\newlabel{fig3}{{2}{6}}
\newlabel{fig95}{{3}{7}}
\newlabel{fig8}{{4}{7}}
-\newlabel{figLT95}{{5}{7}}
\bibstyle{apalike}
\bibdata{Example}
\bibcite{berman04}{Berman and Calinescu, 2004}
\bibcite{kim2013maximum}{Kim and Cobb, 2013}
\bibcite{Kumar:2005}{Kumar et\nobreakspace {}al., 2005}
\bibcite{li2013survey}{Li and Vasilakos, 2013}
+\newlabel{figLT95}{{5}{8}}
\newlabel{sec:Conclusion and Future Works}{{6}{8}}
\bibcite{ling2009energy}{Ling and Znati, 2009}
\bibcite{pujari2011high}{Manju and Pujari, 2011}
subregions. Thus, the optimal number of subregions can be seen as a trade-off
between execution time and coverage performance.
-%The DiLCO-32 has more suitable times in the same time it turn on redundent nodes more. We think that in distributed fashion the solving of the optimization problem in a subregion can be tackled by sensor nodes. Overall, to be able to deal with very large networks, a distributed method is clearly required.
\subsubsection{Network lifetime}