X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/0a4831505b7eaf36f78bc512ac5c62f033fe0d59..81197a19a27249d75ca25536fab51cfbd68176b8:/INTRODUCTION.tex

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 The MuDiLCO protocol for Multiround Distributed Lifetime Coverage Optimization protocol, presented in chapter 5, is an extension of the approach introduced in chapter 4. In the DiLCO protocol, the activity scheduling based optimization is planned for each subregion periodically only for one sensing round.  We study the possibility of dividing the sensing phase into multiple rounds. In fact, we make a multiround optimization, while it was a single round optimization in our previous contribution. The activation of the sensors is planned for many rounds in advance compared with the previous approach. 
 
 %\item We devise a framework to schedule nodes to be activated alternatively such that the network lifetime is prolonged  while ensuring that a certain level of   coverage is preserved. A key idea in our framework is  to exploit the spatial-temporal subdivision. On the one hand, the area of interest is divided into several smaller subregions and, on the other hand, the timeline is divided into periods of equal length. In each subregion, the sensor nodes will cooperatively choose a  leader which will schedule  nodes' activities, and this grouping of sensors is similar to typical cluster architecture. 
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+ 
 \item We design a third protocol, called Perimeter-based Coverage Optimization (PeCO).
 %which schedules nodesâ activities (wake up and sleep stages) with the objective of maintaining a good coverage ratio while maximizing the network lifetime. This protocol is applied in a distributed way in regular subregions obtained after partitioning the area of interest in a preliminary step. It works in periods and is based on the resolution of an integer program to select the subset of sensors operating in active status for each period.  
-We have proposed a new mathematical optimization model. Instead of  trying to cover a set of specified points/targets as in previous protocols and most of the methods proposed in the literature, we formulate  mixed-integer program based on perimeter coverage of each sensor. The model involves integer variables to capture the deviations between the actual level of coverage and the required  level. The idea is that an optimal scheduling  will be  obtained by  minimizing a weighted sum of these deviations. This contribution is demonstrated in chapter 6.
+We have proposed a new mathematical optimization model. Instead of  trying to cover a set of specified points/targets as in previous protocols and most of the methods proposed in the literature, we formulate  a mixed-integer program based on perimeter coverage of each sensor. The model involves   variables to capture the deviations between the actual level of coverage and the required  level. The idea is that an optimal scheduling  will be  obtained by  minimizing a weighted sum of these deviations. This contribution is demonstrated in chapter 6.
 
 \item %We add an improved model of energy consumption to assess the efficiency of our protocols. 
 We conducted extensive simulation experiments using the discrete event simulator OMNeT++, to demonstrate the  efficiency of our protocols. We compared our proposed distributed optimization protocols with two approaches found in the literature: DESK~\cite{DESK} and GAF~\cite{GAF}. Simulation results based on multiple criteria (energy consumption, coverage ratio, network lifetime and so on) show that the proposed protocols can prolong efficiently the network lifetime and improve the coverage performance.
