update by Ali

[ThesisAli.git] / INTRODUCTION.tex

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 \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 spatial an temporal subdivision. On the one hand  the area of interest if divided into several smaller subregions and on the other hand the time line 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. We  propose a new mathematical  optimization model. Instead of  trying to cover a set of specified points/targets as in most of the methods proposed in the literature, we formulate an 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. So that an optimal scheduling  will be  obtained by  minimizing a  weighted sum  of these deviations. This contribution is demonstrated in Chapter 5.
 
 
 \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 spatial an temporal subdivision. On the one hand  the area of interest if divided into several smaller subregions and on the other hand the time line 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. We  propose a new mathematical  optimization model. Instead of  trying to cover a set of specified points/targets as in most of the methods proposed in the literature, we formulate an 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. So that an optimal scheduling  will be  obtained by  minimizing a  weighted sum  of these deviations. This contribution is demonstrated in Chapter 5.
 

-\item We add an improved model of energy consumption to assess the efficiency of our protocols as well as 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 to 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.
+\item We add an improved model of energy consumption to assess the efficiency of our protocols as well as 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 to 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.

 
 
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