X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/177bdfaea3e016d6f01f2ed9fcf1c95ebb4dac0e..e2fea957f33c77ba4585e64d92c0c22d80a90d9a:/CHAPITRE_01.tex?ds=inline

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index 46f24af..a49ca9d 100644
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@@ -288,7 +288,7 @@ The majority of synchronous schemes work in periodic (cyclic) way by preparing t
 
 \item \textbf{Asynchronous Schemes:} 
 %The time among the wireless sensor nodes does not need synchronization. 
-The wireless sensor node wakes up to send packets without taking into account whether the receiving sensor nodes are waked up and ready to receive. These schemes do not need time synchronization which consumes energy~\cite{ref74}. They do not need to exploit the limited resources (processing, memory, and radio) of the sensor nodes because there are no  shared wake up schedules to be exchanged or saved in the memory. Therefore,  exchanging the packets among the wireless sensor nodes, which are not aware of each other's wake-up schedules, is have considered as a major challenge in asynchronous schemes. These schemes can been categorized into three groups~\cite{ref57}: 
+The wireless sensor node wakes up to send packets without taking into account whether the receiving sensor nodes are waked up and ready to receive. These schemes do not need time synchronization which consumes energy~\cite{ref74}. They do not need to exploit the limited resources (processing, memory, and radio) of the sensor nodes because there are no  shared wake up schedules to be exchanged or saved in the memory. Therefore,  exchanging the packets among the wireless sensor nodes, which are not aware of each other's wake-up schedules, is a major challenge in asynchronous schemes. These schemes can been categorized into three groups~\cite{ref57}: 
  
 \begin{enumerate} [(A)]
 \item Transmitter-initiated: a special frame is sent by the transmitting sensor node to inform the receiving sensor node that it has a data frame to send.  If the receiving sensor node is hearing the special frame during one of its wake up intervals, the receiving node waits for sending the data frame by sender to receive it. The major advantage of these schemes is the low memory and processing requirements whilst the major disadvantages are low-duty-cycle and the non-deterministic sleep latency. 
@@ -330,7 +330,8 @@ Data driven schemes are classified into two main approaches~\cite{ref59,ref22}.
  
 %\begin{enumerate} [(I)]
 \subsubsection{Data Reduction Schemes} 
-Data reduction schemes deal with reducing the amount of data to be transmitted to a sink. They can be divided into stochastic approaches, time series forecasting, and algorithmic approaches. In stochastic approaches, physical phenomena are transformed using stochastic characterization. The aggregation by these protocols requires high processing. Therefore, it is feasible only on powerful sensor nodes with a big battery. In time series forecasting, the old values of periodic sampling can be used to forecast a future value in the same series. In algorithmic approaches, sensed phenomena is described using heuristic or state transition model. 
+Data reduction schemes deal with reducing the amount of data to be transmitted to a sink. They can be divided into stochastic approaches, time series forecasting, and algorithmic approaches. In stochastic approaches, physical phenomena are transformed using stochastic characterization. The aggregation by these protocols requires high processing. Therefore, it is feasible only on powerful sensor nodes with a big battery. In time series forecasting, the old values of periodic sampling can be used to forecast a future value in the same series. 
+%In algorithmic approaches, sensed phenomena is described using heuristic or state transition model. 
 
 \subsubsection{Energy Efficient Data Acquisition Schemes} 
 They concentrate on the energy consumption reduction in the sensing unit. These schemes are divided into adaptive sampling, hierarchical sampling, and model-based active sampling. In adaptive sampling, the amount of data acquired from the transducer can be reduced by spatial or temporal correlation between data. These approaches are more efficient to be used in centralized fusion, but they consume more energy due to requiring a high processing. Hierarchical sampling is more efficient when there are different types of sensors installed on the nodes. These approaches are more energy efficient and application specific. The model-based approaches are similar to data prediction schemes. These approaches aim to decrease the data samples by using computed models and to conserve the energy by means of data acquisition. 
@@ -340,9 +341,12 @@ They concentrate on the energy consumption reduction in the sensing unit. These
 
 \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. These energy sources are 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}. 
+\begin{enumerate} [i)]
+\item{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. These energy sources are 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}. 
 
-\subsubsection{Wireless Charging}In wireless charging, the power can be transmitted between the devices without requiring a connection between the transmitter and the receiver. These techniques participate in increasing the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed in two ways: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.
+\item{Wireless Charging}In wireless charging, the power can be transmitted between the devices without requiring a connection between the transmitter and the receiver. These techniques participate in increasing the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed in two ways: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.
+
+\end{enumerate}
 
 \subsection{Radio Optimization}
 
@@ -356,7 +360,7 @@ direction; and cognitive radio and cooperative communications schemes~\cite{ref2
 In WSN, some wireless sensor nodes in a certain region may die and this creates a hole in the WSN. This problem can be solved by placing the wireless sensor nodes in sensing field by using an optimal distribution or by deploying a small number of relay wireless sensor nodes with powerful capabilities. The major goal of relay nodes is the communication with other wireless sensor nodes or relay nodes~\cite{ref52}. This solution can enhance the power balancing and avoid overloaded wireless sensor nodes in a particular region of a WSN.
 
 \subsubsection{Sink Mobility} 
-In WSNs including a static sink, the wireless sensor nodes which are near the sink drain their power more rapidly compared with other sensor nodes, and this leads to WSN disconnection and limited network lifetime~\cite{ref53}. Sending all the data to the sink maximizes the overload on the sensor nodes near to the sink. In order to overcome this problem and prolong the network lifetime, a solution is to use a mobile sink moving within the area of interest so as to collect the sensory data from the static sensor nodes over a single hop communication.  A mobile sink avoids the multi-hop communication and conserves the energy at the static sensor nodes near to the base station, extending the lifetime of WSN~\cite{ref54,ref55}.
+In WSNs including a static sink, the wireless sensor nodes which are near the sink drain their power more rapidly compared with other sensor nodes, and this leads to WSN disconnection and limited network lifetime~\cite{ref53}. Sending all the data to the sink maximizes the overload on the sensor nodes near to the sink. In order to overcome this problem and prolong the network lifetime, we can use a mobile sink which moves within the area of interest to collect the sensory data from the static sensor nodes over a single hop communication.  A mobile sink avoids the multi-hop communication and conserves the energy at the static sensor nodes near to the base station, extending the lifetime of WSN~\cite{ref54,ref55}.
 
  
 
@@ -409,7 +413,7 @@ A major research challenge in  WSNs, which has  been addressed by a large amount
 \item \textbf{Barrier coverage}~\cite{ref99,ref100} where the main goal is to detect targets as they cross a barrier, which is usually a long belt region such as one can be found in intrusion detection and border surveillance applications.  
 \end{enumerate}
 
-\indent The sensing quality and capability can be assessed by a sensing coverage model of trained through the identification of a mathematical relationship between the point and the sensor node in the sensing field. In the real world, there are sometimes obstacles in the environment that affect the sensing range \cite{ref104}. Therefore, several sensing coverage models have been suggested according to application requirements and physical working environment such as~\cite{ref103}: boolean sector coverage, boolean disk coverage, attenuated disk coverage, truncated attenuated disk, detection coverage, and estimation coverage Models. However, two main sensing coverage models have been used for simulating the performance of wireless sensors~\cite{ref104,ref105,ref106}:
+\indent The sensing quality and capability can be assessed by a sensing coverage model obtained through the identification of a mathematical relationship between the point and the sensor node in the sensing field. In the real world, there are sometimes obstacles in the environment that affect the sensing range \cite{ref104}. Therefore, several sensing coverage models have been suggested according to application requirements and physical working environment such as~\cite{ref103}: boolean sector coverage, boolean disk coverage, attenuated disk coverage, truncated attenuated disk, detection coverage, and estimation coverage models. However, two main sensing coverage models have been used for simulating the performance of wireless sensors~\cite{ref104,ref105,ref106}:
 
 \begin{enumerate}[(A)] 
 \item \textbf{Binary Disc Sensing Model:}
@@ -475,7 +479,7 @@ This dissertation deals with activity based scheduling to ensure the best covera
 
 \end{enumerate}
 
-\section{Energy Consumption Modeling} 
+\section{Energy Consumption Model} 
 \label{ch1:sec:9}
 %\indent The WSNs have been received a lot of interests because the low energy consumption of the sensor nodes.  
 %One of the most critical issues in WSNs is to reduce the energy consumption of the limited power battery of the sensor nodes so as to prolong the network lifetime as long as possible. 
@@ -543,6 +547,6 @@ In this dissertation, we developed another energy consumption model that based o
 \section{Conclusion}
 \label{ch1:sec:10}
 
-\indent In this chapter an overview of the wireless sensor networks has been presented. Unlike traditional ad-hoc networks, WSNs are collaborative and very oriented toward a specific application domain. The structure of the typical wireless sensor network and the main components of the sensor nodes have been detailed. Several types of wireless sensor networks are described. Various fields of applications covering a wide spectrum including health, home, environmental, military, and industrial applications have been presented.  As shown, since sensor nodes have limited battery life; since it is impossible to replace batteries, especially in remote and hostile environments; the limited power of a battery represents the critical challenge in WSNs. The main challenges in WSNs have been explained. Energy efficiency is the primary challenge to increase the network lifetime. Therefore, energy efficient solutions have been proposed in order to handle that challenge. Many energy efficient mechanisms have been illustrated, which are aimed to reduce the energy consumption of the different parts of the wireless sensor nodes. The definition of the network lifetime has been presented in different contexts.  The problem of the coverage in WSNs is also explained.  
+\indent In this chapter, an overview of the wireless sensor networks has been presented. Unlike traditional ad-hoc networks, WSNs are collaborative and very oriented toward a specific application domain. The structure of the typical wireless sensor network and the main components of the sensor nodes have been detailed. Several types of wireless sensor networks are described. Various fields of applications covering a wide spectrum including health, home, environmental, military, and industrial applications have been presented.  As shown, since sensor nodes have limited battery life; since it is impossible to replace batteries, especially in remote and hostile environments; the limited power of a battery represents the critical challenge in WSNs. The main challenges in WSNs have been explained. Energy efficiency is the primary challenge to increase the network lifetime. Therefore, energy efficient solutions have been proposed in order to handle that challenge. Many energy efficient mechanisms have been illustrated, which are aimed to reduce the energy consumption of the different parts of the wireless sensor nodes. The definition of the network lifetime has been presented in different contexts.  The problem of the coverage in WSNs is also explained.  
 %One of the main scientific research challenges in WSNs is how to build energy efficient coverage protocols. 
 This chapter highlights the main design issues that need to be considered when designing an energy efficient coverage protocol for WSNs. In addition, energy consumption models have been discussed. 
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