carried out by a leader node which solves an integer program.
Simulation results show that the proposed approach can prolong the
network lifetime and improve the coverage performance.
-\end{abstract}
+\end{abstract*&^@!U&*T@}
%\keywords{Area Coverage, Wireless Sensor Networks, lifetime Optimization, Distributed Protocol.}
The first group of constraints indicates that some primary point $p$
should be covered by at least one sensor and, if it is not always the
case, overcoverage and undercoverage variables help balancing the
-restriction equation by taking positive values. There are two main %%RAPH restriction equations????
+restriction equations by taking positive values. There are two main
objectives. First we limit the overcoverage of primary points in order to
activate a minimum number of sensors. Second we prevent the absence of monitoring on
some parts of the subregion by minimizing the undercoverage. The
A sensor node has limited energy resources and computing power,
therefore it is important that the proposed algorithm has the shortest
possible execution time. The energy of a sensor node must be mainly
-used for the sensing phase, not for the pre-sensing ones. %%RAPH: plusieurs phase de pre-sensing??
+used for the sensing phase, not for the pre-sensing ones.
Table~\ref{table1} gives the average execution times in seconds
on a laptop of the decision phase (solving of the optimization problem)
during one round. They are given for the different approaches and
