X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/946555d85197e77a0b8f06b8ee4e4d2b6c788f3d..66978fd42279df51961394530794cf103cd3ab81:/INTRODUCTION.tex?ds=sidebyside

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@@ -29,11 +29,11 @@ election and sensor activity scheduling based optimization, where the challenges
  The developed optimization protocols should be able to perform a distributed optimization process on the subregions where the sensor nodes  in each subregion collaborate to select the leader by which the optimization algorithm is executed. In addition, the proposed protocols should be able to achieve effective trade-off between
 coverage quality and the consumed energy in each subregion of the sensing field in order to achieve extended network lifetime whilst maintaining adequate coverage ratio. 
 
-\section{Main Contributions of this Dissertation}
+\section{The main Contributions of this Dissertation}
  
 The coverage problem in WSNs is becoming more and more important for many applications ranging from military applications such as battlefield surveillance to the civilian applications such as health-care surveillance and habitant monitoring. The main contributions in this dissertation concentrate on design a distributed optimization protocols so as to extend the lifetime of the WSNs. The main contributions can be summarized as follow:
  
- \begin{enumerate} [i)]
+\begin{enumerate} [i)]
  \item In Chapter 3, we design a protocol that focuses on the area coverage problem with the objective of maximizing the network lifetime. Our proposition, the Distributed Lifetime Coverage Optimization (DILCO) protocol, maintains the coverage and improves the lifetime in WSNs. DILCO protocol presented in chapter 3 is an extension of our approach introduced in \cite{ref159}. In \cite{ref159}, the protocol is deployed over only two subregions. In DILCO protocol, the area of interest is first divided into subregions using a divide-and-conquer algorithm and an activity scheduling for sensor nodes is then planned by the elected leader in each subregion. 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 many node failures. Our DiLCO protocol considers periods, where a period starts with a discovery phase to exchange information between sensors of the same subregion, in order to choose in a suitable manner a sensor node (the leader) to carry out the coverage strategy. In each subregion the activation of the sensors for the sensing phase of the current period is obtained by solving an integer program. The resulting activation vector is broadcast by a leader to every node of its subregion.
 
 \item In Chapter 4, we extend our work that explained in chapter 3 and present a generalized framework that can be applied to provide the cover sets of all rounds in each period. The MuDiLCO protocol (for Multiround Distributed Lifetime Coverage Optimization protocol) presented in chapter 4 is an extension of the approach introduced in chapter 3. In DiLCO protocol, the activity scheduling based optimization is planned for each subregion periodically only for one round. Whilst, we study the possibility of dividing the sensing phase into multiple rounds and we also add an improved model of energy consumption to assess the efficiency of our approach. In fact, we make a multiround optimization, while it was a single round optimization in our previous work.
