X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/dc6cf8e426e52e11890b51d8cfbe8193285bea12..refs/heads/master:/CHAPITRE_02.tex?ds=sidebyside

diff --git a/CHAPITRE_02.tex b/CHAPITRE_02.tex
index ea8bba5..43a8692 100644
--- a/CHAPITRE_02.tex
+++ b/CHAPITRE_02.tex
@@ -44,7 +44,7 @@ This chapter concentrates only on area coverage and target coverage problems bec
 This dissertation mainly focuses on the area coverage problem, where the ultimate goal is to choose the minimum number of sensor nodes to cover the whole sensing field. 
 %We have focused mainly on the area coverage problem. Therefore, we represent the sensing area of each sensor node in the sensing field as a set of  primary points and then achieving full area coverage by covering all the points in the sensing field. The ultimate goal of the area coverage problem is to choose the minimum number of sensor nodes to cover the whole sensing region and prolonging the lifetime of the WSN. 
 
-Many centralized and distributed coverage algorithms for activity scheduling have been proposed in the literature, based on different assumptions and objectives. In centralized algorithms, a central controller (base station) makes all decisions and distributes the results to sensor nodes. The centralized algorithms have the advantage of requiring very low processing power from the sensor nodes (except for the base station) which have usually limited processing capabilities.  On the contrary, the exchange of packets in large WSNs may consume a considerable amount of energy in a centralized approach compared to a distributed one. The exchange of packets is between the sensor nodes and the base station. The centralized algorithms ensure nearly or close to optimal solution . They provide less redundant active sensor nodes during monitoring the sensing field. Moreover, centralized approaches usually suffer from the scalability and reliability problems, making them less competitive than the network size increases.
+Many centralized and distributed coverage algorithms for activity scheduling have been proposed in the literature, based on different assumptions and objectives. In centralized algorithms, a central controller (base station) makes all decisions and distributes the results to sensor nodes. The centralized algorithms have the advantage of requiring very low processing power from the sensor nodes (except for the base station) which have usually limited processing capabilities.  On the contrary, the exchange of packets in large WSNs may consume a considerable amount of energy in a centralized approach compared to a distributed one. The exchange of packets is between the sensor nodes and the base station. Centralized algorithms provide solutions  close to optimal solutions. They provide less redundant active sensor nodes during monitoring the sensing field. But, centralized approaches usually suffer from the scalability and reliability problems, making them less competitive when the network size increases. 
 
 In distributed algorithms, on the other hand, the decision process is localized in each individual sensor node, and only informations from neighboring nodes are used for the activity decision. Overall, distributed algorithms are more suitable for large-scale networks, but it can not give an optimal (or near-optimal) solution based only on local informations. They provide more redundant active sensor nodes during monitoring the sensing field. The exchange of packets is between the sensor nodes and their neighbors. Distributed algorithms are more robust against sensor failure.  Moreover, a recent study conducted in \cite{ref226} concludes that there is a threshold in terms of network size to switch from a distributed to a centralized algorithm. 
 
@@ -114,7 +114,7 @@ Y. Li et al.~\cite{ref142} present a framework with heuristic strategies to solv
 
 In the case of non-disjoint algorithms~\cite{ref117,ref167,ref144,ref147,ref118}, sensors may participate in more than one  cover set. In some cases, this may prolong the lifetime of the network in comparison  to the disjoint cover set algorithms, but designing  algorithms for  non-disjoint cover  sets generally  induces  a higher order  of complexity. Moreover, in  case of a sensor's  failure, non-disjoint scheduling  policies are less resilient and reliable because a sensor may be involved in more than one cover sets. For instance,  
 %%%Cardei et al.~\cite{ref167} present a  Linear Programming (LP)  solution and a greedy  approach to extend the  sensor network lifetime  by organizing the sensors  into a maximal  number of  non-disjoint cover  sets. Simulation  results show that by allowing sensors to  participate in multiple sets, the network lifetime increases compared with related work~\cite{ref115}. 
-The authors in~\cite{ref148}, address the problem of minimum cost area coverage in which full coverage is performed by using the minimum number of sensors for an arbitrary geometric shape region. A geometric solution to the minimum cost coverage problem under a deterministic deployment is proposed. The probabilistic coverage solution which provides a relationship between the probability of coverage and the number of randomly deployed sensors in an arbitrarily-shaped region is suggested. 
+the authors in~\cite{ref148}, address the problem of minimum cost area coverage in which full coverage is performed by using the minimum number of sensors for an arbitrary geometric shape region. A geometric solution to the minimum cost coverage problem under a deterministic deployment is proposed. The probabilistic coverage solution which provides a relationship between the probability of coverage and the number of randomly deployed sensors in an arbitrarily-shaped region is suggested. 
 %%%The work in~\cite{ref144} addresses the area coverage problem by proposing a Geometrically based Activity Scheduling scheme, named GAS, to fully cover the area of interest in WSNs. The authors deal with a small area, called target area coverage, which can be monitored by a single sensor instead of area coverage, which focuses on a large area that should be monitored by many sensors cooperatively. They explain that GAS is capable to monitor the target area by using the fewest number of sensors and it can produce as many cover sets as possible. A novel area coverage method to divide the sensors called Node Coverage Grouping (NCG) is suggested~\cite{ref147}. The sensors in the connectivity group are within sensing range of each other and the data collected by those in the same group are supposed to be similar. They prove that dividing N sensors via NCG into connectivity groups is an NP-hard problem. So, a heuristic algorithm of NCG with time complexity of $O(n^3)$ is proposed. For some applications, such as monitoring an ecosystem with extremely diversified environment, it might be a premature assumption that sensors near to each other sense similar data. 
 The problem of k-coverage  over the area of interest in WSNs is addressed in~\cite{ref152}. It is mathematically formulated and the spatial sensor density for full k-coverage is determined. The relation between the communication range and the sensing range is constructed by this work to retain the k-coverage and connectivity in WSN. After that, four configuration protocols are proposed for treating the k-coverage in WSNs. Simulation results show that their protocols outperform an existing distributed k-coverage configuration protocol. The work presented in~\cite{ref151} solves the area coverage and connectivity problem in sensor networks in an integrated way. The network lifetime is divided into a fixed number of rounds. A coverage bitmap of sensors of the domain is generated in each round and based on this bitmap,  it is decided which sensors stay active or go to sleep. They check the connection of the graph via laplacian of the adjacency graph of active sensors in each round. They define the connected coverage problem as an optimization problem and a centralized genetic algorithm is used to find the solution. Recent studies show an increasing interest in the use of exact schemes to solve optimization problems in WSNs \cite{ref230,ref231,ref121,ref122,ref120}. Column Generation (CG) has been widely used to address different versions of Maximum-network Lifetime Problem (MLP). CG decomposes the problem into a Restricted Master Problem (RMP) and a Pricing Subproblem (PS). The former maximizes lifetime using an incomplete set of columns and the latter is used to identify new profitable columns.  
 %%%A. Rossi et al.~\cite{ref121} introduce an efficient implementation of a genetic algorithm based on CG to extend the lifetime and maximize target coverage in wireless sensor networks under bandwidth constraints. The authors show that the use of metaheuristic methods to solve PS in the context of CG allows to obtain optimal solutions quite fast and to produce high-quality solutions when the algorithm is stopped before returning an optimal solution. More recently, 
@@ -122,7 +122,7 @@ The problem of k-coverage  over the area of interest in WSNs is addressed in~\ci
 
 More recently, 
 %%%the authors in~\cite{ref118} consider an area coverage optimization algorithm based on linear programming approach to select the minimum number of working sensor nodes, in order to preserve a  maximum coverage and to extend the lifetime of the network. The experimental results show that linear programming can provide a fewest number of active nodes and maximize the network lifetime coverage. 
-M. Rebai et al.~\cite{ref141}, formulate the problem of full grid area coverage problem using two integer linear programming models: the first, is in model that takes into account only the overall coverage constraint; the second, both the connectivity and the full grid coverage constraints are taken into consideration. This work does not consider the energy constraint. H. Cheng et al.~\cite{ref119} define a heuristic area coverage algorithm called Cover Sets Balance  (CSB), which  chooses  a set  of  active nodes  using  the tuple  (data coverage range, residual  energy).  Then, they introduce  a new Correlated Node Set Computing (CNSC) algorithm to  find the correlated node set for a given node. After that, they propose a High Residual Energy  First (HREF) node selection algorithm to minimize the number of active nodes. X. Liu et al.~\cite{ref143} explain that in some applications of WSNs such as Structural Health Monitoring (SHM) and volcano monitoring, the traditional coverage model which is a geographic area defined for individual sensors is not always valid. For this reason, they define a generalized area coverage model, which is not required to have the coverage area of individual nodes, but only based on a function deciding whether a set of sensor nodes is capable of satisfy the requested monitoring task for a certain area. They propose two approaches for dividing the deployed nodes into suitable cover sets.
+M. Rebai et al.~\cite{ref141}, formulate the problem of full grid area coverage problem using two integer linear programming models: the first, is a model that takes into account only the overall coverage constraint; the second, both the connectivity and the full grid coverage constraints are taken into consideration. This work does not consider the energy constraint. H. Cheng et al.~\cite{ref119} define a heuristic area coverage algorithm called Cover Sets Balance  (CSB), which  chooses  a set  of  active nodes  using  the tuple  (data coverage range, residual  energy).  Then, they introduce  a new Correlated Node Set Computing (CNSC) algorithm to  find the correlated node set for a given node. After that, they propose a High Residual Energy  First (HREF) node selection algorithm to minimize the number of active nodes. X. Liu et al.~\cite{ref143} explain that in some applications of WSNs such as Structural Health Monitoring (SHM) and volcano monitoring, the traditional coverage model which is a geographic area defined for individual sensors is not always valid. For this reason, they define a generalized area coverage model, which is not required to have the coverage area of individual nodes, but only based on a function deciding whether a set of sensor nodes is capable of satisfy the requested monitoring task for a certain area. They propose two approaches for dividing the deployed nodes into suitable cover sets.
 
 
   
@@ -149,7 +149,7 @@ field completely. Simulations results show that this approach can prolong the li
 
 The works presented in~\cite{ref134,ref135,ref136} focus on coverage-aware, distributed energy-efficient, and distributed clustering methods respectively, which aim at extending the network lifetime, while the coverage is ensured.
 
-In this dissertation, we focus in more details on two distributed coverage algorithms: GAF and DESK, because we compared our proposed coverage optimization protocols with them during performance evaluation. GAF algorithm is chosen for comparison as a competitor because it is famous and easy to implement, as well as many authors referred to it in many publications. DESK algorithm is also selected as competitor in the comparison because it works into rounds fashion (network lifetime divides into rounds) similar to our approaches, as well as DESK is a full distributed coverage approach. 
+In this dissertation, we focus in more details on two distributed coverage algorithms: GAF and DESK, because we compared our proposed coverage optimization protocols with them during performance evaluation. GAF algorithm is chosen for comparison as a competitor because it is famous and easy to implement, as well as many authors referred to it in many publications. DESK algorithm is also selected as competitor in the comparison because it works into rounds fashion (network lifetime divided into rounds) similar to our approaches, as well as DESK is a full distributed coverage approach. 
  
 
 \subsection{Geographical Adaptive Fidelity (GAF)}
@@ -164,7 +164,7 @@ GAF is developed by Xu et al. \cite{GAF}, it uses geographic location informatio
 \label{gaf1}
 \end{figure}
 
-For two adjacent squares grids, (for example, A and B in Figure~\ref{gaf1}) all sensor nodes inside A can communicate with sensor nodes inside B and vice versa. Therefore, all the sensor nodes are equivalent from the point of view the routing. The size of the fixed grid is based on the radio communication range $R_c$. It is supposed that the fixed grid is square with $r$ units on a side as shown in Figure~\ref{gaf1}. The distance between the farthest sensor nodes in two adjacent squares, such as B and C in Figure~\ref{gaf1}, should not be greater than the radio communication range $R_c$. For instance, the sensor node \textbf{2} of grid B can communicate with the sensor node \textbf{5} of square grid C. Thus, 
+For two adjacent squares grids, (for example, A and B in Figure~\ref{gaf1}) all sensor nodes inside A can communicate with sensor nodes inside B and vice versa. Therefore, all the sensor nodes are equivalent from the point of view of the routing. The size of the fixed grid is based on the radio communication range $R_c$. It is supposed that the fixed grid is square with $r$ units on a side as shown in Figure~\ref{gaf1}. The distance between the farthest sensor nodes in two adjacent squares, such as B and C in Figure~\ref{gaf1}, should not be greater than the radio communication range $R_c$. For instance, the sensor node \textbf{2} of grid B can communicate with the sensor node \textbf{5} of square grid C. Thus, 
 
 
 \begin{eqnarray}
@@ -179,7 +179,7 @@ or
 r \leq \dfrac{R_c}{\sqrt{5}} 
 \end{eqnarray}
 
-The sensor nodes in GAF can be in one of the folling three states: Active, Sleeping, or Discovery. Figure~\ref{gaf2} shows the state transition diagram. Each sensor node is initiated with discovery state. 
+The sensor nodes in GAF can be in one of the following three states: Active, Sleeping, or Discovery. Figure~\ref{gaf2} shows the state transition diagram. Each sensor node is initiated with discovery state. 
 In discovery state, the radio of each sensor node is turned on. Thereafter, the discovery messages are exchanged among the sensor nodes within the same grid. The discovery message consists of four fields, node id, grid id, estimated node active time (enat), and node state. The node uses its location and grid size to determine the square grid id.   
 
 \begin{figure}[h!]
@@ -234,7 +234,6 @@ w_{i} = \left \{
   \dfrac{\eta}{n_i^\alpha l(e_i,r_i)^\beta} * W + z & \mbox{if $e_i \geq e_{threshold}$} \\
   W & \mbox{otherwise,}\\
 \end{array} \right.
-%\label{eq12} 
 \notag
 \end{equation} 
 
@@ -346,9 +345,9 @@ check if its $n_i$ is decreased to 0 or not. If $n_i$ of a sensor node is 0 (i.e
 
 & \tiny  X. Deng et al. (2005)~\cite{ref133}  & \OK &   & \OK &  & \OK &  & \OK &  & \OK &  &  &  &\\
 
-&\textbf{\textcolor{red}{ \tiny DiLCO Protocol (2014)}}                  &  \textbf{\textcolor{red}{\OK}}   &   & \textbf{\textcolor{red}{\OK}}   &   &   & \textbf{\textcolor{red}{\OK}}  & \textbf{\textcolor{red}{\OK}}   &   & \textbf{\textcolor{red}{\OK}} &   &\textbf{\textcolor{red}{\OK}}  &    &  \\
+&\textbf{\textcolor{red}{ \tiny DiLCO Protocol (2015)}}                  &  \textbf{\textcolor{red}{\OK}}   &   & \textbf{\textcolor{red}{\OK}}   &   &   & \textbf{\textcolor{red}{\OK}}  & \textbf{\textcolor{red}{\OK}}   &   & \textbf{\textcolor{red}{\OK}} &   &\textbf{\textcolor{red}{\OK}}  &    &  \\
 
-&\textbf{\textcolor{red}{ \tiny MuDiLCO Protocol (2014)}}    &  \textbf{\textcolor{red}{\OK}}   &   & \textbf{\textcolor{red}{\OK}}   &   &   & \textbf{\textcolor{red}{\OK}}  & \textbf{\textcolor{red}{\OK}}   &   & \textbf{\textcolor{red}{\OK}} & \textbf{\textcolor{red}{\OK}}  &\textbf{\textcolor{red}{\OK}}  &    &  \\
+&\textbf{\textcolor{red}{ \tiny MuDiLCO Protocol (2015)}}    &  \textbf{\textcolor{red}{\OK}}   &   & \textbf{\textcolor{red}{\OK}}   &   &   & \textbf{\textcolor{red}{\OK}}  & \textbf{\textcolor{red}{\OK}}   &   & \textbf{\textcolor{red}{\OK}} & \textbf{\textcolor{red}{\OK}}  &\textbf{\textcolor{red}{\OK}}  &    &  \\
 
 &\textbf{\textcolor{red}{ \tiny PeCO Protocol (2015)}}                  &  \textbf{\textcolor{red}{\OK}}   &   & \textbf{\textcolor{red}{\OK}}   &   &   & \textbf{\textcolor{red}{\OK}}  & \textbf{\textcolor{red}{\OK}}   &   & \textbf{\textcolor{red}{\OK}} &   &\textbf{\textcolor{red}{\OK}}  &    &  \\
 
@@ -366,7 +365,7 @@ check if its $n_i$ is decreased to 0 or not. If $n_i$ of a sensor node is 0 (i.e
 \label{ch2:sec:05}
 This chapter describes some coverage problems in the literature, with their assumptions and proposed solutions.
 The coverage is considered as an essential requirement for many applications in WSNs because the better the coverage of an area of interest is, the better the sensing measurements of the physical phenomenon also is. Therefore, many extensive researches have been focused on that problem. These researches aim at designing mechanisms that efficiently manage or schedule the sensors after deployment, since sensor nodes have a limited battery life.
-Many coverage algorithms for maintaining the coverage and improving the network lifetime in WSNs were proposed. On the one hand, the full centralized coverage algorithms provide optimal or near optimal solution with low computation power for the sensors (except for the base station) but they deplete the battery power due to the communication overhead, so they are not scalable for large size networks. On the other hand, the distributed coverage algorithms provide a lower quality solution in comparison with centralized approaches and the communication between neighbors may be large especially for dense networks. Distributed coverage algorithms are reliable and scalable. The two coverage approaches has advantages and disadvantages. Therefore, each approach can be used based on the application requirements. We conclude from this chapter that it is desirable to introduce an hybrid approach to take into account the advantages of both centralized and distributed coverage approaches. Such an hybrid approach can provide a good quality coverage and prolong the network lifetime.
+Many coverage algorithms for maintaining the coverage and improving the network lifetime in WSNs were proposed. On the one hand, the full centralized coverage algorithms provide optimal or near optimal solution with low computation power for the sensors (except for the base station) but they deplete the battery power due to the communication overhead, so they are not scalable for large size networks. On the other hand, the distributed coverage algorithms provide a lower quality solution in comparison with centralized approaches and the communication between neighbors may be large especially for dense networks. Distributed coverage algorithms are reliable and scalable. The two coverage approaches have advantages and disadvantages. Therefore, each approach can be used based on the application requirements. We conclude from this chapter that it is desirable to introduce an hybrid approach to take into account the advantages of both centralized and distributed coverage approaches. Such an hybrid approach can provide a good quality coverage and prolong the network lifetime.
 
 
 
