Simple update by Ali

[Sensornets15.git] / Example.tex

diff --git a/Example.tex b/Example.tex
index bd29ee69d763da4d19ad50ad77cb5694cc7e7357..ec9239052db043414374621c5a201bd5bcd711ae 100644 (file)
--- a/Example.tex
+++ b/Example.tex
@@ -168,11 +168,6 @@ used~\cite{castano2013column,rossi2012exact,deschinkel2012column}. {\it  In DiLC
   \cite{pedraza2006}  where the  objective is  to maximize  the number  of cover
   sets.}
 
-<<<<<<< HEAD
-=======
-
-
->>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba
 
 \section{\uppercase{Description of the DiLCO protocol}}
 \label{sec:The DiLCO Protocol Description}
@@ -182,10 +177,7 @@ on  each subregion  in  the area  of interest.   It  is based  on two  efficient
 techniques: network leader election  and sensor activity scheduling for coverage
 preservation  and  energy  conservation,  applied  periodically  to  efficiently
 maximize the lifetime in the network.
-<<<<<<< HEAD
-=======
 
->>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba
 
 \subsection{Assumptions and models}
 
@@ -280,12 +272,6 @@ to each sensor  in the same subregion to  indicate it if it has to  be active or
 not.  Alternately, if  the  sensor  is not  the  leader, it  will  wait for  the
 Active-Sleep packet to know its state for the coming sensing phase.
 
-<<<<<<< HEAD
-=======
-
-
-
->>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba
 
 \begin{algorithm}[h!]                
 
@@ -548,8 +534,7 @@ the efficiency of our approach:
   connectivity  is crucial because  an active  sensor node  without connectivity
   towards a base  station cannot transmit any information  regarding an observed
   event in the area that it monitors.
-  
-    
+     
 \item {{\bf Coverage Ratio (CR)}:} it measures how well the WSN is able to 
   observe the area of interest. In our case, we discretized the sensor field
   as a regular grid, which yields the following equation to compute the
@@ -562,15 +547,6 @@ where  $n$ is  the number  of covered  grid points  by active  sensors  of every
 subregions during  the current  sensing phase  and $N$ is the total number  of grid
 points in  the sensing field. In  our simulations, we have  a layout of  $N = 51
 \times 26 = 1326$ grid points.
-<<<<<<< HEAD
-=======
-%The accuracy of this method depends on the distance between grids. In our
-%simulations, the sensing field has been divided into 50 by 25 grid points, which means
-%there are $51 \times 26~ = ~ 1326$ points in total.
-% Therefore, for our simulations, the error in the coverage calculation is less than ~ 1 $\% $.
-
-
->>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba
 
 \item {{\bf  Energy Consumption}:}  energy consumption (EC)  can be seen  as the
   total amount of  energy   consumed   by   the   sensors   during   $Lifetime_{95}$   or
@@ -593,12 +569,7 @@ refers to the energy needed by all the leader nodes to solve the integer program
 during a period.  Finally, $E^a_{m}$ and $E^s_{m}$ indicate  the energy consumed
 by the whole network in the sensing phase (active and sleeping nodes).
 
-<<<<<<< HEAD
-=======
-
 
-
->>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba
 \end{itemize}
 %\end{enumerate}
 
@@ -650,10 +621,7 @@ nodes, and thus enables the extension of the network lifetime.
 \label{fig3}
 \end{figure} 
 
-<<<<<<< HEAD
-=======
 
->>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba
 \subsubsection{Energy consumption}
 
 Based on  the results shown in  Figure~\ref{fig3}, we focus on  the DiLCO-16 and
@@ -713,10 +681,6 @@ prevents it  to  ensure a  good  coverage   especially  on   the  borders   of
 subregions. Thus,  the optimal number of  subregions can be seen  as a trade-off
 between execution time and coverage performance.
 
-<<<<<<< HEAD
-=======
-
->>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba
 \subsubsection{Network lifetime}
 
 In the next figure, the network lifetime is illustrated. Obviously, the lifetime
@@ -740,11 +704,7 @@ DESK and GAF for the lifetime of  the network. More specifically, if we focus on
 the larger level  of coverage ($95\%$) in the case of  our protocol, the subdivision
 in $16$~subregions seems to be the most appropriate.
 
-<<<<<<< HEAD
-=======
 
-
->>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba
 \section{\uppercase{Conclusion and future work}}
 \label{sec:Conclusion and Future Works} 
 
@@ -771,11 +731,6 @@ there is an optimal number of  subregions.  Therefore, in case of our simulation
 context  a subdivision in  $16$~subregions seems  to be  the most  relevant. The
 optimal number of subregions will be investigated in the future.
 
-<<<<<<< HEAD
-=======
-
-
->>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba
 \section*{\uppercase{Acknowledgements}}
 
 \noindent  As a  Ph.D.   student, Ali  Kadhum  IDREES would  like to  gratefully