X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/LiCO.git/blobdiff_plain/96d9b61965449470ef69145928021ced6bdc607d..8672f2758b38ef0ee17391e8ae39be587be825c3:/PeCO-EO/articleeo.tex

diff --git a/PeCO-EO/articleeo.tex b/PeCO-EO/articleeo.tex
index 23f8cb3..b6becc3 100644
--- a/PeCO-EO/articleeo.tex
+++ b/PeCO-EO/articleeo.tex
@@ -507,7 +507,7 @@ in  the current  period.   Each  sensor node  determines  its  position and  its
 subregion using an  embedded GPS or a location discovery  algorithm. After that,
 all the sensors collect position  coordinates, remaining energy, sensor node ID,
 and the number of their one-hop  live neighbors during the information exchange.
-\textcolor{blue}{Both INFO packet and ActiveSleep packet contain two parts: header and data payload. The sensor ID is included in the header, where the header size is 8 bits. The data part includes position coordinates (64 bits), remaining energy (32 bits), and the number of one-hop live neighbors (8 bits). Therefore the size of the INFO packet is 112 bits. The ActiveSleep packet is 16 bits size, 8 bits for the header and 8 bits for data part that includes only sensor status (0 or 1).}
+\textcolor{green}{Both INFO packet and ActiveSleep packet contain two parts: header and data payload. The sensor ID is included in the header, where the header size is 8 bits. The data part includes position coordinates (64 bits), remaining energy (32 bits), and the number of one-hop live neighbors (8 bits). Therefore the size of the INFO packet is 112 bits. The ActiveSleep packet is 16 bits size, 8 bits for the header and 8 bits for data part that includes only sensor status (0 or 1).}
 The sensors  inside a same  region cooperate to  elect a leader.   The selection
 criteria for the leader are (in order  of priority):
 \begin{enumerate}
@@ -719,8 +719,8 @@ approach.
   sensing period~$p$, $R$  is the number of subregions, and  $|J|$ is the number
   of sensors in the network.
   
-\item {\bf \textcolor{blue}{Energy Saving Ratio (ESR)}}:  
-\textcolor{blue}{this metric, which shows the ability of a protocol to save energy, is defined by:
+\item {\bf \textcolor{green}{Energy Saving Ratio (ESR)}}:  
+\textcolor{green}{this metric, which shows the ability of a protocol to save energy, is defined by:
 \begin{equation*}
 \scriptsize
 \mbox{ESR}(\%) = \frac{\mbox{Number of alive sensors during this round}}
@@ -853,12 +853,10 @@ keeping a greater coverage ratio as shown in Figure \ref{figure5}.
 \label{figure6}
 \end{figure} 
 
-\subsubsection{\textcolor{blue}{Energy Saving Ratio}} 
+\subsubsection{\textcolor{green}{Energy Saving Ratio}} 
 
-%\textcolor{blue}{In this experiment, we study the energy saving ratio, see Figure~\ref{fig5}, for 200 deployed nodes. 
-%The larger the ratio  is,  the more  redundant sensor  nodes are switched off, and consequently  the longer the  network may  liv%e. }
 
-\textcolor{blue}{The  simulation  results  show  that our  protocol  PeCO  saves
+\textcolor{green}{The  simulation  results  show  that our  protocol  PeCO  saves
   efficiently energy by  turning off some sensors during the  sensing phase.  As
   shown in  Figure~\ref{fig5}, GAF provides  better energy saving than  PeCO for
   the  first fifty  rounds. Indeed  GAF  balances the  energy consumption  among
@@ -934,14 +932,12 @@ for  different   coverage  ratios.   We  denote  by   Protocol/70,  Protocol/80,
 Protocol/85, Protocol/90,  and Protocol/95 the  amount of time during  which the
 network  can satisfy  an  area  coverage greater  than  $70\%$, $80\%$,  $85\%$,
 $90\%$, and  $95\%$ respectively,  where the  term Protocol  refers to  DiLCO or
-PeCO.  \textcolor{blue}{Indeed there are applications that do not require a 100\% coverage of the
+PeCO.  \textcolor{green}{Indeed there are applications that do not require a 100\% coverage of the
 area to be  monitored. For example, forest
 fire application might require complete coverage
 in summer seasons while only require 80$\%$ of the area to be covered in rainy seasons~\citep{li2011transforming}. As another example, birds habit study requires only 70$\%$-coverage at nighttime when the birds are sleeping while requires 100$\%$-coverage at daytime when the birds are active~\citep{1279193}. 
-%Mudflows monitoring applications may require part of the area to be covered in sunny days. Thus, to extend network lifetime, the coverage quality can be decreased if it is acceptable~\citep{wang2014keeping}}. 
  PeCO always  outperforms DiLCO  for the  three  lower coverage  ratios, moreover  the
 improvements grow  with the network  size. DiLCO outperforms PeCO when the coverage ratio is required to be $>90\%$, but PeCO extends the network lifetime significantly when coverage ratio can be relaxed.}
-%DiLCO  is better for  coverage ratios near 100\%, but  in that case PeCO  is not ineffective for  the smallest network sizes.
 
 \begin{figure}[h!]
 \centering \includegraphics[scale=0.55]{figure9.eps}
@@ -966,7 +962,7 @@ $\beta=0.4$ seems to  achieve the best compromise between  lifetime and coverage
 ratio.   That explains  why  we have  chosen this  setting  for the  experiments
 presented in the previous subsections.
 
-%As can be seen in Table~\ref{my-labelx},  it is obvious and clear that when $\alpha$ decreased and $\beta$ increased by any step, the network lifetime for $Lifetime_{50}$ increased and the $Lifetime_{95}$ decreased. Therefore, selecting the values of $\alpha$ and $\beta$ depend on the application type used in the sensor nework. In PeCO protocol, $\alpha$ and $\beta$ are chosen based on the largest value of network lifetime for $Lifetime_{95}$.
+
 
 \begin{table}[h]
 \centering
