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}{We suppose that 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 their 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)}
+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).}
The sensors inside a same region cooperate to elect a leader. The selection
criteria for the leader are (in order of priority):
\begin{enumerate}
of sensors in the network.
\item {\bf \textcolor{blue}{Energy Saving Ratio (ESR)}}:
-\textcolor{blue}{we consider a performance metric linked to energy. This metric, called Energy Saving Ratio (ESR), is defined by:
+\textcolor{blue}{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}}
\subsubsection{\textcolor{blue}{Energy Saving Ratio (ESR)}}
-\textcolor{blue}{In this experiment, we consider an Energy Saving Ratio (see Figure~\ref{fig5}) for 200 deployed nodes.
-The longer the ratio is, the more redundant sensor nodes are switched off, and consequently the longer the network may live. }
+%\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 allows to
+ efficiently save 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, because GAF balances the energy consumption among
+ sensor nodes inside each small fixed grid and thus permits to extend the life of
+ sensors in each grid fairly but in the same time turn on large number of
+ sensors during sensing that lead later to quickly deplete sensor's batteries
+ together. After that GAF provide less energy saving compared with other
+ approaches because of the large number of dead nodes. DESK algorithm shows less
+ energy saving compared with other approaches due to activate a large number of
+ sensors during the sensing. DiLCO protocol provides less energy saving ratio
+ compared with PeCO because it generally activate a larger number of sensor
+ nodes during sensing. Note that again as the number of rounds increases PeCO
+ becomes the most performing one, since it consumes less energy compared with
+ other approaches.}
\begin{figure}[h!]
%\centering
\label{fig5}
\end{figure}
-\textcolor{blue}{The simulation results show that our protocol PeCO allows to efficiently save 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, because GAF balance the energy consumption among sensor nodes inside each small fixed grid that permits to extend the life of sensors in each grid fairly but in the same time turn on large number of sensors during sensing that lead later to quickly deplete sensor's batteries togehter. After that GAF provide less energy saving compared with other approches because of the large number of dead nodes. DESK algorithm shows less energy saving compared with other approaches due to activate a larg number of sensors during the sensing. DiLCO protocol provides less energy saving ratio commpared with PeCO because it generally activate a larger number of sensor nodes during sensing. Note that again as the number of rounds increases PeCO becomes the most performing one, since it consumes less energy compared with other approaches.}
\subsubsection{Energy Consumption}
\end{figure}
Figure~\ref{figure9} compares the lifetime coverage of the DiLCO and PeCO protocols
-for different coverage ratios. We denote by Protocol/50, Protocol/80,
+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 $50\%$, $80\%$, $85\%$,
+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
area to be monitored. For example, forest
fire application might require complete coverage
-in summer seasons while only requires 80$\%$ of the area to be covered in rainy seasons \cite{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 \cite{vu2009universal}. 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\cite{wang2014keeping}}. PeCO might be an interesting method since it achieves a good balance between a high level coverage ratio and network lifetime. PeCO
-always outperforms DiLCO for the three lower coverage ratios, moreover the
-improvements grow with the network size. \textcolor{blue}{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.}
+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!]
(contract ANR-11-LABX-01-01).
\bibliographystyle{gENO}
-\bibliography{biblio} % biblio
+\bibliography{biblio} %articleeo
\end{document}
