[ThesisAli.git] / CHAPITRE_01.tex

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\chapter{Wireless Sensor Networks}
\label{ch1}
%% Introduction
%\lettrine[lines=2]{A}{u} 

\section{Introduction}
\label{ch1:sec:01}
The wireless networking has received more attention and fast growth in the last decade. The growing demand for the use of wireless applications and emerging the wireless devices such as portable computers, cellular phones, and personal digital assistants (PDAs) have led to develop different infrastructures of wireless networks. The wireless networks can be classified into two classes based on the network architecture~\cite{ref154,ref155}: Infrastructure-based networks that consist of a fixed network structure such as cellular networks and wireless local-area networks
(WLANs); and Infrastructureless networks that constructed dynamically by the cooperation of the wireless nodes in the network, where each node capable of sending the packets and taking the decision based on the network status. Examples of such type of networks include mobile ad hoc networks and wireless sensor networks. Figure~\ref{WNT} shows the taxonomy of wireless networks.

\begin{figure}[h!]
\centering
%\includegraphics[scale=0.4]{Figures/ch1/WNT.eps} 
\includegraphics[scale=0.7]{Figures/ch1/WSNT.jpg} 
\caption{ The taxonomy of wireless networks.}
\label{WNT}
\end{figure}

In recent years, there is increasing interest in Wireless Sensor Networks (WSNs) by many researchers around the world. WSNs are considered as one of the most researched fields in the last decade due to the extensive research in this discipline. It represents a special case of the Ad Hoc networks. Recent advances in wireless networking, Micro-Electro-Mechanical Systems (MEMS), and embedded computing technologies have led to construct low-cost, small-sized, and low-power sensor nodes. These sensor nodes can perform detection, computation, and data communication of surrounding environment. A WSN includes a large number of sensor nodes that can sense, process, and transmit data over a wireless communication. The sensor nodes communicate with each other by using multi-hop wireless communications and cooperate together to monitor the area of interest. The measured data is reported to a monitoring center called sink for further analysis~\cite{ref1,ref2}. The WSN receives the orders from the end user by means of the sink. These orders specify data aggregation, computation and delivery missions to wireless sensor nodes, after that the sensed measurements are received from the WSN by the sink~\cite{ref3}. The cooperation among the wireless sensor nodes in WSNs has been led to several advantages over the traditional wireless ad-hoc networks, like self-organization, rapid deployment, flexibility, and inherent intelligent-processing capability~\cite{ref5}.


\section{Wireless Sensor Network Architecture} 
\label{ch1:sec:02}
A typical WSN architecture consists of a set of a typical wireless sensor nodes, which are capable of sensing the surrounded physical phenomenon such as fire in the forest (see~figure~\ref{wsn}), and then send the sensed data to a controller node called a sink. One or more sink in WSN are responsible for collecting and processing the received sensed data, and then send that data through the Internet to the end-user. 

In this WSN architecture, the basic element is a typical wireless sensor node that composed of four major units~\cite{ref17,ref18}: sensing, computation, communication, and power. In addition, there are three optional units, which can be combined with the sensor node such as localization system, mobilizer, and power generator. Figure~\ref{twsn} shows the components of a typical wireless sensor node~\cite{ref17}.

\begin{figure}[h!]
\centering
\includegraphics[scale=0.5]{Figures/ch1/twsn2.pdf} 
\caption{ The components of a typical wireless sensor node.}
\label{twsn}
\end{figure}

\begin{enumerate} [(I)]
\item \textbf{Sensing Unit:} consists of two main parts: sensors and analog to digital converters (ADCs). It is responsible for sensing the physical phenomena. The analog signal produced by the sensors is converted into digital data by ADC. The resulted digital data is sent to the computation unit  for further processing.

\item \textbf{Computation Unit:} The main purpose of this unit is to manage and manipulate the instructions that related to sensing, communication, and self-organization. This allows to the sensor node cooperates with other sensor nodes in order to perform the allocated sensing tasks. It is composed of a processor chip, an active short-term memory for storing the sensed data, an internal flash memory for storing program instructions, and an internal timer.

\item \textbf{Communication Unit:} It is responsible for all data transmission and reception of the sensor node that are performed by the transceiver circuitry. A transceiver circuit is composed of a mixer, frequency synthesizer, voltage-controlled oscillator (VCO), phase-locked loop (PLL), demodulator, and power amplifiers, all of which consume valuable power~\cite{ref19}.

\item \textbf{Power Unit:} This unit represents the most significant part of wireless sensor node.  It supplies the other units by the needed power. 

\end{enumerate}

Furthermore, additional components can be incorporated into wireless sensor node and according to the application requirements, such as  a localization system, a power generator, and a mobilizer~\cite{ref17,ref19}. These components are showed by the dashed boxes in figure~\ref{twsn}.

\begin{enumerate} [(I)]         

\item \textbf{Localization System:} It is important that the wireless sensor node is equipped with a location finding system because it is necessary for many WSN applications. It is required for routing algorithms and sensing coverage algorithms, which need information about the location of the wireless sensor nodes. The location finding system is composed of a Global Positioning System (GPS) or a discovery algorithm that executes a localization system to provides information about the location of wireless sensor node using distributed computation.

\item \textbf{Mobilizer:} The mobility function is sometimes needed in many applications to move the wireless sensor node from one location to another so as to perform a certain task in WSN. Therefore, it is necessary that the wireless sensor node equipped with the mobilizer system for such applications. A high energy consumption is needed to support the mobility in wireless sensor node, and it should be supported efficiently. The movement of wireless sensor node is controlled by the mobility function with cooperation with the sensing unit and the computation unit.

\item \textbf{Power Generator:} Several applications in WSNs need to operate for a longer time. So, it is essential to equip the wireless sensor node with additional power source in order to prolong the network lifetime. The better energy source to generate the power for outdoor applications is a solar cell. An another power harvesting mechanisms~\cite{ref20,ref21} for thermal, motion, vibration, micro water flow, Biological, pressure gradients, and electromagnetic radiation energy harvesting can be used to yield increasing power output to extend the network lifetime. 
\end{enumerate}

\begin{figure}[h!]
\centering
\includegraphics[scale=0.9]{Figures/ch1/wsn.jpg} 
\caption{ Wireless sensor network architecture.}
\label{wsn}
\end{figure}

The TinyOS has been used as an operating system in wireless sensor node. It is developed by the university of California, Berkeley and designed to work on platforms with limited storage and processing power.


\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. These WSNs suffer from different conditions and challenges. WSNs can be classified into six types, where five types of them presented in~\cite{ref4,ref5}. Figure~\ref{wsnt} gives an example of WSNs types.
\begin{figure}[h!]
\centering
\includegraphics[scale=0.5]{Figures/ch1/typesWSN.pdf} 
\caption{Examples for types of WSNs}
\label{wsnt}
\end{figure} 

\begin{enumerate}[(I)]

\item \textbf{Terrestrial WSNs:}
The wireless sensor nodes are deployed over the land constructing a network of hundreds to thousands of sensor devices. Several applications are used terrestrial WSNs such as physical environmental sensing and monitoring, industrial monitoring, and surface explorations. The main challenges in this type of WSNs are ensuring coverage and connectivity with removing redundancy, energy-efficient routing, data communication reduction, balancing energy consumption, energy-efficient data aggregation. This dissertation focuses on this type of WSNs.

\item \textbf{Underground WSNs:} 
The wireless sensor nodes are deployed over caves, mines, or underground and communicate through soil~\cite{ref9,ref10}. The most important applications in underground WSNs are structural monitoring, agriculture monitoring, landscape management, underground environment monitoring of soil, water or mineral and military border monitoring. The essential challenges of underground WSNs are the high levels of attenuation and signal loss in communication. Therefore, it needs a certain type of devices so as to provides a robust wireless communication underground. The risk of devices comes from unsuitable underground conditions, replace or recharge the battery seems to be impossible, and the WSN deployment is expensive. 

\item \textbf{Underwater WSNs:} 
A WSN is composed of a wireless sensor nodes deployed in the water such as the ocean~\cite{ref11,ref12}. Many challenges should be faced in this type of WSN such as the high cost of the underwater sensor devices; underwater wireless communication has limited bandwidth, high latency, signal fading, and long propagation delay problems; sparse deployment in which the wireless sensors should be able to self-organized to adapt to various condition of the ocean environment; and the limited power of the wireless sensor node battery as well as it is impossible or difficult to replace or recharge it that led to looking for about energy efficient underwater wireless communication mechanisms. The main underwater WSNs applications are seismic monitoring, disaster prevention monitoring, underwater robotics, pollution monitoring, equipment monitoring, and undersea surveillance and exploration. 

\item \textbf{Multimedia WSNs:} 
It consists of inexpensive wireless sensor devices supplied with CMOS cameras or  microphones devices. It is deployed in a pre-guided way to ensure the coverage, where the multimedia WSN capable of retrieving the audio, video, and image contents from the physical environment~\cite{ref13,ref14,ref15}. The multimedia data such as images, videos, and sounds can be stored by these wireless sensor devices. The multimedia WSN contributed in improving some existing WSN applications such as tracking and monitoring.  The main challenges in multimedia WSN include:  the processing, filtering, and compressing the multimedia data;  the requested bandwidth and high energy consumption; Quality-of-Service provisioning is very difficult because of the link capacity and delays; it should combine different wireless techniques; energy-efficient cross-layer design; It needs flexible architecture to support various applications; and the deployment is based on the multimedia devices coverage. 

\item \textbf{Mobile WSNs:} 
A network composed of a mobile sensor nodes that can self-moving and reacting for the physical phenomena~\cite{ref16}. The mobile sensor node is self-organized and it is capable of replacing its position autonomously. In addition, it is able to sense, process, and communicate with other mobile sensors. Many challenges that should be faced in mobile WSNs such as: maintaining a sufficient sensing coverage and connectivity; the self-organization; the navigation and controlling mobile sensors; mobility management; processing and distributing in WSN; location determination with mobility; and minimizing the energy consumption especially during the movement. The mobile WSN applications are environment, habitat, and underwater monitoring; target tracking; military surveillance; and search and rescue. The mobile WSNs provide a higher coverage ratio and connectivity compared with static sensors.

\item \textbf{Flying WSNs:} 
A network consists of a low-cost wireless sensor nodes, which are equipped with a Micro Aerial Vehicles (MAVs). It can fly autonomously or can be operated remotely without intervention of any human personnel~\cite{ref6,ref7}.  The general objective of this type of WSN is to retrieve the information from some inaccessible locations.  For example, establishing an ad hoc network connection between rescuers and disaster victims over airborne relays or surveying an area from the air. Flying WSN provides a remote sensing and wireless networking platforms that collect the data from local sensors or other sources, and send the collected information over airborne wireless relays to a ground station. Using Flying WSNs have led to new developments for both military and civilian applications due to their flexibility, versatility, easy installation, and the operating low-cost  \cite{ref8}. The applications are search and destroy operations, disaster monitoring, relay for ad hoc networks, wind estimation, managing wildfire, border surveillance, remote sensing, and traffic monitoring. The main challenges are constructing a lightweight MAV capable of flight;  the wireless communication; designing a software protocols  to achieve semi-autonomous flight; and combining all the subsystems such as  propulsion, flight control, and wireless networking into a flying WSN.
\end{enumerate}


\section{Wireless Sensor Network Applications}
\label{ch1:sec:04}
\indent The fast development in WSNs has been led to study  their different characteristics extensively. However, the WSN is concentrated on various applications. In this section, we demonstrate a different academic and commercial applications. The WSN is composed of various types of sensors such as \cite{ref17,ref19}: thermal, seismic, magnetic, visual, infrared, acoustic, and radar. These sensors 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. Thus, a wide range of WSN applications can be classified into five classes~\cite{ref22}. Figure~\ref{WSNAP} shows classification of WSN applications. 

\begin{figure}[h!]
\centering
\includegraphics[scale=0.4]{Figures/ch1/WSNAP.eps} 
\caption{Classification of WSN applications}
\label{WSNAP}
\end{figure}

\begin{enumerate}[(I)]

\item \textbf{Health-care Applications:} There is increasing interest and extensive research in the health-care applications. Two types of health-care systems are recognized~\cite{ref22}: vital status monitoring and remote health-care surveillance. In vital status monitoring applications, sick persons are wearing the sensors in order to oversee the state of their health and to allow medical staff to monitor and control the patient's status expeditiously. The most general used vital signs are ECG, pulse oximetry, body temperature, heart rate, and blood pressure~\cite{ref27}. These applications include  mass-casualty disaster monitoring, vital sign monitoring in hospitals, and sudden fall or epilepsy seizure detection. On the other hand, remote health-care surveillance refers to the health services that do not require continuous existence of health care. These applications include elderly monitoring, providing support to a physically impaired person, gather clinically relevant information for rehabilitation supervision~\cite{ref28}, location tracking, and medication intake monitoring~\cite{ref27}.


\item \textbf{ Environment and agriculture Applications:}
\indent Several WSNs applications have been developed for the precision agriculture, cattle monitoring, and environmental monitoring.

\indent Precision agriculture refers to the science of using the innovative and modern technology to improve the crop production. The WSNs are the main technology for developing of precision agriculture~\cite{ref29}. This technology contributes to increasing the agricultural yields, improving quality, and reducing costs whilst decreasing the damaging impact on the environment. The wireless sensors are distributed over the target field so as to monitor the main parameters such as soil moisture, atmospheric temperature, and creating a decision support system \cite{ref22}. The wireless sensors can be used in agricultural services like Irrigation, fertilization,  pest control, animal and pastures monitoring, horticulture(e.g., greenhouse and viticulture)~\cite{ref30}. 

\indent In cattle monitoring applications, the WSN is used to livestock control and monitoring such as virtual fencing for extensive grazing systems, animal behavior study, health monitoring, to detect disease breakouts, to localize them, and to control end-product quality (meat, milk).  

\indent 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,
surveillance, reconnaissance, and targeting systems. It estimates the unexpected events such as  natural disasters and threats as well as some of the military applications keep under surveillance friendly forces, equipment, and ammunition; battlefield surveillance;  reconnaissance of enemy forces; targeting; battle damage assessment; and nuclear, biological, and chemical attack detection and reconnaissance~\cite{ref19}. 

\indent 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 observe the agent's situation and the environmental factors. The main applications include emergency rescue teams, miners, and soldiers. In passive supervision systems, the wireless static sensors are scattered over a large field in order to monitor a civil area or nuclear site for a longer time. These applications include 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:}
The fast development in the domain of Intelligent Transport Systems (ITS) ranging from flight transport and traffic management to in-vehicle services like driver alert or traffic monitoring. As a result, the transportation data collection and communication represent a major role in the ITS~\cite{ref37}.

\indent The WSNs can be integrated with the transportation systems such as traffic monitoring, real-time safety systems, and commercial services~\cite{ref22}. In traffic-monitoring systems, the wireless sensors are embedded within or across the pavement or intersections, and some sensors are installed above or on the side of roads so as to collect the information related to the traffic~\cite{ref36}. These WSN traffic systems are used to detect the vehicles, vehicles count, and classification. In safety applications, the wireless sensors are used to deal with many cases such as driving safety \cite{ref41}, vehicle safety~\cite{ref38},  where many wireless sensors are scattered on roads or vehicles, collaborating through Vehicle-to-Vehicle, Vehicle-to-Roadside, and Vehicle-to-Infrastructure communications. Extensive research in these domains is concentrated on preventing the collisions among vehicles by Vehicle-to-Vehicle communications~\cite{ref40}. In addition, commercial applications can be given by service providers. They include route guidance to avoid rush-hour jams, smart high-speed tolling, assistance in finding a parking space, and automobile journey statistics collection~\cite{ref22}.


\item \textbf{Industry Applications: Manufacturing and Smart Grids:}
The most significant goal for many companies is the automation of controlling and monitoring systems in many applications such as manufacturing, water treatment, electrical power distribution, and oil and gas refining. The WSNs is incorporated in Supervisory Control, Data Acquisition (SCADA) systems and smart grids~\cite{ref22}. SCADA systems are a computer software by which the industrial processes in factories are controlled and supervised. The wireless sensors are used with actuators to control the factory,  detection of liquid/gas leakages, and inventory management. These applications are needed for precise monitoring of temperature, shock, and noise factors in remote locations such as tanks, turbine engines, or pipelines. In Smart Grids, the goal is to supervise the power supply and  depletion operation. The main applications in smart grid include: sensing the relevant parameters affecting power output (pressure, humidity, wind orientation, radiation, etc.); control of turbines, motors and underground cables; home energy management; and remote detection of faulty components.

\end{enumerate}
%\section{Protocol Design Requirements}
%\label{ch1:sec:06}

\section{The Main Challenges in Wireless Sensor Networks}
\label{ch1:sec:05}
\indent 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 represent the main reason to propose a different solutions so as to face them 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 in order to extend the network lifetime.

\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 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 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 the 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 needs 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. Therefore, the main challenge is how to apply 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,  the 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.

  

\item \textbf{Reliability:} Many applications require a high quality of coverage. These applications need to deploy a large number of a cheap sensor nodes so as to satisfy their requirements. This large number of the sensor nodes may be prone to the failure and this will affect the quality of service provided by the application. However, it is important to build mechanisms inside the protocols so as to avoid the failure of some sensor nodes during the network operation and to increase the robustness of the proposed protocol in WSNs.

\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{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 be cooperated to find this route and to serve as relays.

\item \textbf{Data Management:} Represents one of the challenges that contributes in depleting the energy of the sensor nodes in WSNs. The main task of the WSN after deploying the sensor nodes in the target environment that need to be monitored is to collect the sensed data from this physical environment and then transmit it to the base station. Since there are many sensor nodes in WSN; and since every sensor node want to transmit its sensed data to the base station; there is a large amount of data that need to be managed, processed and routed, to the sink and it represents a real challenge in WSNs.

\item \textbf{Security:} The sensitivity of the information collected by WSNs represents the final challenge that should be faced in WSNs. This information is susceptible to malicious intrusions and hacker attacks. However, it is necessary to provide energy efficient schemes by WSNs to protect this information during the operation of WSNs.

\end{enumerate}



\section{Energy-Efficient Mechanisms in Wireless Sensor Networks}
\label{ch1:sec:06}
\indent 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=0.4]{Figures/ch1/WSN-M.eps} 
\caption{Energy-Efficient Mechanisms in Wireless Sensor Networks}
\label{emwsn}
\end{figure}

\subsection{Energy-Efficient Routing:}

\indent The energy-efficient routing is a significant factor in the design of WSN protocols in order to satisfy the main constraints in the hardware, power, and other resources of wireless sensor nodes~\cite{ref42}. Many challenging factors need to be taken into consideration in designing a routing protocol for WSN such as limited energy capacity, node deployment, sensor location, dynamic network, hardware resource constraints, data aggregation, latency, scalability, and fault tolerance.


\subsubsection{Routing Metric based on Residual Energy:} Lifetime maximization can be achieved by using the residual power of wireless sensor node as a routing metric that should be taken into account in executing the routing protocol in WSNs. The routing protocols should be concentrated on the remaining power of sensor nodes during taking the decision to select the next hop toward the destination. They should not only depend on the shortest path solution.  They prioritize routes on the basis of an energy metric (sometimes with other routing metrics). Therefore,  it is called energy-aware routing protocols.~\cite{ref45,ref46}.

\subsubsection{Multipath Routing:} Represents an efficient strategy that 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. Many multipath routing protocol summarized in~\cite{ref50,ref51}.

\subsection{Cluster Architectures:}

\indent 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 for managing the activities of the wireless sensor nodes in the cluster and it communicates and coordinates with other cluster heads or with 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 is led to decrease the communication range within the cluster. Therefore, the energy needed for communication among the nodes inside the cluster is minimized.
\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 The continuous changing of cluster head according to the 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}

\indent In addition, the clustering supports network scalability in WSNs~\cite{ref43,ref44}. The clustering approach represents an efficient mechanism for scalability of WSN and providing energy-efficient data aggregation by minimizing the consumption of a limited energy by means of grouping the sensor nodes and organizing them hierarchically. Several important design considerations that should be taken into account during designing clustering algorithms such as limited energy, network lifetime, limited abilities, application dependency, secure communication, cluster formation, cluster head selection, synchronization, data aggregation, repair mechanisms, and Quality of Service (QoS)~\cite{ref161}.


\subsection{Scheduling Schemes:}

\indent Many scheduling schemes have been suggested so as to decrease the energy depletion and improve the lifetime of WSNs~\cite{ref58,ref59}. These schemes deal with scheduling the states of wireless sensor nodes and putting the idle sensor nodes into sleep mode (i.e, turn off the radio unit) to save the energy. Figure~\ref{wsns} summarizes the scheduling schemes in WSNs. In this figure, the scheduling schemes are classified into two main branches~\cite{ref56,ref57}: 
\begin{itemize}
\item Wake up scheduling aims to manage the wireless sensor node states (sleep/wake up) in WSN by selecting a set of time intervals for a sensor nodes to be awake from continuous time duration. 
\item Topology control in which a set of a wireless sensor nodes is chosen to be awake from a given sensor nodes in WSN.
\end{itemize}

 
\begin{figure}[h!]
\centering
\includegraphics[scale=0.5]{Figures/ch1/WSN-S.pdf} 
\caption{Scheduling Schemes in Wireless Sensor Networks}
\label{wsns}
\end{figure}


\subsubsection{Wake up Scheduling Schemes:}

\indent This section demonstrates the scheduling schemes from the point of view of schedule composition process and the framework of the wake-up schedule. In these scheduling schemes, the wake-up interval refers to the period of time at which the radio unit is turned on so as to send or receive the packets. On the other hand, the sleep interval refers to a period of time at which the radio unit is turned off so as to retain the energy of wireless sensor node. Some schemes divide the time into equal length durations of time and are called slotted schemes. The other schemes work with the time in continuous way and are called unslotted schemes. The sleep and wake up intervals are defined for the unslotted schemes, whilst for the slotted schemes, these intervals are represented as multiple slots. The wake-up schedule represents a set of a wake-up and sleep intervals, which are produced for one period. This schedule replicates to each period and it can be changed by the wake-up scheduling scheme during the different periods of time. The final goal of this wake-up schedule is to permit to exchange the data among the wireless sensor nodes in WSN during the wake-up interval. As shown in figure~\ref{wsns}, the requirement for synchronization has been categorized the wake-up scheduling into three categories~\cite{ref57}:

\begin{enumerate} [(I)]

\item \textbf{Synchronous Schemes:} The time synchronization among wireless sensor nodes is required. Several synchronous approaches have been suggested that based on the time synchronization in their work. The majority of synchronous schemes work in periodic (cyclic) way by preparing the same wake-up schedule for every period unless a change by wake-up scheduling algorithm. On the other hand, the aperiodic schemes do not apply the periodic schedule. 

\begin{enumerate} [(A)]
\item The periodic wakeup scheduling schemes work either in slotted and unslotted way, where the period is divided into equal-length slots in the slotted schemes. The major challenge in periodic wakeup scheduling is to select and activate the best time interval(s) for a period so as to the wireless sensor node performs the communication (sending and receiving). This is from point of view of wireless sensor node, whilst from the standpoint of the WSN, choosing the time intervals through the wireless sensor nodes to satisfy a certain performance factor in WSN seems to be hard task. This performance can be carried out with the cooperation among the sensor nodes in WSN to produce the wake-up schedule. The periodic wakeup scheduling schemes are classified into five groups based on the degree of the cooperation~\cite{ref57}:
 
\begin{enumerate} [(i)]
\item Neighbor-coordinated in which the wireless sensor node generates its own wake-up schedule taking into consideration the wake-up schedules of its neighbor sensor nodes. 
%The protocols that used this approach like  S-MAC protocol, Timeout MAC (T-MAC), Pattern-MAC (PMAC), Dynamic S-MAC (DSMAC), and ESC; 
\item Path-coordinated is suggested to allow the wireless sensor nodes along the path to collaborate to manage their wake-up schedules so as to permit to packets passing on the path without delay.  
%Some examples  used this approach~\cite{ref65,ref66,ref67}; 
\item Network-coordinated: the wireless sensor nodes are cooperated in order to produce a global or per sensor node wake up schedule that achieves a specific objective.  These schemes can be centralized or distributed. In centralized scheme, one sensor node responsible for constructing the wake-up schedule for a subset or all nodes in WSN. In a distributed scheme, every wireless sensor node in the network contributes in the production of their wake-up schedules.  
%Instances that used this approach in~\cite{ref68,ref69}; 
\item Non-collaborative: in these schemes, the wireless sensor node applies control theory or other mechanisms, which are based on local information inside the sensor node (such as queue length or duty cycle). It does not use the information from the other nodes in order to construct its own wake-up schedule. 
%Some examples that used these schemes~\cite{ref70,ref71}.
\end{enumerate}

\item Aperiodic wakeup scheduling: the wireless sensor node decides its own schedule to wake up or sleep in each slot randomly. 
%Examples that used this technique are proposed in~\cite{ref72,ref73}.
 
\end{enumerate}

\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 wake up and ready to receive. The major advantage is received by these schemes in that they do not need time synchronization that lead to removing the energy consumption is required to apply the periodic time resynchronization among the sensor nodes~\cite{ref74}. Another advantage of using the asynchronous schemes in that they do not need to exploit the limited resources (processing, memory, and radio) of the sensor nodes because there is 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 be 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 advantages of these schemes represented by the low requirement of the memory and processing whilst the major disadvantages are low-duty-cycle and the sleep latency is non-deterministic. 
%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 sleep latency, is stochastic. 
%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. 
%The proposed works in~\cite{ref81,ref82,ref83} give an instances for these schemes.
\end{enumerate}
 
\item \textbf{Hybrids Schemes:} Some schemes need to use both time synchronous and asynchronous methods. According to WSN circumstances, the wake up scheduling switches between synchronous and asynchronous modes, where the synchronous schemes work efficiently in the heavy load circumstances whilst in the light load circumstances, the asynchronous schemes are more efficient. 
%The protocols in~\cite{ref79,ref80} are an examples on these schemes.
\end{enumerate}



\subsubsection{Topology Control Schemes:}

\indent The topology control schemes deal with the redundancy in the WSNs. The WSN is always deploying with high density and in a random way, where a large number of wireless sensor nodes are usually throwing by the airplane over the area of interest. The purpose of deploying a dense WSN is to cope with the sensor failure during or after the WSN deployment and to maximize the network lifetime by means of exploiting the overlapping among the sensor nodes in the network by putting the redundant sensor nodes into sleep mode in order to benefit from it later. The major goal of topology control protocols is to dynamically adapt network topology based on requirements of application so as to minimize the number of active sensor nodes, achieve the tasks of the network, and prolong the network lifetime~\cite{ref56,ref22}. Many factors can be used to decide which sensor nodes should be turned on or off and when. The topology control schemes have been classified into two categories~\cite{ref56}: 
 
\begin{enumerate} [(I)]
\item \textbf{Location Driven Protocols} in which determining which wireless sensor node to turn on or off based on its location that should be known; for example, Geographical Adaptive Fidelity (GAF) protocol~\cite{ref84}. These schemes are called network coverage that describing how the sensing field is monitored using minimum number of wireless sensor nodes in order to achieve application requirements and prolong the network lifetime~\cite{ref102}.

\item \textbf{Connectivity Driven Protocols} in which the wireless sensor nodes are activated or deactivated so that the sensing coverage and connectivity of WSN are assured such as Span protocol~\cite{ref85}.
\end{enumerate}


\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. 
%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}:
 
%\begin{enumerate} [(I)]
\subsubsection{Data Reduction Schemes} deal with reducing the amount of data need to be transmitted to sink. They can be divided into stochastic approaches, time series forecasting, and algorithmic approaches. In stochastic approaches, the physical phenomena are transformed using stochastic characterization. The aggregation by these protocols requires high processing. Therefore, it is feasible to work on a 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 are demonstrated using heuristic or state transition model. 

\subsubsection{Energy Efficient Data Acquisition Schemes} are concentrated 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 that 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 it consumes more energy due to requiring a high processing. While, the hierarchical sampling are more efficient when there are different types of sensors are 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 conserve the energy by means of data acquisition. 
%\end{enumerate} 

\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~\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}. 

\subsubsection{Wireless Charging:}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 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:}

\indent 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:}
\indent The relay node placement and the mobility of the sink can be considered as energy-efficient strategies, which are used to minimize the consumption of the energy and extend the lifetime of WSNs.
%\begin{enumerate} [(I)]
\subsubsection{Relay node placement:} 
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 using 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 avoiding the overloaded wireless sensor nodes in a particular region in WSN.

\subsubsection{Sink Mobility:} 
In the WSNs include 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 in WSN to the sink maximizes the overload on the sensor nodes near 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 near to the base station, extending the lifetime of WSN~\cite{ref54,ref55}.

 

%\end{enumerate}




\section{Network Lifetime in Wireless Sensor Networks}  
\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 sometimes 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 previously proposed works in prolonging the network lifetime, various definitions 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 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 with 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 losing 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.

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 $\alpha$.


\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 as less 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 regions of some sensor  nodes to save energy by turning off  some of them during the sensing phase to prolong the network lifetime. The most discussed coverage problems in literature can be classified into three types~\cite{ref96}:
\begin{enumerate}[(i)] 
\item \textbf{Area coverage}~\cite{ref97,ref153} where every point inside an area is to be monitored. The work in this dissertation deals with this type of coverage.
\item \textbf{Target coverage}~\cite{ref98,ref153} 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. 
\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}:

\begin{enumerate}[(A)] 
\item \textbf{The Binary Disc Sensing Model:}
It is the simplest sensing coverage model in which every point in the sensing field can be sensed if it is within the sensing range of the wireless sensor node. Otherwise, it is not able to detect any point that is outside the sensing range of the sensor node. The sensing range in this model can be viewed as a circular disk with a radius equal to $R_s$. Assume that a sensor node $s_i$ is deployed in the position $(x_i,y_i)$. For any point P at the position $(x,y)$, the equation \ref{eq1-ch1} shows the binary sensor model that expresses the coverage $C_{xy}$ of the point P by sensor node $s_i$ as follow
\begin{equation}
C_{xy}\left(s_i \right)  = \left \{ 
\begin{array}{l l}
  1& \mbox{if $d(s_i,P)$  $<$ $R_s$,} \\
  0 &  \mbox{otherwise.}\\
\end{array} \right.
\label{eq1-ch1} 
\end{equation}

where $d(s_i,P) = \sqrt{(x_i - x)^2 + (y_i - y)^2}$, denotes the Euclidean distance between sensor node $s_i$ and P. 


\item \textbf{The Probabilistic Sensing Model}
In reality, the event detection by the sensor node is imprecise. Hence, the coverage $C_{xy}$ requires to be represented in probabilistic way. The probabilistic sensing model is more practical, which can be used as an extension of the binary disc sensing model. The equation \ref{eq2-ch1} shows the probabilistic sensing model that expresses the coverage $C_{xy}$ of the point P by the sensor node $s_i$ as follow

\begin{equation}
C_{xy}\left(s_i \right)  = \left \{ 
\begin{array}{l l l}
  1 & \mbox{if $d(s_i,P)$  $ \leqslant $ $R_s - R_u$} \\
  \emph{e^{-\lambda\alpha^{\beta}}} & \mbox{if $R_s - R_u$ $ < $ $d(s_i,P)$  $ < $ $R_s + R_u$} \\
  0 &  \mbox{if $R_s + R_u$ $ \leq $ $d(s_i,P)$}\\
\end{array} \right.
\label{eq2-ch1} 
\end{equation}

Where $R_u$ is a measure of the uncertainty in sensor detection, $\alpha = d(s_i,P) - (R_s - R_u)$.  The $\lambda$ and the $\beta$ are parameters that measure detection probability when a point P is at distance greater than $R_u$ but within a distance from the sensor node $s_i$. 

\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.




\section{Design Issues for Coverage Problems:}
\label{ch1:sec:11}
\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 $\textbf{Deployment Method}$ 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 $\textbf{Coverage Ratio}$ 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 $\textbf{Network Connectivity}$ is to ensure the existence of a path from any sensor node in WSN to the sink. The connected WSN refers to guarantee sending the sensed data from one sensor node to another sensor node directly toward the sink. 
%It is necessary to consider the communication range of wireless sensor node is at least twice that of the sensing range ($R_c \geqslant 2R_s$) so as to imply connectivity among the sensor nodes during covering the sensing field~\cite{ref108}.

 
\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.

\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:

\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 Transmission:  processing for address determination, packetization, encoding, framing, and maybe queuing; supply for the baseband and RF circuitry.

\item Reception:  Low-noise amplifier, downconverter oscillator, filtering, detection, decoding, error detection, and address check; reception even if a node is not the intended receiver.

\item Listening: Similar to reception except that the signal processing chain stops at the detection.

\item Data Acquisition: sensing, processing sensed data, analog to digital conversion, preprocessing, and maybe storing.

\item Sleeping: a low power to the sensor node to stay alive.



\end{enumerate}

%In this section, two energy consumption models are explained. The first model called radio energy dissipation model and the second model represent our energy consumption model, which has been used by the proposed protocols in this dissertation.


%\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.
\begin{figure}[h!]
\centering
\includegraphics[scale=0.4]{Figures/ch1/RDM.eps} 
\caption{Radio energy dissipation model}
\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


\begin{equation}
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 $d \geqslant d_0$}\\
\end{array} \right.
\label{eq3-ch1} 
\end{equation}

As well as to receive a k-bit packet, the radio expends

\begin{equation}
      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. 

%\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.