end of reading

diff --git a/LiCO_Journal.tex b/LiCO_Journal.tex
index 7e788d4f6524d030e7276e745ed2a82c4ba02b61..ad465b3e27d2fb0821c46f866e681906cac8296c 100644 (file)
--- a/LiCO_Journal.tex
+++ b/LiCO_Journal.tex
@@ -726,7 +726,7 @@ different node densities going from  100 to 300~nodes were performed considering
 each time 25~randomly  generated networks. The nodes are deployed  on a field of
 interest of $(50 \times 25)~m^2 $ in such a way that they cover the field with a
 high coverage ratio. Each node has an  initial energy level, in Joules, which is
 each time 25~randomly  generated networks. The nodes are deployed  on a field of
 interest of $(50 \times 25)~m^2 $ in such a way that they cover the field with a
 high coverage ratio. Each node has an  initial energy level, in Joules, which is

-randomly drawn in the interval $[500-700]$.   If it's energy provision reaches a
+randomly drawn in the interval $[500-700]$.   If its energy provision reaches a

 value below  the threshold $E_{th}=36$~Joules,  the minimum energy needed  for a
 node  to stay  active during  one period,  it will  no more  participate in  the
 coverage task. This value corresponds to the energy needed by the sensing phase,
 value below  the threshold $E_{th}=36$~Joules,  the minimum energy needed  for a
 node  to stay  active during  one period,  it will  no more  participate in  the
 coverage task. This value corresponds to the energy needed by the sensing phase,


@@ -821,6 +821,7 @@ one,  called  DESK,  is  a  fully distributed  coverage  algorithm  proposed  by
 \cite{ChinhVu}. The second one,  called GAF~\cite{xu2001geography}, consists in
 dividing  the monitoring  area into  fixed  squares. Then,  during the  decision
 phase, in each square, one sensor is  chosen to remain active during the sensing
 \cite{ChinhVu}. The second one,  called GAF~\cite{xu2001geography}, consists in
 dividing  the monitoring  area into  fixed  squares. Then,  during the  decision
 phase, in each square, one sensor is  chosen to remain active during the sensing

+%%RC can we download DILCO?

 phase. The last  one, the DiLCO protocol~\cite{Idrees2}, is  an improved version
 of a research work we presented in~\cite{idrees2014coverage}. Let us notice that
 LiCO and  DiLCO protocols are  based on the  same framework. In  particular, the
 phase. The last  one, the DiLCO protocol~\cite{Idrees2}, is  an improved version
 of a research work we presented in~\cite{idrees2014coverage}. Let us notice that
 LiCO and  DiLCO protocols are  based on the  same framework. In  particular, the


@@ -920,7 +921,7 @@ different  network  sizes.   As  highlighted  by  these  figures,  the  lifetime
 increases with the size  of the network, and it is clearly  the larger for DiLCO
 and LiCO  protocols.  For instance,  for a  network of 300~sensors  and coverage
 ratio greater than 50\%, we can  see on Figure~\ref{fig3LT}(b) that the lifetime
 increases with the size  of the network, and it is clearly  the larger for DiLCO
 and LiCO  protocols.  For instance,  for a  network of 300~sensors  and coverage
 ratio greater than 50\%, we can  see on Figure~\ref{fig3LT}(b) that the lifetime

-is about two times longer with  LiCO compared to DESK protocol.  The performance
+is about twice longer with  LiCO compared to DESK protocol.  The performance

 difference    is    more    obvious   in    Figure~\ref{fig3LT}(b)    than    in
 Figure~\ref{fig3LT}(a) because the gain induced  by our protocols increases with
 the time, and the lifetime with a coverage  of 50\% is far more longer than with
 difference    is    more    obvious   in    Figure~\ref{fig3LT}(b)    than    in
 Figure~\ref{fig3LT}(a) because the gain induced  by our protocols increases with
 the time, and the lifetime with a coverage  of 50\% is far more longer than with


@@ -964,7 +965,7 @@ not so bad for the smallest network sizes.
 \label{sec:Conclusion and Future Works}
 
 In this paper  we have studied the problem of  lifetime coverage optimization in
 \label{sec:Conclusion and Future Works}
 
 In this paper  we have studied the problem of  lifetime coverage optimization in

-WSNs. We designed  a new protocol, called Lifetime  Coverage Optimization, which
+WSNs. We have designed  a new protocol, called Lifetime  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
 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


@@ -982,7 +983,7 @@ targets/points to be covered.
 %periods, each period consists  of four stages: (i) Information Exchange,
 %(ii) Leader Election, (iii) a Decision based new optimization model in order to
 %select the  nodes remaining  active for the last stage,  and  (iv) Sensing.
 %periods, each period consists  of four stages: (i) Information Exchange,
 %(ii) Leader Election, (iii) a Decision based new optimization model in order to
 %select the  nodes remaining  active for the last stage,  and  (iv) Sensing.

-We  carried out  several simulations  to  evaluate the  proposed protocol.   The
+We  have carried out  several simulations  to  evaluate the  proposed protocol.   The

 simulation  results  show   that  LiCO  is  more   energy-efficient  than  other
 approaches, with respect to lifetime,  coverage ratio, active sensors ratio, and
 energy consumption.
 simulation  results  show   that  LiCO  is  more   energy-efficient  than  other
 approaches, with respect to lifetime,  coverage ratio, active sensors ratio, and
 energy consumption.


@@ -1000,8 +1001,8 @@ sensor-testbed to evaluate it in real world applications.
 
 \noindent  As a  Ph.D.   student, Ali  Kadhum IDREES  would  like to  gratefully
 acknowledge the  University of Babylon -  IRAQ for financial support  and Campus
 
 \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 has also been supported by the Labex
-ACTION.
+France for the  received support. This work is also partially funded by the Labex ACTION program (contract ANR-11-LABX-01-01). 
+

 
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