\section{Applications}
\label{ch1:sec:04}
%\indent The fast development in WSNs has been led to study their different characteristics extensively. However, the WSN is concentrated on various applications.
-In this section, we describe different academic and commercial applications. A WSN can use various types of sensors such as \cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar. These sensors are capable of observing a different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. Consequently, a wide range of WSN applications can be classified into five classes~\cite{ref22}, as shown in Figure~\ref{WSNAP}.
+In this section, we describe different academic and commercial applications. A WSN can use various types of sensors such as \cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar. These sensors are capable of observing different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. Consequently, a wide range of WSN applications can be classified into five classes~\cite{ref22}, as shown in Figure~\ref{WSNAP}.
\begin{figure}[h!]
\centering
As can be seen in Figure~\ref{fig2}, our protocol works in periods fashion, where each is divided into 4 phases: Information~Exchange, Leader~Election, Decision, and Sensing.
%The information exchange among wireless sensor nodes is described in chapter 4, section \ref{ch4:sec:02:03:01}. The leader election in each subregion is explained in chapter 4, section \ref{ch4:sec:02:03:02},
-The difference with MuDiLCO in that the elected leader in each subregion is for each period. In the decision phase, each leader will solve an integer program to select which cover sets will be activated in the following sensing phase to cover the subregion to which it belongs. The integer program will produce $T$ cover sets, one for each round. The leader will send an ActiveSleep packet to each sensor in the subregion based on the algorithm's results, indicating if the sensor should be active or not in
-each round of the sensing phase. Each sensing phase is itself divided into $T$ rounds and for each round a set of sensors (a cover set) is responsible for the sensing task.
-%Each sensor node in the subregion will receive an ActiveSleep packet from leader, informing it to stay awake or to go to sleep for each round of the sensing phase.
-Algorithm~\ref{alg:MuDiLCO}, which will be executed by each node at the beginning of a period, explains how the ActiveSleep packet is obtained. In this way, a multiround optimization process is performed during each
-period after Information~Exchange and Leader~Election phases, in order to produce $T$ cover sets that will take the mission of sensing for $T$ rounds.
\begin{figure}[ht!]
\centering \includegraphics[width=160mm]{Figures/ch5/GeneralModel.jpg} % 70mm Modelgeneral.pdf
\caption{MuDiLCO protocol.}
\end{figure}
-%This protocol minimizes the impact of unexpected node failure (not due to batteries running out of energy), because it works in periods. On the one hand, if a node failure is detected before making the decision, the node will not participate during this phase. On the other hand, if the node failure occurs after the decision, the sensing task of the network will be temporarily affected: only during the period of sensing until a new period starts.
-
-%The energy consumption and some other constraints can easily be taken into account since the sensors can update and then exchange their information (including their residual energy) at the beginning of each period. However, the pre-sensing phases (Information Exchange, Leader Election, and Decision) are energy consuming for some nodes, even when they do not join the network to monitor the area.
-
-
-
\begin{algorithm}[h!]
% \KwIn{all the parameters related to information exchange}
% \KwOut{$winer-node$ (: the id of the winner sensor node, which is the leader of current round)}
\end{algorithm}
+The difference with MuDiLCO in that the elected leader in each subregion is for each period. In the decision phase, each leader will solve an integer program to select which cover sets will be activated in the following sensing phase to cover the subregion to which it belongs. The integer program will produce $T$ cover sets, one for each round. The leader will send an ActiveSleep packet to each sensor in the subregion based on the algorithm's results, indicating if the sensor should be active or not in
+each round of the sensing phase. Each sensing phase is itself divided into $T$ rounds and for each round a set of sensors (a cover set) is responsible for the sensing task.
+%Each sensor node in the subregion will receive an ActiveSleep packet from leader, informing it to stay awake or to go to sleep for each round of the sensing phase.
+Algorithm~\ref{alg:MuDiLCO}, which will be executed by each node at the beginning of a period, explains how the ActiveSleep packet is obtained. In this way, a multiround optimization process is performed during each
+period after Information~Exchange and Leader~Election phases, in order to produce $T$ cover sets that will take the mission of sensing for $T$ rounds.
+
+
+%This protocol minimizes the impact of unexpected node failure (not due to batteries running out of energy), because it works in periods. On the one hand, if a node failure is detected before making the decision, the node will not participate during this phase. On the other hand, if the node failure occurs after the decision, the sensing task of the network will be temporarily affected: only during the period of sensing until a new period starts.
+
+%The energy consumption and some other constraints can easily be taken into account since the sensors can update and then exchange their information (including their residual energy) at the beginning of each period. However, the pre-sensing phases (Information Exchange, Leader Election, and Decision) are energy consuming for some nodes, even when they do not join the network to monitor the area.
+
+
+
As expected, the execution time increases with the number of rounds $T$ taken into account to schedule the sensing phase. The times obtained for $T=1,3$ or $5$ seem bearable, but for $T=7$ they become quickly unsuitable for a sensor node, especially when the sensor network size increases. Again, we can notice that if we want to schedule the nodes activities for a large number of rounds,
we need to choose a relevant number of subregions in order to avoid a complicated and cumbersome optimization.
-On the one hand, a large value for $T$ permits to reduce the energy overhead due to the three pre-sensing phases, on the other hand a leader node may waste a considerable amount of energy to solve the optimization problem. \\ \\ \\ \\ \\ \\ \\
+On the one hand, a large value for $T$ permits to reduce the energy overhead due to the three pre-sensing phases, on the other hand a leader node may waste a considerable amount of energy to solve the optimization problem. %\\ \\ \\ \\ \\ \\ \\
\item {{\bf Network lifetime}}
%\subsection{Network lifetime}
\contentsline {section}{\numberline {5.3}Primary Points based Multiround Coverage Problem Formulation}{103}{section.5.3}
\contentsline {section}{\numberline {5.4}Experimental Study and Analysis}{104}{section.5.4}
\contentsline {subsection}{\numberline {5.4.1}Simulation Setup}{104}{subsection.5.4.1}
-\contentsline {subsection}{\numberline {5.4.2}Metrics}{105}{subsection.5.4.2}
-\contentsline {subsection}{\numberline {5.4.3}Results Analysis and Comparison }{106}{subsection.5.4.3}
+\contentsline {subsection}{\numberline {5.4.2}Metrics}{104}{subsection.5.4.2}
+\contentsline {subsection}{\numberline {5.4.3}Results Analysis and Comparison }{105}{subsection.5.4.3}
\contentsline {section}{\numberline {5.5}Conclusion}{112}{section.5.5}
\contentsline {chapter}{\numberline {6} Perimeter-based Coverage Optimization to Improve Lifetime in WSNs}{113}{chapter.6}
\contentsline {section}{\numberline {6.1}Introduction}{113}{section.6.1}
\addjury{Karine}{Deschinkel}{Co-Supervisor}{Assistant Professor at University of Franche-Comt\'e}
\addjury{Michel}{Salomon}{Co-Supervisor}{Assistant Professor at University of Franche-Comt\'e}
\fi
- \addjury {}{Prof Sylvain CONTASSOT-VIVIER} {University of Lorraine} {Examiner}
-\addjury {} {Prof Ye-Qiong SONG} {University of Lorraine} {Reviewer}
+ \addjury {} {Prof Ye-Qiong SONG} {University of Lorraine} {Reviewer}
\addjury{} {Assoc Prof Hamida SEBA (HDR)} {University of Claude Bernard Lyon1} {Reviewer}
-\addjury {} {Prof Raphaël Couturier} {University of Franche-Comt\'e} {Director}
-\addjury {} {Asst Prof Karine Deschinkel} {University of Franche-Comt\'e} {Supervisor}
-\addjury {} {Asst Prof Michel Salomon} {University of Franche-Comt\'e} {Supervisor}
+\addjury {}{Prof Sylvain CONTASSOT-VIVIER} {University of Lorraine} {Examiner}
+\addjury {} {Prof Raphaël Couturier} {University of Franche-Comt\'e} {Supervisor}
+\addjury {} {Asst Prof Karine Deschinkel} {University of Franche-Comt\'e} { Co-supervisor}
+\addjury {} {Asst Prof Michel Salomon} {University of Franche-Comt\'e} {Co-supervisor}
% Supervisors:\\
