\title{Distributed Lifetime Coverage Optimization Protocol \\in Wireless Sensor Networks}
\author{\authorname{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier}
-\affiliation{FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comte, Belfort, France}
+\affiliation{FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comt\'e, Belfort, France}
%\affiliation{\sup{2}Department of Computing, Main University, MySecondTown, MyCountry}
\email{ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}
%\email{\{f\_author, s\_author\}@ips.xyz.edu, t\_author@dc.mu.edu}
techniques for solving linear programs with too many variables, have been also
used~\cite{castano2013column,rossi2012exact,deschinkel2012column}.
+% ***** Part which must be rewritten - Start
+
+% Start of Ali's papers catalog => there's no link between them or with our work
+% (use of subregions; optimization based method; etc.)
+
Diongue and Thiare~\cite{diongue2013alarm} proposed an energy aware sleep
scheduling algorithm for lifetime maximization in wireless sensor networks
(ALARM). The proposed approach permits to schedule redundant nodes according to
sensing during the current epoch. After that, the produced schedule is sent to
the sensor nodes in the network.
+% What is the link between the previous work and this paragraph about DiLCO ?
+
{\it In DiLCO protocol, the area coverage is divided into several smaller
subregions, and in each of which, a node called the leader is on charge for
selecting the active sensors for the current period.}
WSNs where each sensor has variable sensing radius. The final objective is to
maximize the network coverage lifetime in WSNs.
+% Same remark, no link with the two previous citations...
{\it In DiLCO protocol, each leader, in each subregion, solves an integer
program with a double objective consisting in minimizing the overcoverage and
limiting the undercoverage. This program is inspired from the work of
\cite{pedraza2006} where the objective is to maximize the number of cover
sets.}
+% ***** Part which must be rewritten - End
+
\iffalse
Some algorithms have been developed in ~\cite{yang2014energy,ChinhVu,vashistha2007energy,deschinkel2012column,shi2009,qu2013distributed,ling2009energy,xin2009area,cheng2014achieving,ling2009energy} to solve the area coverage problem so as to preserve coverage and prolong the network lifetime.
many performance metrics like coverage ratio or network lifetime. We have also
study the impact of the number of subregions chosen to subdivide the area of
interest, considering different network sizes. The experiments show that
-increasing the number of subregions allows to improves the lifetime. The more
-there are subregions, the more the network is robust against random
-disconnection resulting from dead nodes. However, for a given sensing field and
-network size 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.
+increasing the number of subregions improves the lifetime. The more there are
+subregions, the more the network is robust against random disconnection
+resulting from dead nodes. However, for a given sensing field and network size
+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.
\iffalse
\noindent In this paper, we have addressed the problem of the coverage and the lifetime
