\section{The Objective of this Dissertation}
-The primary objective of this dissertation is to develop energy-efficient distributed optimization protocols in wireless sensor networks that optimizes both coverage and network lifetime so as to maintain the coverage and prolongs the lifetime of the WSN. The developed protocols should schedule node’ activities (wake up and sleep stages) with the objective of maintaining a good coverage ratio while maximizing the network lifetime.
+The primary objective of this dissertation is to develop energy-efficient distributed optimization protocols in wireless sensor networks that optimize both coverage and network lifetime so as to maintain the coverage and prolong the lifetime of the WSN. The developed protocols should schedule node’ activities (wake up and sleep stages) with the objective of maintaining a good coverage ratio while maximizing the network lifetime.
The proposed protocols should be able to combine two efficient techniques: network leader
election and sensor activity scheduling based optimization, where the challenges include how to select the most efficient leader in each subregion and the best representative active nodes, which take the mission of monitoring during the current round.
- 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.
+ 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{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)]
- \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 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. 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.
+\item 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. In fact, we make a multiround optimization, while it was a single round optimization in our previous contribution.
-\item In Chapter 5, 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.
+\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 conducted extensive simulation experiments, using the discrete event simulator OMNeT++, to demonstrate the efficiency of our protocol. 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.
% \section{ Refereed Journal and Conference Publications}
\section{Dissertation Outline}
- The Dissertation is organized as follows: the next chapter presents a scientific background about wireless sensor networks .Chapter 2 states a review of the related literatures to the coverage problem in WSN, the prior works, and the current works. Chapter 3 describes the the proposed DiLCO protocol. Chapter 4 presents the MuDiLCO protocol. The LiCO protocol is illustrated in Chapter 5. Finally, we conclude our work in Chapter 6.
+The dissertation is organized as follows: the next chapter presents a scientific background about wireless sensor networks. Chapter 2 states a review of the related literatures to the coverage problem in WSN, the prior works, and the current works. Chapter 3 describes the the proposed DiLCO protocol. Chapter 4 presents the MuDiLCO protocol. The LiCO protocol is illustrated in Chapter 5. Finally, we conclude our work in Chapter 6.
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