lifetime by using an adaptive scheduling. The area of interest is
divided into subregions and an activity scheduling for sensor nodes is
planned for each subregion.
-% DEBUT AJOUT
-{\bf In fact, the nodes in a subregion can be seen as a cluster where
+ In fact, the nodes in a subregion can be seen as a cluster where
each node sends sensing data to the cluster head or the sink node.
Furthermore, the activities in a subregion/cluster can continue even
- if another cluster stops due to too much node failures.}
-% FIN AJOUT
+ if another cluster stops due to too much node failures.
Our scheduling scheme considers rounds, where a round starts with a
discovery phase to exchange information between sensors of the
subregion, in order to choose in suitable manner a sensor node to
different densities varying from 50 to 250~nodes. Experimental results
were obtained from randomly generated networks in which nodes are
deployed over a $(50 \times 25)~m^2 $ sensing field.
-% DEBUT MODIFICATION
-{\bf More precisely, the deployment is controlled at a coarse scale in
+More precisely, the deployment is controlled at a coarse scale in
order to ensure that the deployed nodes can fully cover the sensing
- field with the given sensing range.}
-% FIN MODIFICATION
+ field with the given sensing range.
10~simulation runs are performed with
different network topologies for each node density. The results
presented hereafter are the average of these 10 runs. A simulation
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.
-Table~\ref{table1} gives the average execution times {\bf in seconds}
-on a laptop of the decision phase
-% DEBUT AJOUT
-{\bf (resolution of the optimization problem)}
-% FIN AJOUT
+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
various numbers of sensors. The lack of any optimization explains why
the heuristic has very low execution times. Conversely, the Strategy
monitoring an area becomes disconnected. In figure~\ref{fig8}, the
network lifetime for different network sizes and for both Strategy
with Two Leaders and the Simple Heuristic is illustrated.
-% DEBUT MODIFICATION
-{\bf We do not consider anymore the centralized Strategy with One
+ We do not consider anymore the centralized Strategy with One
Leader, because, as shown above, this strategy results in execution
- times that quickly become unsuitable for a sensor network.}
-% FIN MODIFICATION
+ times that quickly become unsuitable for a sensor network.
\begin{figure}[h!]
%\centering
In future, we plan to study and propose a coverage protocol which
computes all active sensor schedules in a single round, using
optimization methods such as swarms optimization or evolutionary
-algorithms.
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-{\bf This single round will still consists of 4 phases, but the
+algorithms. This single round will still consists of 4 phases, but the
decision phase will compute the schedules for several sensing phases
- which aggregated together define a kind of meta-sensing phase.}
-% FIN AJOUT
+ which aggregated together define a kind of meta-sensing phase.
The computation of all cover sets in one round is far more
difficult, but will reduce the communication overhead.
