\section{Types of Wireless Sensor Networks}
\label{ch1:sec:03}
-According to the physical phenomena for which the WSN is developed, several WSNs are deployed on the ground, underground and underwater, which suffer from different conditions and challenges. WSNs can be classified into six types, where five types of them presented in~\cite{ref4,ref5} and we added the sixth type. Figure~\ref{wsnt} gives an examples for WSNs types.
+According to the physical phenomena for which the WSN is developed, several WSNs are deployed on the ground, underground and underwater, which suffer from different conditions and challenges. WSNs can be classified into six types, where five types of them presented in~\cite{ref4,ref5} and we added the sixth type. This dissertation is used the terrestrial WSN. Figure~\ref{wsnt} gives an examples for WSNs types.
\begin{figure}[h!]
\centering
\includegraphics[scale=0.5]{Figures/ch1/typesWSN.pdf}
\section{Wireless Sensor Network Applications}
\label{ch1:sec:04}
-The high development in WSNs led to extensive study on different characteristics of it. However, the WSN has been applied with concentrating on various applications. In this section, we demonstrated a different academic and commercial applications that developed for WSNs. The WSN composed of various types of sensors such as~\cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar, which are capable of observing a different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, the presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. So, There are a wide range of WSN applications and these applications can be classified into five classes~\cite{ref22}. Figure~\ref{WSNAP} shows classification of WSN applications.
+The fast development in WSNs has been led to extensive study on different characteristics of it. However, the WSN has been applied with concentrating on various applications. In this section, we demonstrated a different academic and commercial applications that developed for WSNs. The WSN composed of various types of sensors such as~\cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar, which are capable of observing a different physical conditions such as: temperature, humidity, pressure, speed, direction, movement, light, soil makeup, noise levels, the presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. So, There are a wide range of WSN applications and these applications can be classified into five classes~\cite{ref22}. Figure~\ref{WSNAP} shows classification of WSN applications.
\begin{figure}[h!]
\centering
Various WSN applications for environmental monitoring have been used in coastline erosion, air quality monitoring, safe drinking water and contamination control~\cite{ref22}.
\item \textbf{Public safety and military systems Applications}
-The WSNs can be incorporated into military command, control, communications, computing, intelligence,
+The WSNs can be incorporated into military command, control, communications, computing, intelligence,
surveillance, reconnaissance, and targeting systems. It estimates the unpredictable events such as natural disasters and threats as well as some of the military WSN applications keep under surveillance friendly forces, equipment, and ammunition; battlefield surveillance; reconnaissance of opposing forces and terrain; targeting; battle damage assessment; and nuclear, biological, and chemical (NBC) attack detection and reconnaissance~\cite{ref19}. According to figure~\ref{WSNAP}, the public safety and military applications are categorized into active intervention and passive supervision~\cite{ref22}. In active intervention systems, the wireless sensors are portable with the agents and is devoted to the security of the team activities. During the work of the team, the leader will monitor the agents situation and the environmental impact factors. The main applications includes: emergency rescue teams, miners and soldiers. In passive supervision systems, the wireless static sensors are scattered over a large field for monitoring a civil area or nuclear site for a longer time. These applications includes: surveillance and target tracking , emergency navigation, fire detection in a building, structural health monitoring and natural disaster prevention such as in the case of tsunamis, eruptions or flooding.
\item \textbf{Transportation systems Applications}
\section{The Main Challenges in Wireless Sensor Networks}
\label{ch1:sec:05}
-There are many challenges need to be faced in WSNs, which are received increasing attention by a large number of researchers during the last few years. These challenges were the reason in proposing different solutions so as to face these challenges (see section~\ref{ch1:sec:06}).
+There are many challenges need to be faced in WSNs, which are received increasing attention by a large number of researchers during the last few years. These challenges were the reason in proposing different solutions so as to face these challenges as will be explained in next section~\ref{ch1:sec:06}.
\begin{enumerate} [(I)]
\item \textbf{Extended Network Lifetime:} one fundamental issue in WSNs is how to prolong the network lifetime as long as possible. Since sensor battery has a limited power; and since it is difficult to recharge or replace it especially in remote or hostile environment; It is necessary to reduce the energy consumption by using energy-efficient methods so as to extend the network lifetime.
The energy limited nature of wireless sensor nodes need to use energy efficient mechanisms to prolong network lifetime. The energy efficient mechanisms can be classified into five categories~\cite{ref22}. Figure~\ref{emwsn} summarizes the energy-efficient mechanisms in WSNs.
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.5]{Figures/ch1/WSN-M.pdf}
+\includegraphics[scale=0.4]{Figures/ch1/WSN-M.eps}
\caption{Energy-Efficient Mechanisms in Wireless Sensor Networks}
\label{emwsn}
\end{figure}
\begin{enumerate} [(I)]
-\item \textbf{Cluster architectures:} in this strategy, the wireless sensor nodes are grouped into several groups that called clusters, each group of wireless sensor nodes are managed by a single sensor node, which is called cluster head. The cluster head takes the responsibility of manging the activities of the wireless sensor nodes with the cluster and it communicates and coordinates with other cluster heads or the base station in the WSN. This mechanism conserves the energy in WSNs by means of~\cite{ref43,ref22}:
+\item \textbf{Energy as a routing metric:} lifetime maximization can be achieved by using the residual power of wireless sensor node as a routing metric and take it into account during executing the routing protocol in WSNs. So, the routing protocols should concentrate on the remaining power of sensor nodes during taking the decision to select the next hop toward the destination and not depend on the shortest path solution. It prioritizes routes on the basis of an energy metric (sometimes with other routing metrics) so it is called energy-aware routing protocols~\cite{ref45,ref46}.
+
+\item \textbf{Multipath routing:} efficient strategy that can provides reliability, security and load balancing in order to forward packets in a limited energy and constrained resources(computation, communication, and storage) networks like WSNs~\cite{ref50}. The single path routing is simple and scalable but it is not efficient for energy constrained networks such as WSNs . There are many multipath routing protocol are summarized in~\cite{ref50,ref51}.
+
+\end{enumerate}
+
+
+\subsection{Cluster architectures}
+In this strategy, the wireless sensor nodes are grouped into several groups that called clusters, each group of wireless sensor nodes are managed by a single sensor node, which is called cluster head. The cluster head takes the responsibility of manging the activities of the wireless sensor nodes with the cluster and it communicates and coordinates with other cluster heads or the base station in the WSN. This mechanism conserves the energy in WSNs by means of~\cite{ref43,ref22}:
+
\begin{enumerate}[(a)]
\item Grouping the wireless sensor nodes into clusters 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.
\end{enumerate}
In addition, the clustering supports network scalability in WSNs~\cite{ref43,ref44}.
-\item \textbf{Energy as a routing metric:} lifetime maximization can be achieved by using the residual power of wireless sensor node as a routing metric and take it into account during executing the routing protocol in WSNs. So, the routing protocols should concentrate on the remaining power of sensor nodes during taking the decision to select the next hop toward the destination and not depend on the shortest path solution. It prioritizes routes on the basis of an energy metric (sometimes with other routing metrics) so it is called energy-aware routing protocols~\cite{ref45,ref46}.
-
-\item \textbf{Multipath routing:} efficient strategy that can provides reliability, security and load balancing in order to forward packets in a limited energy and constrained resources(computation, communication, and storage) networks like WSNs~\cite{ref50}. The single path routing is simple and scalable but it is not efficient for energy constrained networks such as WSNs . There are many multipath routing protocol are summarized in~\cite{ref50,ref51}.
-
-\item \textbf{Relay node placement:} in WSN, some wireless sensor nodes in a certain region may be died and this will leads to create a hole in the WSN. This problem can be solved by placing the wireless sensor nodes in sensing field using optimal distribution or by deploying a small number of relay wireless sensor nodes with a powerful capabilities whose major goal is the communication with other wireless sensor nodes or relay nodes~\cite{ref52}. his solution can enhance the power balancing and avoiding the overloaded wireless sensor nodes in a particular region in WSN.
-
-\item \textbf{Sink Mobility:} in WSNs that included a static sink, the wireless sensor nodes, which are near the sink drain their power more rapidly compared with other sensor nodes that leads to WSN disconnection and limited network lifetime~\cite{ref53}. This is happening due to sending all the data in WSN to the sink that maximizes the overload on the wireless sensor nodes close to sink. In order to overcome this problem and prolong the network lifetime; it is necessary to use a mobile sink to move within the area of WSN so as to collect the sensory data from the static sensor nodes over a single hop communication. The mobile sink avoids the multi-hop communication and conserves the energy at the static sensor nodes close the base station, extending the lifetime of WSN~\cite{ref54,ref55}.
-
-\end{enumerate}
-
-\subsection{Radio Optimization}
-In wireless sensor node, the radio is the most energy-consuming unit for draining the battery power. Extensive researches have been focused on decreasing the power depletion due to wireless communication by means of optimizing the radio parameters such as: coding and modulation schemes; transmission Power and antenna
-direction; and cognitive radio and Cooperative communications schemes~\cite{ref22}.
\subsection{Scheduling Schemes}
In the last years, extensive researches have been focused on energy harvesting and wireless charging techniques. These solutions are representing 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}. 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}. In wireless charging, the wireless power can be transmitted between the devices without requiring to the connection between the transmitter and the receiver. These techniques are participating in increasing the the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed by using two manners: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.
+\subsection{Radio Optimization}
+In wireless sensor node, the radio is the most energy-consuming unit for draining the battery power. Extensive researches have been focused on decreasing the power depletion due to wireless communication by means of optimizing the radio parameters such as: coding and modulation schemes; transmission Power and antenna
+direction; and cognitive radio and Cooperative communications schemes~\cite{ref22}.
+
+\subsection{Relay nodes and Sink Mobility}
+
+\begin{enumerate} [(I)]
+\item \textbf{Relay node placement:} in WSN, some wireless sensor nodes in a certain region may be died and this will leads to create a hole in the WSN. This problem can be solved by placing the wireless sensor nodes in sensing field using optimal distribution or by deploying a small number of relay wireless sensor nodes with a powerful capabilities whose major goal is the communication with other wireless sensor nodes or relay nodes~\cite{ref52}. This solution can enhance the power balancing and avoiding the overloaded wireless sensor nodes in a particular region in WSN.
+
+\item \textbf{Sink Mobility:} in WSNs that included a static sink, the wireless sensor nodes, which are near the sink drain their power more rapidly compared with other sensor nodes that leads to WSN disconnection and limited network lifetime~\cite{ref53}. This is happening due to sending all the data in WSN to the sink that maximizes the overload on the wireless sensor nodes close to sink. In order to overcome this problem and prolong the network lifetime; it is necessary to use a mobile sink to move within the area of WSN so as to collect the sensory data from the static sensor nodes over a single hop communication. The mobile sink avoids the multi-hop communication and conserves the energy at the static sensor nodes close the base station, extending the lifetime of WSN~\cite{ref54,ref55}.
+
+\end{enumerate}
+
+
+
+
\section{Network Lifetime in Wireless Sensor Networks}
\label{ch1:sec:07}
The limited resources in WSNs have been addressed, and one of the main challenges in WSNs is the limited power resource. For this reason, there are 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. The authors have been 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 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 of 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 $\alpha$.
\contentsline {section}{\numberline {0.1}General Introduction}{xv}{section.0.1}
\contentsline {section}{\numberline {0.2}Motivation of the Dissertation}{xvi}{section.0.2}
\contentsline {section}{\numberline {0.3}The Objective of this Dissertation}{xvi}{section.0.3}
-\contentsline {section}{\numberline {0.4}Main Contributions of this Dissertation}{xvii}{section.0.4}
+\contentsline {section}{\numberline {0.4}The main Contributions of this Dissertation}{xvii}{section.0.4}
\contentsline {section}{\numberline {0.5}Dissertation Outline}{xviii}{section.0.5}
\contentsline {part}{I\hspace {1em}Scientific Background}{xix}{part.1}
\contentsline {chapter}{\numberline {1}Wireless Sensor Networks}{1}{chapter.1}
\contentsline {section}{\numberline {1.5}The Main Challenges in Wireless Sensor Networks}{9}{section.1.5}
\contentsline {section}{\numberline {1.6}Energy-Efficient Mechanisms in Wireless Sensor Networks}{11}{section.1.6}
\contentsline {subsection}{\numberline {1.6.1}Energy-Efficient Routing}{11}{subsection.1.6.1}
-\contentsline {subsection}{\numberline {1.6.2}Radio Optimization}{13}{subsection.1.6.2}
-\contentsline {subsection}{\numberline {1.6.3}Scheduling Schemes}{13}{subsection.1.6.3}
+\contentsline {subsection}{\numberline {1.6.2}Cluster architectures}{12}{subsection.1.6.2}
+\contentsline {subsection}{\numberline {1.6.3}Scheduling Schemes}{12}{subsection.1.6.3}
\contentsline {subsubsection}{\numberline {1.6.3.1}Wake up Scheduling Schemes:}{13}{subsubsection.1.6.3.1}
-\contentsline {subsubsection}{\numberline {1.6.3.2}Topology Control Schemes:}{16}{subsubsection.1.6.3.2}
+\contentsline {subsubsection}{\numberline {1.6.3.2}Topology Control Schemes:}{15}{subsubsection.1.6.3.2}
\contentsline {subsection}{\numberline {1.6.4}Data-Driven Schemes:}{16}{subsection.1.6.4}
-\contentsline {subsection}{\numberline {1.6.5}Battery Repletion:}{17}{subsection.1.6.5}
+\contentsline {subsection}{\numberline {1.6.5}Battery Repletion:}{16}{subsection.1.6.5}
+\contentsline {subsection}{\numberline {1.6.6}Radio Optimization}{17}{subsection.1.6.6}
+\contentsline {subsection}{\numberline {1.6.7}Relay nodes and Sink Mobility}{17}{subsection.1.6.7}
\contentsline {section}{\numberline {1.7}Network Lifetime in Wireless Sensor Networks}{17}{section.1.7}
\contentsline {section}{\numberline {1.8}Coverage in Wireless Sensor Networks }{18}{section.1.8}
\contentsline {section}{\numberline {1.9}Design Issues for Coverage Problems}{19}{section.1.9}
-\contentsline {section}{\numberline {1.10}Energy Consumption Models}{20}{section.1.10}
+\contentsline {section}{\numberline {1.10}Energy Consumption Models}{21}{section.1.10}
\contentsline {subsection}{\numberline {1.10.1}Radio Energy Dissipation Model}{21}{subsection.1.10.1}
\contentsline {subsection}{\numberline {1.10.2}Our Energy Consumption Model}{22}{subsection.1.10.2}
\contentsline {section}{\numberline {1.11}Conclusion}{23}{section.1.11}
