\section{Wireless Sensor Network Architecture}
\label{ch1:sec:02}
-In a typical WSN architecture, the basic element is a typical wireless sensor node that composed of four major units~\cite{ref17,ref18}: sensing unit, computation unit, communication unit, and power unit. In addition, there are three optional units, which can be combined with the sensor node such as: localization system, mobilizer, and power generator. Figure~\ref{twsn} shows the components of a typical wireless sensor node~\cite{ref17}.
+A typical WSN architecture consists of a set of a typical wireless sensor nodes, which are capable of sensing the physical phenomenon around it such as fire in the forest (see~figure~\ref{wsn}), and then send the sensed data to a controller node called a sink. One or more sink in WSN are responsible for collecting and processing the sensed data by the wireless sensors, and then send it through the Internet to the end user.
+
+In those WSN architecture, the basic element is a typical wireless sensor node that composed of four major units~\cite{ref17,ref18}: sensing unit, computation unit, communication unit, and power unit. In addition, there are three optional units, which can be combined with the sensor node such as: localization system, mobilizer, and power generator. Figure~\ref{twsn} shows the components of a typical wireless sensor node~\cite{ref17}.
\begin{figure}[h!]
\centering
\item \textbf{Power Generator:} Several WSN applications need to operate for a longer time, so it is essential to equip the wireless sensor node with additional power source in order to prolong the network lifetime. The better energy source to generate the power for outdoor applications is a solar cells. An another power harvesting mechanisims~\cite{ref20,ref21} for thermal, motion, vibration, micro water flow, Biological, pressure gradients, and electromagnetic radiation energy harvesting can be used that yield increasing power output to extend the network lifetime.
\end{enumerate}
-The TinyOS has been used as an operating system in wireless sensor node. It is developed by the university of California, Berkeley and designed to work on platforms with limited storage and processing power.
-
-A typical WSN architecture consists of a set of a typical wireless sensor nodes, which are capable of sensing the phenomenon of interest around it such as fire in the forest (see~figure~\ref{wsn}) and then send the sensed data to a controller node called a sink. One or more sink in WSN are responsible for collecting and processing the sensed data by the wireless sensors, and then send it through the Internet to the end user.
\begin{figure}[h!]
\centering
\includegraphics[scale=0.9]{Figures/ch1/wsn.jpg}
\label{wsn}
\end{figure}
+The TinyOS has been used as an operating system in wireless sensor node. It is developed by the university of California, Berkeley and designed to work on platforms with limited storage and processing power.
+
\section{Types of Wireless Sensor Networks}
\label{ch1:sec:03}
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.
