X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/4affd5b38260812e93709165c95e5bba9030af55..c5833cac353b334ae2450046541cb61c6b5b54e3:/CHAPITRE_01.tex

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@@ -153,10 +153,10 @@ The most significant goal for many companies is the automation of controlling an
 
 \item \textbf{Coverage:} One of the fundamental challenges in WSNs is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area (or region) of interest. The major objective is to choose the minimum number of sensor nodes in order to monitor the target sensing field without affecting on the application requirements in executing its tasks as long time as possible. 
 
-\item \textbf{Routing:} Represents one of the important problems in WSNs that needs to be solved efficiently. The limited resources of WSNs and the impacts of wireless communication led to a big challenge in ensuring energy-efficient routing. However, it is not enough to use the shortest path to route the packets among the sensor nodes toward the sink. It is necessary to design an energy-efficient routing protocol that considers the remaining energy of sensor node during taking the decision to route the packet to the next hop toward the destination. This participates in energy conservation and balancing among the sensor node in WSNs. 
+\item \textbf{Routing:} Represents one of the important problems in WSNs that needs to be solved efficiently. The limited resources of WSNs and the impacts of wireless communication lead to a big challenge in ensuring energy-efficient routing. However, it is not enough to use the shortest path to route the packets among the sensor nodes toward the sink. It is necessary to design an energy-efficient routing protocol that considers the remaining energy of sensor node during taking the decision to route the packet to the next hop toward the destination. This participates in energy conservation and balancing among the sensor node in WSNs. 
 
 \item \textbf{Autonomous and Distributed Management:}
-Since the nature of many WSN applications need to be deployed in a remote or hostile environment, it is important that the wireless sensor nodes work in autonomous and distributed way to communicate and cooperate with other sensor nodes without human intervention because the maintenance or the repair may be difficult. The distributed management consumes less energy because it is based on only local information from the neighboring sensor nodes; on the other hand, it does not give the optimal solution, so the main challenge is how to apply the a distributed management in WSNs and in the same time ensuring an optimal or near optimal solution.
+Since the nature of many WSN applications need to be deployed in a remote or hostile environment, it is important that the wireless sensor nodes work in autonomous and distributed way to communicate and cooperate with other sensor nodes without human intervention because the maintenance or the repair may be difficult. The distributed management consumes less energy because it is based on only local information from the neighboring sensor nodes, moreover, it does not give the optimal solution. So the main challenge is how to apply the a distributed management in WSNs and in the same time ensuring an optimal or near optimal solution.
    
 \item \textbf{Scalability:} Many physical phenomenons require to be deployed densely with a large number of sensor nodes for different reasons such as: the large sensed area,  reliability requirement, or network lifetime prolongation. It is necessary that the proposed protocols in WSNs are scalable for these large number of sensor
 nodes in order to achieve their tasks efficiently.
@@ -205,7 +205,7 @@ nodes in order to achieve their tasks efficiently.
 \begin{enumerate}[(a)]
 \item Grouping the wireless sensor nodes into clusters is led to decrease the communication range within the cluster and therefore minimize the energy needed to communication among the nodes inside the cluster.
 \item Minimizing the energy hungry operations such as collaboration and aggregation to the cluster head.
-\item Limiting the number of communications (transmitting and receiving) due to the fusion operation carried out by the cluster head.
+%\item Limiting the number of communications (transmitting and receiving) due to the fusion operation carried out by the cluster head.
 \item The continuous changing of cluster head according to residual energy is led to balance the energy consumption among wireless sensor nodes inside the cluster.
 \item Some nodes can be turned-off within the same cluster whilst the cluster head manage the responsibilities.
 \end{enumerate}
@@ -296,7 +296,7 @@ Data driven schemes are classified into two main approaches~\cite{ref59,ref22}:
 
 \subsection{Battery Repletion:}
 
-\indent In the last years, extensive researches have been focused on energy harvesting and wireless charging techniques. These solutions represent alternate energy sources to recharge wireless sensor batteries without human intervention and instead of depending on the limited power supplied by a typical batteries~\cite{ref91,ref59}.
+\indent In the last years, extensive researches have been focused on energy harvesting and wireless charging techniques. These solutions represent alternate energy sources to recharge wireless sensor batteries without human intervention~\cite{ref91,ref59}.
 
 \subsubsection{Energy Harvesting:} In energy harvesting, several sources of environmental energy have been developed so as to enable the wireless sensors to acquire energy from the surrounding environment like solar, wind energy, vibration based energy harvesting, radio signals for scavenging RF power, Thermoelectric generators, and shoe-mounted piezoelectric generator to power artificial organs~\cite{ref59}. 
 
@@ -327,7 +327,25 @@ In WSNs includes a static sink, the wireless sensor nodes, which are near the si
 \label{ch1:sec:07}
 \indent The limited resources in WSNs have been addressed, and one of the main challenges in WSNs is the limited power resource. For this reason, extensive researches have been proposed in order to prolong the network lifetime by means of designing and implementing energy-efficient protocols. The reason for these large number of proposed protocols to maximize the network lifetime is the difficulty and sometime impossibility to replace or recharge the batteries of wireless sensor nodes especially in the large WSN and hostile environment. 
 
-\indent The authors have defined the network lifetime in different contexts and use it as a metric to evaluate the performance of their protocols. Based on the previous proposed works in prolonging the network lifetime, Various definitions are exist for the lifetime of a sensor network~\cite{ref92,ref93} such as:~\textbf{(i)} is the time spent by WSN until the death of the first wireless sensor node ( or cluster head ) in the network due to its energy depletion.~\textbf{(ii)} is the time spent by WSN and has at least a specific set $\beta$ of alive sensor nodes in WSN.~\textbf{(iii)} is the time spent by WSN until the death of all wireless sensor nodes in WSN because they have been depleted their energy.~\textbf{(iv)} for  k-coverage is the time spent by WSN in covering the area of interest by at least $k$ sensor nodes.~\textbf{(v)} for 100 $\%$ coverage is the time spent by WSN in covering each target or the whole area by at least one sensor node.~\textbf{(vi)} for $\alpha$-coverage: the total time by which at least $\alpha$ part of the sensing field is covered by at least one node; or is the time spent by WSN until the coverage ratio becomes less than a predetermined threshold $\alpha$.~\textbf{(vii)} the working time spent by the system before either the coverage ratio or delivery ratio become less than a predetermined threshold.~\textbf{(viii)} the number of the successful data gathering trips.~\textbf{(ix)} the number of sent packets.~\textbf{(x)} the percentage of wireless sensor nodes that have a route to the sink.~\textbf{(xi)} the prediction of the total period of time during which the probability of ensuring the connectivity and k-coverage concurrently is at least $\alpha$.~\textbf{(xii)} the time spent by WSN until loosing the connectivity or the coverage.~\textbf{(xiii)} the time spent by WSN until acceptable event detection ratio is not acceptable in the network.~\textbf{(xiv)} the time spent by WSN and the application requirement has been met.
+\indent The authors have defined the network lifetime in different contexts and use it as a metric to evaluate the performance of their protocols. Based on the previous proposed works in prolonging the network lifetime, Various definitions are exist for the lifetime of a sensor network~\cite{ref92,ref93} such as: 
+%~\textbf{(i)}
+\begin{enumerate} [i.]
+
+\item The time spent by WSN until the death of the first wireless sensor node ( or cluster head ) in the network due to its energy depletion~\cite{ref162,ref163}.
+\item The time spent by WSN and has at least a specific set $\beta$ of alive sensor nodes in WSN~\cite{ref164,ref165}.
+\item The time spent by WSN until the death of all wireless sensor nodes in WSN because they have been depleted their energy~\cite{ref166}.
+\item For  k-coverage, is the time spent by WSN in covering the area of interest by at least $k$ sensor nodes~\cite{DESK}.
+\item For 100 $\%$ coverage is the time spent by WSN in covering each target or the whole area by at least one sensor node~\cite{ref167}.
+\item For $\alpha$-coverage: the total time by which at least $\alpha$ part of the sensing field is covered by at least one node~\cite{ref168}; or is the time spent by WSN until the coverage ratio becomes less than a predetermined threshold $\alpha$~\cite{ref169}.
+\item The working time spent by the system before either the coverage ratio or delivery ratio become less than a predetermined threshold~\cite{ref170}.
+\item The number of the successful data gathering trips~\cite{ref173}.
+\item The number of sent packets~\cite{ref174}.
+\item The percentage of wireless sensor nodes that have a route to the sink~\cite{ref170}.
+\item The prediction of the total period of time during which the probability of ensuring the connectivity and k-coverage concurrently is at least $\alpha$~\cite{ref175}.
+\item The time spent by WSN until loosing the connectivity or the coverage~\cite{ref171}.
+\item The time spent by WSN until acceptable event detection ratio is not acceptable in the network~\cite{ref166}.
+\item The time during which the application requirement is satisfied~\cite{ref172}.
+\end{enumerate}
 
 \indent According to the above definitions for network lifetime, there is no universal definition reflects the requirements of each application and the effects of the environment. In real WSN, the network lifetime reflects a set of a particular circumstances of the environment. Accordingly, the current definitions are applicable for the WSNs that meet a particular conditions. However, many more parameters, which are affecting on the network lifetime of WSN such as~\cite{ref92}:  heterogeneity, node mobility, topology changes, application characteristics, quality of service, and completeness.
 
@@ -336,10 +354,9 @@ The network lifetime has been defined in this dissertation as the time spent by
 
 \section{Coverage in Wireless Sensor Networks } 
 \label{ch1:sec:8}
-%\indent Energy efficiency is  a crucial issue in wireless sensor networks since sensory consumption, in  order to  maximize the network lifetime, represents  the major difficulty when designing WSNs. As a consequence, one of the scientific research challenges in  WSNs, which has  been addressed by a large amount of literature during the  last few  years, is  the design of  energy efficient  approaches for coverage and connectivity~\cite{ref94,ref101}. 
-Coverage reflects how well a sensor  field is  monitored. On the one hand, we want to monitor the  area of
-interest in the most efficient way~\cite{ref95}.  On the other hand, we want to
-use few energy as possible. Sensor nodes are battery-powered  with no
+%\indent Energy efficiency is  a crucial issue in wireless sensor networks since sensory consumption, in  order to  maximize the network lifetime, represents  the major difficulty when designing WSNs. As a consequence, 
+
+One of the scientific research challenges in  WSNs, which has  been addressed by a large amount of literature during the  last few  years, is  the design of  energy efficient  approaches for coverage and connectivity~\cite{ref94,ref101}. Coverage reflects how well a sensor field is  monitored. On the one hand, we want to monitor the  area of interest in the most efficient way~\cite{ref95}.  On the other hand, we want to use few energy as possible. Sensor nodes are battery-powered  with no
 mean  of recharging  or replacing,  usually  due to  environmental (hostile  or
 unpractical environments)  or cost reasons.   Therefore, it is desired  that the
 WSNs are deployed  with high densities so as to  exploit the overlapping sensing
@@ -398,7 +415,7 @@ The coverage protocols proposed in this dissertation use the binary disc sensing
 \begin{enumerate}[(i)]
 \item $\textbf{Coverage Type}$ refers to determining what is it exactly that you are trying to cover. Typically, it may be required to monitor a whole area, observe a set of targets, or look for a breach among a barrier. 
   
-\item $\textbf{Deployment Method}$ refers to the way by which the wireless sensor nodes are deployed over the target sensing field in order to build the wireless sensor network. Generally, the sensor nodes can be placed either deterministically or randomly in the target sensing field so as to construct the wireless sensor network~\cite{ref107}. The method of placement the sensor nodes can be selected based on the type of sensors, application, and the environment. In the deterministic placing, the deployment can be achieved in case of small number of sensor nodes and in friendly environment, whilst for a large number of sensor nodes or where the area of interest is inaccessible or hostile, a random placing is the choice. The sensor network can be either dense or sparse. The dense deployment is preferred when the robust security is important or when it is required that the area is covered by more than one sensor node. On the other hand, the sparse deployment is used when the dense deployment is expensive or when the maximum coverage is performed by a less number of sensor nodes.
+\item $\textbf{Deployment Method}$ refers to the way by which the wireless sensor nodes are deployed over the target sensing field in order to build the wireless sensor network. Generally, the sensor nodes can be placed either deterministically or randomly in the target sensing field so as to construct the wireless sensor network~\cite{ref107}. The method of placement the sensor nodes can be selected based on the type of sensors, application, and the environment. In the deterministic placing, the deployment can be achieved in case of small number of sensor nodes and in friendly environment, whilst for a large number of sensor nodes or where the area of interest is inaccessible or hostile, a random placing is the choice. The sensor network can be either dense or sparse. The dense deployment is preferable when it is necessary to provide a security robustness in WSNs. On the other hand, the sparse deployment is used when the dense deployment is expensive or when the maximum coverage is performed by a less number of sensor nodes.
  
 \item $\textbf{Coverage Degree}$ refers to how many sensor nodes are required to cover a target or an area. This K-coverage mean the point in the sensing field is covered by at least K sensor nodes. Some applications need a high reliability to achieve their tasks, therefore, the sensing field is deployed densely so as to perform a K-coverage for this field. The simple coverage problem consists of a coverage degree equal to one (i.e., K=1), where every point in the sensing field is covered by at least one sensor.
 
