Chapter 5 modified by Ali

[ThesisAli.git] / INTRODUCTION.tex

diff --git a/INTRODUCTION.tex b/INTRODUCTION.tex
index f591b02c100b547c612cfc2d2f9b6fb6271ad368..7a18e636a06b26977ba7070061737f27ea46eeef 100644 (file)
--- a/INTRODUCTION.tex
+++ b/INTRODUCTION.tex
@@ -10,7 +10,7 @@ The enormous development of wireless networks, with the emergence of fourth and
 
 WSN is an ad hoc wireless networks, which consists of a large number of wireless cheap devices called sensors. A sensor node can perform  communication, sensing, processing, and storage tasks with a limited capability. It can be used by human to monitor physical phenomena remotely and without any outside intervention. 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. The distributed self-organization and self-configuration capabilities of wireless sensor nodes enable myriad applications for monitoring, sensing and controlling the physical world.
 
 
 WSN is an ad hoc wireless networks, which consists of a large number of wireless cheap devices called sensors. A sensor node can perform  communication, sensing, processing, and storage tasks with a limited capability. It can be used by human to monitor physical phenomena remotely and without any outside intervention. 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. The distributed self-organization and self-configuration capabilities of wireless sensor nodes enable myriad applications for monitoring, sensing and controlling the physical world.
 

-The sensor nodes have several limitations, such as the power source, processing capability, bandwidth, uncertainty of sensed data, and the vulnerability of sensor nodes to the physical world. These limitations have been tackled by many researchers during the last years, and consequently, many solutions are taking these constraints into account have been proposed. Sensor nodes are battery-powered without means of recharging or replacing, usually due to environmental (hostile or unpractical environments) or cost reasons. %Since batteries are the most important limited resource inside sensor nodes, it is desirable that 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.
+The sensor nodes have several limitations, such as the power source, processing capability, bandwidth, uncertainty of sensed data, and the vulnerability of sensor nodes to the physical world. These limitations have been tackled by many researchers during the last years, and consequently, many solutions taking these constraints into account have been proposed. Sensor nodes are battery-powered without means of recharging or replacing, usually due to environmental (hostile or unpractical environments) or cost reasons. %Since batteries are the most important limited resource inside sensor nodes, it is desirable that 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 when designing of the WSN protocols due to 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 to minimize the energy consumption and extend the network lifetime during monitoring a certain area by a 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 proposed in this dissertation focus on the area coverage problem in WSNs. The major goal of the area coverage problem is to ensure monitoring the entire sensing field for as long as possible. The area coverage problem is closely related to the performance of WSNs in many applications, such as monitoring a battlefield, target detection, tracking, personal protection, animal habit monitoring, and homeland security.
 
 Since the network lifetime depends on sensor lifetime, the power depletion represents the most significant part when designing of the WSN protocols due to 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 to minimize the energy consumption and extend the network lifetime during monitoring a certain area by a 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 proposed in this dissertation focus on the area coverage problem in WSNs. The major goal of the area coverage problem is to ensure monitoring the entire sensing field for as long as possible. The area coverage problem is closely related to the performance of WSNs in many applications, such as monitoring a battlefield, target detection, tracking, personal protection, animal habit monitoring, and homeland security.


@@ -23,7 +23,7 @@ environments, it is desirable that a WSN should be deployed with high density be
 
 Although many works on energy-efficient coverage have been introduced, there is still need for a protocol which can schedule sensor nodes in an efficient way with: a minimum number of active sensors and less communication overhead so as to maintain the coverage and extend the network lifetime as long as possible. The main question is how to reduce the redundancy while maintaining a good coverage with minimum energy consumption?
  
 
 Although many works on energy-efficient coverage have been introduced, there is still need for a protocol which can schedule sensor nodes in an efficient way with: a minimum number of active sensors and less communication overhead so as to maintain the coverage and extend the network lifetime as long as possible. The main question is how to reduce the redundancy while maintaining a good coverage with minimum energy consumption?
  

-
+\iffalse

 \section*{3. The Objective of this Dissertation}
 \addcontentsline{toc}{section}{3. The Objective of this Dissertation}
 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. 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.
 \section*{3. The Objective of this Dissertation}
 \addcontentsline{toc}{section}{3. The Objective of this Dissertation}
 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. 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.


@@ -32,10 +32,10 @@ The proposed protocols should be able to combine two efficient techniques: netwo
 election and sensor activity scheduling based optimization, where the challenges include how to select the most efficient leader and the best representative active nodes which take the mission of monitoring during the current period.
 
  In addition, the developed optimization protocols should be able to perform a distributed optimization process, by subdividing into subregions the region of interest where the sensor nodes in each subregion collaborate to select the leader which execute the optimization algorithm. 
 election and sensor activity scheduling based optimization, where the challenges include how to select the most efficient leader and the best representative active nodes which take the mission of monitoring during the current period.
 
  In addition, the developed optimization protocols should be able to perform a distributed optimization process, by subdividing into subregions the region of interest where the sensor nodes in each subregion collaborate to select the leader which execute the optimization algorithm. 

- 
+\fi

 
 

-\section*{4. Main Contributions of this Dissertation}
- \addcontentsline{toc}{section}{4. Main Contributions of this Dissertation}
+\section*{3. Main Contributions of this Dissertation}
+ \addcontentsline{toc}{section}{3. 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 designing distributed optimization protocols to extend the lifetime of WSNs.  We summarize the main contributions of our research as follows:
  
 %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 designing distributed optimization protocols to extend the lifetime of WSNs.  We summarize the main contributions of our research as follows:
  


@@ -50,7 +50,7 @@ The MuDiLCO protocol for Multiround Distributed Lifetime Coverage Optimization p
 
 %\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 the spatial-temporal subdivision. On the one hand, the area of interest is divided into several smaller subregions and, on the other hand, the timeline 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. 
 
 
 %\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 the spatial-temporal subdivision. On the one hand, the area of interest is divided into several smaller subregions and, on the other hand, the timeline 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. 
 

-\item We have designed a third protocol, called Perimeter-based Coverage Optimization (PeCO).
+\item We design a third protocol, called Perimeter-based Coverage Optimization (PeCO).

 %which schedules nodes’ activities (wake up and sleep stages) with the objective of maintaining a good coverage ratio while maximizing the network lifetime. This protocol is applied in a distributed way in regular subregions obtained after partitioning the area of interest in a preliminary step. It works in periods and is based on the resolution of an integer program to select the subset of sensors operating in active status for each period.  
 We have proposed a new mathematical optimization model. Instead of  trying to cover a set of specified points/targets as in my previous protocols and 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. The idea is that an optimal scheduling  will be  obtained by  minimizing a weighted sum of these deviations. This contribution is demonstrated in chapter 6.
 
 %which schedules nodes’ activities (wake up and sleep stages) with the objective of maintaining a good coverage ratio while maximizing the network lifetime. This protocol is applied in a distributed way in regular subregions obtained after partitioning the area of interest in a preliminary step. It works in periods and is based on the resolution of an integer program to select the subset of sensors operating in active status for each period.  
 We have proposed a new mathematical optimization model. Instead of  trying to cover a set of specified points/targets as in my previous protocols and 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. The idea is that an optimal scheduling  will be  obtained by  minimizing a weighted sum of these deviations. This contribution is demonstrated in chapter 6.
 


@@ -66,7 +66,7 @@ We have proposed a new mathematical optimization model. Instead of  trying to co
 
 % \section{ Refereed Journal and Conference Publications}
  
 
 % \section{ Refereed Journal and Conference Publications}
  

-\section*{5. Dissertation Outline}
-\addcontentsline{toc}{section}{5. Dissertation Outline}
+\section*{4. Dissertation Outline}
+\addcontentsline{toc}{section}{4. 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 WSNs, prior works and current works. Evaluation tools and optimization solvers are investigated in chapter 3. Chapter 4 describes the proposed DiLCO protocol, while chapter 5 and 6 respectively present the MuDiLCO and PeCO protocols. Finally, we conclude our work in chapter 7.
  
 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 WSNs, prior works and current works. Evaluation tools and optimization solvers are investigated in chapter 3. Chapter 4 describes the proposed DiLCO protocol, while chapter 5 and 6 respectively present the MuDiLCO and PeCO protocols. Finally, we conclude our work in chapter 7.