X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/LiCO.git/blobdiff_plain/be59291130ebe0f562cd03e4e3ec724dd0792e5f..cb151d3544ce08c9c270b4dcd4201e2520900e5e:/LiCO_Journal.tex?ds=inline

diff --git a/LiCO_Journal.tex b/LiCO_Journal.tex
index fca3d13..a714974 100644
--- a/LiCO_Journal.tex
+++ b/LiCO_Journal.tex
@@ -60,7 +60,7 @@ 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  a such approach  called Lifetime  Coverage Optimization
+this paper,  we propose such an approach  called Lifetime  Coverage Optimization
 protocol (LiCO).   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
@@ -321,7 +321,7 @@ complete coverage of a convex area implies connectivity among active nodes.
 Tseng in~\cite{huang2005coverage}. It  can be expressed as follows:  a sensor is
 said to be perimeter  covered if all the points on its  perimeter are covered by
 at least  one sensor  other than  itself.  They  proved that  a network  area is
-$k$-covered if and only if each sensor in the network is $k$-perimeter-covered.
+$k$-covered if and only if each sensor in the network is $k$-perimeter-covered (perimeter covered by at least $k$ sensors).
 %According to this model, we named the intersections among the sensor nodes in the sensing field as intersection points. Instead of working with the coverage area, we consider for each sensor a set of intersection points which are determined by using perimeter-coverage model. 
 Figure~\ref{pcm2sensors}(a)  shows  the coverage  of  sensor  node~$0$. On  this
 figure, we can  see that sensor~$0$ has  nine neighbors and we  have reported on
@@ -365,7 +365,8 @@ positions. The intersection points are  then visited one after another, starting
 from  first  intersection point  after  point~zero,  and  the maximum  level  of
 coverage is determined  for each interval defined by two  successive points. The
 maximum  level of  coverage is  equal to  the number  of overlapping  arcs.  For
-example, between~$5L$  and~$6L$ the maximum  level of  coverage is equal  to $3$
+example, 
+between~$5L$  and~$6L$ the maximum  level of  coverage is equal  to $3$
 (the value is highlighted in yellow  at the bottom of figure~\ref{expcm}), which
 means that at most 2~neighbors can cover  the perimeter in addition to node $0$.
 Table~\ref{my-label} summarizes for each coverage  interval the maximum level of
@@ -377,7 +378,7 @@ above is thus given by the sixth line of the table.
 
 \begin{figure*}[ht!]
 \centering
-\includegraphics[width=137.5mm]{expcm.pdf}  
+\includegraphics[width=137.5mm]{expcm2.jpg}  
 \caption{Maximum coverage levels for perimeter of sensor node $0$.}
 \label{expcm}
 \end{figure*} 
@@ -829,7 +830,7 @@ it corresponds to the configuration producing  the better results for DiLCO. The
 protocols are distinguished  from one another by the formulation  of the integer
 program providing the set of sensors which  have to be activated in each sensing
 phase. DiLCO protocol tries to satisfy the coverage of a set of primary points,
-whereas LiCO protocol objectif is to reach a desired level of coverage for each
+whereas LiCO protocol objective is to reach a desired level of coverage for each
 sensor perimeter. In our experimentations, we chose a level of coverage equal to
 one ($l=1$).