X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/32808ea756588e2c67d047ff4e644c9aeeeee844..6a69f6fbd00159f3ee3efd2536815457a556b09a:/CHAPITRE_05.tex?ds=inline

diff --git a/CHAPITRE_05.tex b/CHAPITRE_05.tex
index d4ebf28..c3839fc 100644
--- a/CHAPITRE_05.tex
+++ b/CHAPITRE_05.tex
@@ -235,7 +235,7 @@ precisely, the  deployment is controlled  at a coarse  scale in order  to ensure
 that  the deployed  nodes can  cover the  sensing field  with the  given sensing
 range.
 
-Our protocol is declined into four versions: MuDiLCO-1,  MuDiLCO-3, MuDiLCO-5, and  MuDiLCO-7, corresponding  respectively to  $T=1,3,5,7$ ($T$  the  number of rounds in one sensing period).  In  the following, we will make comparisons with three other methods. DESK \cite{DESK}, GAF~\cite{GAF}, and DiLCO~\cite{Idrees2}, where MuDiLCO-1 is similar to DiLCO.
+Our protocol is declined into four versions: MuDiLCO-1,  MuDiLCO-3, MuDiLCO-5, and  MuDiLCO-7, corresponding  respectively to  $T=1,3,5,7$ ($T$  the  number of rounds in one sensing period).  In  the following, we will make comparisons with three other methods. DESK \cite{DESK}, GAF~\cite{GAF}, and DiLCO~\cite{Idrees2}, where MuDiLCO-1 is the same of DiLCO.
 %Some preliminary experiments were performed in chapter 4 to study the choice of the number of subregions  which subdivides  the  sensing field,  considering different  network sizes. They show that as the number of subregions increases, so does the network lifetime. Moreover,  it makes  the MuDiLCO protocol  more robust  against random network  disconnection due  to node  failures.  However,  too  many subdivisions reduce the advantage  of the optimization. In fact, there  is a balance between the  benefit  from the  optimization  and the  execution  time  needed to  solve it. Therefore, 
 We set the number of subregions to 16 rather than 32 as explained in section \ref{ch4:sec:04:05}. 
 We use the modeling language and the optimization solver which are mentioned in section \ref{ch4:sec:04:02}. 
@@ -453,9 +453,7 @@ energy consumption,  since network lifetime and energy  consumption are directly
 We have addressed  the problem of the coverage and of the lifetime optimization in wireless  sensor networks.  This is a key  issue as  sensor nodes  have limited resources in terms of memory, energy, and computational power. To cope with this problem,  the field  of sensing  is divided  into smaller subregions  using the concept of divide-and-conquer method, and  then  we propose  a protocol  which optimizes coverage  and lifetime performances in each  subregion.  Our protocol,
 called MuDiLCO (Multiround  Distributed Lifetime Coverage Optimization) combines two  efficient   techniques:  network   leader  election  and   sensor  activity scheduling. The activity  scheduling in each subregion  works in periods,  where each period consists of four  phases: (i) Information Exchange, (ii)  Leader Election, (iii) Decision Phase to plan the activity  of the sensors over $T$ rounds, (iv) Sensing Phase itself divided into T rounds.
 
-Simulations  results show the  relevance of the proposed  protocol in  terms of lifetime, coverage  ratio, active  sensors ratio, energy  consumption, execution time. Indeed,  when dealing with  large wireless sensor networks,  a distributed approach, like  the one we  propose, allows to reduce the difficulty of  a single global optimization problem by partitioning it into many smaller problems, one per subregion, that can be solved  more easily. Nevertheless, results also show that it is not possible to plan the activity of sensors over too many rounds because the resulting optimization problem leads to too high-resolution times and thus to an excessive energy consumption. 
-
-
+Simulations  results show the  relevance of the proposed  protocol in  terms of lifetime, coverage  ratio, active  sensors ratio, energy  consumption, execution time. Indeed,  when dealing with  large wireless sensor networks,  a distributed approach, like  the one we  propose, allows to reduce the difficulty of  a single global optimization problem by partitioning it into many smaller problems, one per subregion, that can be solved  more easily. Nevertheless, results also show that it is not possible to plan the activity of sensors over too many rounds because the resulting optimization problem leads to too high-resolution times and thus to an excessive energy consumption. Compared with DiLCO, It is clear that MuDiLCO improves the network lifetime especially for the dense network, but it is  less robust than DiLCO under  sensor nodes failures. Therefore, choosing the number of rounds $T$ depends on the type of application the WSN is deployed for. 
 
 
  
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