\begin{figure}[h!]
\centering
-\includegraphics[scale=0.9]{Figures/ch1/wsn.jpg}
+\includegraphics[scale=1.2]{Figures/ch1/wsn.jpg}
\caption{ Wireless sensor network architecture.}
\label{wsn}
\end{figure}
\item \textbf{Topology Control:} The maintenance and repair of the network topology is a challenging task due to the large number of inaccessible sensor nodes that are prone to failure. Therefore, some schemes need to be used to deal with the dynamic changing of topology and the failure of some sensor nodes due to energy depletion or malfunction.
-\item \textbf{Heterogeneity:} One essential challenge is to provide a WSN protocol that deals with different sensor node capabilities such as communication, processing, sensing, and energy. The future of WSNs will be heterogeneous with a large number of sensor nodes. These WSNs may reflect different tasks and can be integrated into one big network. Therefore, it is necessary to take the heterogeneity into consideration during the design stage of WSNs protocols.
+\item \textbf{Heterogeneity:} One essential challenge is to provide a WSN protocol that deals with different sensor node capabilities such as communication, processing, sensing, and energy. The future networks will be heterogeneous with a large number of sensor nodes. These WSNs may reflect different tasks and can be integrated into one big network. Therefore, it is necessary to take the heterogeneity into consideration during the design stage of WSNs protocols.
\item \textbf{Wireless Networking:} The networking and wireless communication represent another important challenge in WSNs. The communication range of the signals can be attenuated or faded during the signal propagation across the communication media or during passing through obstacles. The increasing distance between the sensor nodes and the sink requires increased transmission power. However, the long distance can be divided into several small distances using multi-hop communication. The multi-hop communication generates another challenge that is how to find the more energy efficient route to transmit the information from the source to the destination. The sensor nodes should cooperate to find this route and to serve as relays.
\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 have considered as 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, it waits for sending the data frame. The major advantage of these schemes is the low requirement of the memory and processing whilst the major disadvantages are low-duty-cycle and the non-deterministic sleep latency.
+\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 to receive it. The major advantage of these schemes is the low requirement of the memory and processing whilst the major disadvantages are low-duty-cycle and the non-deterministic sleep latency.
%Examples on these schemes in~\cite{ref75,ref76}.
-\item Receiver-initiated: during each wake-up time interval of receiving wireless sensor node, it sends a special frame to inform the senders, which are waked up and ready to receive the data frames. When the sensor node has a packet to send, it wakes up and wait receiving special frame from neighboring sensor nodes. The waiting sensor node is sending its data frame at the moment of receiving the special frame from the neighbor sensor node. The main advantages are a low processing and storage requirement whilst the disadvantage of these schemes are the low performance during low and high duty cycle, as well as their stochastic sleep latency.
+\item Receiver-initiated: during each wake-up time interval of receiving wireless sensor node, it sends a special frame to inform the senders, which of senders are waked up and ready to receive the data frames. When the sensor node has a packet to send, it wakes up and wait receiving special frame from neighboring sensor nodes. The waiting sensor node is sending its data frame at the moment of receiving the special frame from the neighbor sensor node. The main advantages are a low processing and storage requirement whilst the disadvantage of these schemes are the low performance during low and high duty cycle, as well as their stochastic sleep latency.
%The works that proposed in~\cite{ref77,ref78} represents some instances for these approaches.
\item Combinatorial or random: during the wake-up duration, one or more data packets can be exchanged among the wireless sensor nodes. The exchanged data packets among the wireless sensor nodes are increased as the wake-up time increased. In these schemes, the special frames are removed and the energy consumption is decreased. A common disadvantage of these schemes is the non-deterministic sleep
\subsection{Data-Driven Schemes}
-\indent Data-driven approaches aim to decrease the amount of data sent to the sink whilst maintaining the accuracy of sensing within an acceptable level. Therefore, removing unwanted data during the transmission and restriction the sensing tasks during data acquisition can be participating in reducing the energy consumption in WSNs.
+\indent Data-driven approaches aim to decrease the amount of data sent to the sink whilst maintaining the accuracy of sensing within an acceptable level. Therefore, removing unwanted data during the transmission and restricting the sensing tasks during data acquisition can be participating in reducing the energy consumption in WSNs.
%Several data-driven schemes have been proposed in~\cite{ref86,ref87,ref88,ref89,ref90}.
Data driven schemes are classified into two main approaches~\cite{ref59,ref22}:
\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 to reflect 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.
+\indent According to the above definitions for network lifetime, there is no universal definition to reflect 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.
+Many parameters are affecting the network lifetime should be taken into account~\cite{ref92}: heterogeneity, node mobility, topology changes, application characteristics, quality of service, and completeness.
The network lifetime has been defined in this dissertation as the time spent by WSN until the coverage ratio becomes less than a predetermined threshold $\alpha$.
\begin{enumerate}[(i)]
\item \textbf{Area coverage}~\cite{ref97,ref101} where every point inside an area has to be monitored. The work in this dissertation deals with this type of coverage.
\item \textbf{Target coverage}~\cite{ref98,ref101} where the main objective is to cover only a finite number of discrete points called targets.
-\item \textbf{Barrier coverage}~\cite{ref99,ref100} to prevent intruders from entering into the region of interest.
+\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 intrusion detection and border surveillance applications.
\end{enumerate}
\indent The sensing quality and capability can be assessed by a sensing coverage models due to discovering the 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}:
\end{enumerate}
-The coverage protocols have proposed in this dissertation use the binary disc sensing model as a sensing coverage model for each wireless sensor node in WSN.
+The coverage protocols proposed in this dissertation use the binary disc sensing model as a sensing coverage model for each wireless sensor node in WSN because it is a widely used in the literatures, as well as it is easy to formulate the linear programs with it. While the probabilistic model is more complex and it is difficult to use it to create integer programs.
+
+
+%The coverage protocols have proposed in this dissertation use the binary disc sensing model as a sensing coverage model for each wireless sensor node in WSN.
\indent Several design issues should be considered in order to produce solutions for the coverage problems in WSNs. These design issues can be classified into~\cite{ref103}:
\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 of a barrier.
+\item $\textbf{Coverage Type}$ Is the WSN dedicated to monitor a whole area, to observe a set of targets, or to look for a breach of 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 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 means 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 with at least one sensor.
+\item $\textbf{Coverage Degree}$ refers to how many sensor nodes are required to cover a target or an area. A point in the sensing field is said to be K-coverage 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.
\item $\textbf{Coverage Ratio}$ is the percentage of the area of sensing field that fulfills the coverage degree of the application. If all the points in the sensing field are covered, the coverage ratio is $100\%$ and it can be called a complete coverage. Otherwise, it can be called as partial coverage.
\item $\textbf{Activity based Scheduling}$ is to schedule the activation and deactivation of sensor nodes during the network lifetime. The basic objective is to decide which sensors are in what states (active or sleeping mode) and for how long, so that the application coverage requirement can be guaranteed and the network lifetime can be prolonged. Various centralized, distributed, and localized approaches have been proposed for activity scheduling. In distributed algorithms, each node in the network autonomously makes decisions on whether to turn on or turn off itself only using local neighbor information. In centralized algorithms, a central controller (a node or base station) informs every sensor of the time intervals to be activated.
+This dissertation deals with activity based scheduling to ensure the best coverage in WSNs.
\end{enumerate}
\section{Energy Consumption Modeling}
\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. In order to model the energy consumption, four states for a sensor node are used~\cite{ref140}: transmission, reception, listening, and sleeping. In addition, two states should be taken into account: computation and sensed data acquisition. The main tasks of each of these states include:
+%\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. In order to model the energy consumption, four states for a sensor node are used~\cite{ref140}: transmission, reception, listening, and sleeping. In addition, two states should be taken into account: computation and sensed data acquisition. The main tasks of each of these states include:
\begin{enumerate}[(i)]
-\item Computation: processing needed for clustering and executing any algorithm inside the sensor node. The processing that required to physical communication and networking protocols is included in reception and transmission.
+\item Computation: processing needed for executing any algorithm inside the sensor node. The processing that is required to physical communication and networking protocols is included in reception and transmission.
\item Transmission: processing for address determination, packetization, encoding, framing, and maybe queuing; supply for the baseband and RF circuitry.
%\subsection{Radio Energy Dissipation Model:}
%\label{ch1:sec9:subsec1}
-\indent Since the communication unit is the most energy-consuming part of the sensor node; therefore, many authors are used the radio energy dissipation model that proposed in~\cite{ref109,ref110} as energy consumption model during the simulation and evaluation of their works in WSNs. Figure~\ref{RDM} shows the radio energy dissipation model.
+\indent The communication unit is the most energy-consuming part of the sensor node; many authors use the radio energy dissipation model proposed in~\cite{ref109,ref110} as energy consumption model. Figure~\ref{RDM} shows the radio energy dissipation model.
\begin{figure}[h!]
\centering
\includegraphics[scale=0.4]{Figures/ch1/RDM.eps}
\label{RDM}
\end{figure}
-\indent In this model, the radio consumes an energy to execute the transmitter and the power amplifier. The receiver circuitry consumes an energy to run the radio electronics, as described in figure~\ref{RDM}. The channel model can be either free space ($d^2$ power loss) or multipath fading ($d^4$ power loss), based on the distance between the transmitter and receiver. This power loss can be controlled by setting the power amplifier so that if the distance is less than a threshold, the free space ($\varepsilon_{fs}$) model is used (i.e., $\varepsilon_{amp}$ = $\varepsilon_{fs}$); Otherwise, the multipath ($\varepsilon_{mp}$) model is used (i.e., $\varepsilon_{amp}$ = $\varepsilon_{mp}$). Therefore, to transmit a k-bit packet with a distance d, the radio expends
+\indent In this model, the radio consumes energy to execute the transmitter and the power amplifier. The receiver circuitry consumes energy to run the radio electronics, as described in figure~\ref{RDM}. The channel model can be either free space ($d^2$ power loss) or multipath fading ($d^4$ power loss), based on the distance between the transmitter and receiver. This power loss can be controlled by setting the power amplifier so that if the distance is less than a threshold ($d_0$), the free space ($\varepsilon_{fs}$) model is used (i.e., $\varepsilon_{amp}$ = $\varepsilon_{fs}$); Otherwise, the multipath ($\varepsilon_{mp}$) model is used (i.e., $\varepsilon_{amp}$ = $\varepsilon_{mp}$). Therefore, to transmit a K-bit packet with a distance d, the radio expends
\begin{equation}
-E_{tx}\left(k,d \right) = \left \{
+E_{tx}\left(K,d \right) = \left \{
\begin{array}{l l}
- \emph{ kE_{elec} + k \varepsilon_{fs}d^2} & \mbox{if $d < d_0$} \\
- \emph{ kE_{elec} + k \varepsilon_{mp}d^4} & \mbox{if $d \geqslant d_0$}\\
+ \emph{ KE_{elec} + K \varepsilon_{fs}d^2} & \mbox{if $d < d_0$} \\
+ \emph{ KE_{elec} + K \varepsilon_{mp}d^4} & \mbox{if $d \geqslant d_0$}\\
\end{array} \right.
\label{eq3-ch1}
\end{equation}
-As well as to receive a k-bit packet, the radio expends
+As well as to receive a K-bit packet, the radio expends
\begin{equation}
- E_{rx}\left(k,d \right) = \emph{ kE_{elec} }.
+ E_{rx}\left(k,d \right) = \emph{ KE_{elec} }.
\label{eq4-ch1}
\end{equation}
The typical parameters are set as: $E_{elec}$ = 50 nJ/bit, $\varepsilon_{fs}$ = 10 pJ/bit/$m^2$, $\varepsilon_{mp}$ = 0.0013 pJ/bit/$m^4$. In addition, the energy for data aggregation is set as $E_{DA}$ = 5 nJ/bit.
-\indent The radio energy dissipation model have been considered only the energy, which is consumed by the communication part of the sensor node. However, in order to achieve a more accurate model, it is necessary to take into account the energy is consumed by the other parts inside the sensor node such as computation unit and sensing unit.
-
+\indent The radio energy dissipation model considers only the energy consumed by the communication part of the sensor node. However, in order to achieve a more accurate model, it is necessary to take into account the energy consumed by other parts inside the sensor node such as computation unit and sensing unit.
+In this dissertation, we developed another energy consumption model that proposed by~\cite{DESK} and based on \cite{ref112}.
%\subsection{Our Energy Consumption Model:}
%\label{ch1:sec9:subsec2}
+
\section{Conclusion}
\label{ch1:sec:10}
-\indent In this chapter, an overview of the wireless sensor networks have been presented that shows our focus in this dissertation. The structure of the typical wireless sensor network and the main components of the sensor nodes have been demonstrated. Several types of wireless sensor networks are described. Various fields of applications covering a wide spectrum for a WSNs including health, home, environmental, military, and industrial applications have been presented. As demonstrated, 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 explained; on the other hand, the energy efficient solutions have proposed in order to handle these challenges. Many energy efficient mechanisms have been illustrated, which are aimed to reduce the energy consumption by the different units of the wireless sensor nodes in WSNs. The definition of the network lifetime has been presented and in different contexts. The problem of the coverage is explained, where constructing energy efficient coverage protocols one of the main scientific research challenges in WSNs. This chapter highlights the main design issues for the coverage problems that need to be considered in designing a coverage protocol for WSNs. In addition, the energy consumption Modeling have been demonstrated.
+\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 for a WSNs 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 explained. Energy efficiency is the primary challenge to increase the network lifetime. Therefore, the energy efficient solutions have 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 in WSNs. The definition of the network lifetime has been presented and in different contexts. The problem of the coverage in WSNs is 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 for the coverage problems that need to be considered in designing a coverage protocol for WSNs. In addition, the energy consumption modeling have been illustrated.
\ No newline at end of file
