X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/blobdiff_plain/6b1de891e2849975795b04843f3bfe3d5e44b667..0ffd0e6c8834d66f5f7c08ad1752ad0879b8d791:/bare_conf.tex diff --git a/bare_conf.tex b/bare_conf.tex index 0804bff..959f692 100755 --- a/bare_conf.tex +++ b/bare_conf.tex @@ -630,7 +630,7 @@ X_{j} \in \{0,1\}, &\forall j \in J 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 @@ -860,7 +860,7 @@ communications have a small impact on the network lifetime. 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 @@ -944,7 +944,7 @@ subdividing the sensing field into many subregions, which are managed independently and simultaneously, is the most relevant way to maximize the lifetime of a network. -\section{Conclusion and future forks} +\section{Conclusion and Future Works} \label{sec:conclusion} In this paper, we have addressed the problem of the coverage and the lifetime