X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/946555d85197e77a0b8f06b8ee4e4d2b6c788f3d..ed47f890104b60601b60870a8381deb7458cdabf:/INTRODUCTION.tex?ds=inline

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  \section{General Introduction}
-The enormous development in wireless networks and the emergence of fourth-generation technology led to the provision of various services to customers around the world that make of the Internet a more widely used everywhere. This kind of wireless networks may not be appropriate to be used in some sensitive areas that need to deploy a large number of wireless devices, which are capable of decide and communicate with each other in a distributed way  so as to collect the sensed measurements from the physical dangerous environment directly such as volcanoes, nuclear reactors, forest fires, or military battles. So, another type of wireless networks has been emerged to cope with these challenges which is called wireless sensor network (WSN). 
+The enormous development in wireless networks and the emergence of fourth and fifth-generation technology is led to provision of various services to customers around the world that make of the Internet a more widely used everywhere. This kind of wireless networks may not be appropriate to be used in some sensitive areas that need to deploy a large number of wireless devices, which are capable of decide and communicate with each other in a distributed way so as to collect the sensed measurements directly from the physical dangerous environment such as volcanoes, nuclear reactors, forest fires, or military battles. Therefore, another type of wireless networks has been emerged to cope with these challenges, which is called Wireless Sensor Network (WSN). 
 
-It is a special case of the ad hoc wireless networks and it consists of a large number of wireless devices are called sensors, which are able to perform the communication, sensing, processing and storage with a limited capabilities. A WSN can be used by the human to monitor the physical phenomena remotely and without outside intervention. Inside a WSN, the wireless sensor nodes are self-contained units equipped  with a radio transceiver, a microcontroller, a small memory, and a power source, usually a battery. These sensor nodes are cooperating together autonomously to perform the assigned tasks without the intervention or control from outside. The distributed self-organization and self-configuration capabilities of wireless sensor nodes make the distributed WSNs to enable myriad applications for monitoring, sensing and controlling the physical world.
+WSN is a special case of the ad hoc wireless networks and it consists of a large number of wireless cheap devices are called sensors, which are able to perform the communication, sensing, processing and storage with a limited capabilities. A WSN can be used by the human to monitor the physical phenomena remotely and without outside intervention. Inside a WSN, the wireless sensor nodes are self-contained units equipped  with a radio transceiver, a microcontroller, a small memory, and a power source, usually a battery. These sensor nodes are cooperating together autonomously to perform the assigned tasks without the intervention or control from outside. The distributed self-organization and self-configuration capabilities of wireless sensor nodes make the distributed WSNs to enable myriad applications for monitoring, sensing, and controlling the physical world.
+
+The rapid advancement in Micro Electro-Mechanical Systems (MEMS), wireless communication hardware, digital electronics, and  system-on-chip has given rise to use large networks of tiny sensors are becoming cheaper and more and more commercially available. The sensor nodes have several limitations, such as: power source, processing capability, bandwidth, uncertainty of sensed data, and the vulnerability of sensor nodes to physical world. These limitations have been tackled by many researchers during the last years, and consequently, many solutions have been proposed that take these constraints into account on the sensors. Sensor nodes are battery-powered with no means of recharging or replacing, usually due to environmental (hostile or unpractical environments) or cost reasons. Since the batteries are the most important limited resource inside the sensor nodes, therefore, it is desired that the WSNs are deployed with high densities so as to exploit the overlapping sensing regions of some sensor nodes to save energy by turning off some of them during the sensing phase to prolong the network lifetime.
+
+Since the network lifetime depends on sensor lifetime, the power depletion represents the most significant part during designing the WSN protocols because of the limited capacity of the sensor batteries.  The major goal is to extend the network lifetime, taking into consideration the energy source limitations. Several energy-efficient approaches have been suggested so as to minimize the energy consumption and extend the network lifetime during monitoring a certain area by WSN. For example, one of the ways is to turn off the redundant sensors and put them in sleep mode to maintain the energy, whilst the active sensors perform the sensing coverage task during their life. Specifically, the energy-efficient protocols, which are proposed in this dissertation focuses on the area coverage problem in WSNs. The major goal of the area coverage problem is to ensure a maximum area coverage ratio for the entire sensing field of the WSN and for a longer time as possible. The area coverage problem is closely related to the performance of systems in many application, such as, monitoring the battlefield, target detection, tracking, personal protection, animal habit monitoring, and homeland security.
 
-The fast and continuous progress in Micro Electro-Mechanical Systems (MEMS), wireless communication hardware, digital electronics, and  system-on-chip has given rise to the opportunity to use large networks of tiny sensors are becoming cheaper and more and more commercially available. There are several limitations on the sensor nodes such as power source, processing capability, bandwidth, uncertainty of sensed data, and the vulnerability of sensor nodes to physical world. These limitations have been tackled by many researchers during last years and many solutions have been proposed to handle these constraints on the sensors. Sensor nodes are battery-powered with no means of recharging or replacing, usually due to environmental (hostile or unpractical environments) or cost reasons. Since the batteries are the most important limited resource inside the sensor nodes, therefore, it is desired that the WSNs are deployed with high densities so as to exploit the overlapping sensing regions of some sensor nodes to save energy by turning off some of them during the sensing phase to prolong the network lifetime. Many energy-efficient mechanisms have been proposed so as to minimize the energy consumption during operating the WSN its monitoring task and to extend the network lifetime. Specifically, this dissertation focuses on the area coverage problem of in WSNs.
 
-The major goal of the area coverage problem is to ensure a maximum area coverage ratio for the entire sensing field of the WSN and for a longer time as possible. The area coverage problem is closely related to the performance of systems in many application, such as, monitoring the battlefield, target detection and tracking, personal protection, animal habit monitoring, and  homeland security.
 
 \section{Motivation of the Dissertation}
 One of the fundamental challenges in Wireless Sensor Networks (WSNs) is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area (or region) of interest. Since sensor nodes have limited battery life; since it is impossible to replace batteries, especially in remote and hostile
@@ -29,11 +31,11 @@ election and sensor activity scheduling based optimization, where the challenges
  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.
