X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/0a4831505b7eaf36f78bc512ac5c62f033fe0d59..65c128b3fb8dde9bbf4dd1fbe6b8b7853be42917:/CHAPITRE_02.tex

diff --git a/CHAPITRE_02.tex b/CHAPITRE_02.tex
index 4352893..43a8692 100644
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@@ -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. 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 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. 
 
@@ -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)}
@@ -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!]
@@ -365,7 +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.
 
 
 
