this  section,  we  describe in  details  our  Lifetime  Coverage
+\noindent  In  this  section,  we  describe in  details  our Perimeter-based  Coverage
 Optimization protocol.  First we present the  assumptions we made and the models
 we considered (in particular the perimeter coverage one), second we describe the
 background idea of our protocol, and third  we give the outline of the algorithm
@@ -574,11 +574,11 @@ construct the set of active sensors in the sensing stage.
 
 % The leader has the responsibility of applying the integer program algorithm (see section~\ref{cp}), which provides a set of sensors planned to be active in the sensing stage.  As leader, it will send an Active-Sleep packet to each sensor in the same subregion to inform it if it has to be active or not. On the contrary, if the sensor is not the leader, it will wait for the Active-Sleep packet to know its state for the sensing stage.
 
-\section{Lifetime Coverage problem formulation}
+\section{Perimeter-based Coverage Problem Formulation}
 \label{cp}
 
 \noindent In this  section, the coverage model is  mathematically formulated. We
-start  with a  description of  the notations  that will  be used  throughout the
+start  with a  description of the notations that will  be used  throughout the
 section.
 
 First, we have the following sets:
@@ -670,7 +670,7 @@ relative importance of satisfying the associated level of coverage. For example,
 weights associated with  coverage intervals of a specified part  of a region may
 be  given by a  relatively larger  magnitude than  weights associated  with another
 region. This  kind of integer program  is inspired from the  model developed for
-brachytherapy    treatment   planning    for   optimizing    dose   distribution
+brachytherapy treatment planning  for optimizing dose  distribution
 \cite{0031-9155-44-1-012}. The integer  program must be solved by  the leader in
 each subregion at the beginning of  each sensing phase, whenever the environment
 has  changed (new  leader,  death of  some  sensors). Note  that  the number  of
@@ -963,8 +963,8 @@ not ineffective for the smallest network sizes.
 \section{Conclusion and Future Works}
 \label{sec:Conclusion and Future Works}
 
-In this paper  we have studied the problem of  lifetime coverage optimization in
-WSNs. We have designed  a new protocol, called Lifetime  Coverage Optimization, which
+In this paper  we have studied the problem of  Perimeter-based Coverage Optimization in
+WSNs. We have designed  a new protocol, called Perimeter-based  Coverage Optimization, which
 schedules nodes'  activities (wake up  and sleep  stages) with the  objective of
 maintaining a  good coverage ratio  while maximizing the network  lifetime. This
 protocol is  applied in a distributed  way in regular subregions  obtained after
@@ -998,7 +998,7 @@ sensor-testbed to evaluate it in real world applications.
 
 \section*{Acknowledgments}
 
-\noindent  As a  Ph.D.   student, Ali  Kadhum IDREES  would  like to  gratefully
+\noindent  As a  Ph.D.   student, Ali Kadhum IDREES  would  like to  gratefully
 acknowledge the  University of Babylon -  IRAQ for financial support  and Campus
 France for the  received support. This work is also partially funded by the Labex ACTION program (contract ANR-11-LABX-01-01). 
 
