X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/LiCO.git/blobdiff_plain/20aa226716ebbb3990da53146f469c6b56bc617f..3b6e0c9402cf8ca8e7daec7f4520685de215e5df:/PeCO-EO/articleeo.tex?ds=sidebyside

diff --git a/PeCO-EO/articleeo.tex b/PeCO-EO/articleeo.tex
index ba033ea..82007ff 100644
--- a/PeCO-EO/articleeo.tex
+++ b/PeCO-EO/articleeo.tex
@@ -15,13 +15,12 @@
 \title{{\itshape Perimeter-based Coverage Optimization to Improve Lifetime \\
     in Wireless Sensor Networks}}
 
-\author{Ali Kadhum Idrees$^{a, b}$, Karine Deschinkel$^{a}$$^{\ast}$\thanks{$^\ast$Corresponding author. Email: karine.deschinkel@univ-fcomte.fr}, Michel Salomon$^{a}$ and Rapha\"el Couturier $^{a}$
-$^{a}${\em{FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comt\'e,
-          Belfort, France}}
-$^{b}${\em{Department of Computer Science, University of Babylon, Babylon, Iraq}} }         
-          
-        
-
+\author{Ali Kadhum Idrees$^{a,b}$, Karine Deschinkel$^{a}$$^{\ast}$\thanks{$^\ast$Corresponding author. Email: karine.deschinkel@univ-fcomte.fr}, Michel Salomon$^{a}$ and Rapha\"el Couturier $^{a}$
+  $^{a}${\em{FEMTO-ST Institute, UMR 6174 CNRS, \\
+  University Bourgogne Franche-Comt\'e (UBFC), Belfort, France}} \\ 
+  $^{b}${\em{Department of Computer Science, University of Babylon, Babylon, Iraq}}
+}         
+         
 \maketitle
 
 \begin{abstract}
@@ -309,7 +308,7 @@ above is thus given by the sixth line of the table.
 
 \begin{figure*}[t!]
 \centering
-\includegraphics[width=0.95\linewidth]{figure2.eps}  
+\includegraphics[width=0.9\linewidth]{figure2.eps}  
 \caption{Maximum coverage levels for perimeter of sensor node $0$.}
 \label{figure2}
 \end{figure*} 
@@ -392,7 +391,7 @@ of the application.
 
 \begin{figure}[t!]
 \centering
-\includegraphics[width=85mm]{figure4.eps}  
+\includegraphics[width=80mm]{figure4.eps}  
 \caption{PeCO protocol.}
 \label{figure4}
 \end{figure} 
@@ -668,7 +667,7 @@ coverage task. This value corresponds to the energy needed by the sensing phase,
 obtained by multiplying  the energy consumed in the active  state (9.72 mW) with
 the time in seconds for one period (3600 seconds), and adding the energy for the
 pre-sensing phases.  According  to the interval of initial energy,  a sensor may
-be active during at most 20 periods.
+be active during at most 20 periods. Here information exchange is executed every hour but the length of the sensing period could be reduced and adapted dynamically. On the one hand a small sensing period would allow to be more reliable but would have higher communication costs. On the other hand the choice of a long duration may cause problems in case of nodes failure during the sensing period.
 
 The values  of $\alpha^j_i$ and  $\beta^j_i$ have been  chosen to ensure  a good
 network coverage  and a longer  WSN lifetime.  Higher  priority is given  to the
@@ -775,7 +774,13 @@ based on the energy model proposed in \citep{ChinhVu}.
 The modeling  language for Mathematical Programming  (AMPL)~\citep{AMPL} is used
 to generate  the integer program  instance in a  standard format, which  is then
 read and  solved by  the optimization  solver GLPK  (GNU linear  Programming Kit
-available in the public domain) \citep{glpk} through a Branch-and-Bound method.
+available in the public domain) \citep{glpk} through a Branch-and-Bound method. 
+Obviously executing GLPK in practice on a sensor node is usually untractable due to
+the huge memory use. Fortunately, to solve the optimization problem we could use 
+commercial solvers like CPLEX which are less memory consuming and more efficient, or
+implement a lightweight heuristic. For example, for a WSN of 200 sensor nodes, a leader
+node has to deal with constraints induced by about 12 sensor nodes. In that case, to solve the optimization problem a
+memory consumption of more than 1 MB can be observed with GLPK, whereas with CPLEX less than 300 kB would be needed.  
 
 % No discussion about the execution of GLPK on a sensor ?
 
@@ -789,7 +794,9 @@ protocol~\citep{Idrees2}, is an improved version of a research work we presented
 in~\citep{idrees2014coverage}. Let us  notice that PeCO and  DiLCO protocols are
 based on the same framework. In particular,  the choice for the simulations of a
 partitioning  in   16~subregions  was  made   because  it  corresponds   to  the
-configuration  producing  the  best  results   for  DiLCO.   The  protocols  are
+configuration  producing  the  best  results   for  DiLCO. Of course, this number of subregions sould be adapted according to the size of the area of interest and the number of sensors. 
+
+  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,
