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7 \chapter{Wireless Sensor Networks}
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12 \section{Introduction}
14 %The wireless networking has received more attention and fast growth in the last decade.
15 In the last decade, wireless networking has became a major component of the global network infrastructure.
16 More precisely, the growing demand for the use of wireless applications and the continuous arrival of wireless devices such as portable computers, cellular phones, and Personal Digital Assistants (PDAs) have led to develop different infrastructures of wireless networks. 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
17 (WLANs); and Infrastructureless networks that are constructed dynamically by the cooperation of the wireless nodes in the network, where each node is capable of sending packets and taking decisions 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.
21 %\includegraphics[scale=0.4]{Figures/ch1/WNT.eps}
22 \includegraphics[scale=0.7]{Figures/ch1/WSNT.jpg}
23 \caption{Taxonomy of wireless networks.}
27 %In recent years, there is increasing interest in Wireless Sensor Networks (WSNs) by many researchers around the world.
28 %WSNs are considered as one of the most researched fields in the last decade due to the extensive research in this discipline.
29 Wireless Sensor Networks (WSNs) represent a special case of the Ad Hoc networks, resulting from recent advances in wireless networking, Micro-Electro-Mechanical Systems (MEMS), and embedded computing technologies, which have led to construct low-cost, small-sized, and low-power sensor nodes.
30 %These sensor nodes can perform detection, computation, and data communication of surrounding environment.
31 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. Measured data are reported to a monitoring center called base station or sink for further analysis~\cite{ref1,ref2}. A 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 wireless sensor nodes in WSNs has several advantages over traditional wireless ad-hoc networks, like self-organization, rapid deployment, flexibility, and inherent intelligent-processing capability~\cite{ref5}.
34 \section{Architecture}
36 A typical WSN architecture consists in a set of a huge number of 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 sink node. One or more sinks in WSN are responsible for collecting and processing the received sensed data, and making them available through the Internet to the end-user.
38 The basic element is a wireless sensor node that is composed of four major units~\cite{ref17,ref18}: sensing, computation, communication, and power.
39 %In addition, there are three optional units, which can be combined with the sensor node such as localization system, mobilizer, and power generator.
40 Figure~\ref{twsn} shows the components of a typical wireless sensor node~\cite{ref17}.
44 \includegraphics[scale=0.52]{Figures/ch1/twsn2.pdf}
45 \caption{ Components of a typical wireless sensor node.}
49 \begin{enumerate} [(I)]
50 \item \textbf{Sensing Unit:} It consists of two main parts: sensors and Analog to Digital Converters (ADCs). It is responsible for sensing physical phenomena. The analog signal produced by a sensor is converted into digital data by ADC, and then sent to the computation unit for further processing.
52 \item \textbf{Computation Unit:} The main purpose of this unit is to manage and manipulate the instructions that are related to sensing, communication, and self-organization. This allows the sensor node to cooperate 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.
54 \item \textbf{Communication Unit:} It is responsible for all data transmission and reception done by the sensor node, which 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 these components consume valuable power~\cite{ref19}.
56 \item \textbf{Power Unit:} This unit represents the most significant part of a sensor node. It supplies the other units by the needed power.
60 Furthermore, additional components can be incorporated into wireless sensor node 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}.
62 \begin{enumerate} [(I)]
64 \item \textbf{Localization System:} It is important that a 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. Location finding system is composed of a Global Positioning System (GPS) or a discovery algorithm which provides information about the location of wireless sensor node using distributed computation \cite{ref232,ref233}.
66 \item \textbf{Mobilizer:} The mobility function is sometimes needed in some applications, like health monitoring~\cite{ref27} and animal tracking~\cite{ref22}, to move the wireless sensor node from one location to another so as to perform a certain task in WSN. Therefore, it is necessary to equip the node with the mobilizer system for such applications. A high energy provision is needed to support mobility in wireless sensor node, and it should be supported efficiently. The movements are controlled by the mobility function in cooperation with the sensing unit and the computation unit.
68 \item \textbf{Power Generator:} Several applications in WSNs need to operate for a long 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 power in outdoor applications is a solar cell. Other power harvesting mechanisms~\cite{ref20,ref21} like thermal, motion, vibration, micro water flow, biological, pressure gradients, and electromagnetic radiation energy harvesting can be used to yield increasing power output.
73 \includegraphics[scale=1.2]{Figures/ch1/wsn.jpg}
74 \caption{ Wireless sensor network architecture.}
78 Sensor nodes use a software layer called, Operating System (OS), which is logically between the node's hardware and the application layer~\cite{ref18}. The OS enables the applications to interact with hardware resources, to schedule and prioritize tasks, memory management, power management, file management, networking, and to arbitrate between contending applications and services that attempt to reserve resources. The TinyOS is commonly 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.
81 \section{Types of Wireless Sensor Networks}
83 According to the physical phenomena for which the WSN is developed, WSNs can be deployed on the ground, underground, or underwater. WSNs suffer from different conditions and challenges. WSNs can be classified into six types, among which five types are presented in~\cite{ref4,ref5}. Figure~\ref{wsnt} gives examples of different WSNs types.
86 \includegraphics[scale=0.5]{Figures/ch1/typesWSN.pdf}
87 \caption{Examples for types of WSNs}
91 \begin{enumerate}[(I)]
93 \item \textbf{Terrestrial WSNs:}
94 Wireless sensor nodes are deployed over the land constructing a network of hundred to thousand of sensor devices. Several applications use terrestrial WSNs for physical environmental sensing and monitoring, industrial monitoring, and surface explorations. The main challenges in this type of WSNs are to ensure coverage and connectivity with removing redundancy, energy-efficient routing, data communication reduction, balancing energy consumption, energy-efficient data aggregation.
95 %This dissertation focuses on this type of WSNs.
97 \item \textbf{Underground WSNs:}
98 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 able to provide a robust wireless underground communication. The risk on these devices comes from unsuitable underground conditions, replacing or recharging the battery seems to be impossible, and the WSN deployment is expensive.
100 \item \textbf{Underwater WSNs:}
101 This type of WSNs is composed of wireless sensor nodes deployed in the water such as the ocean~\cite{ref11,ref12}. Many challenges must be faced in this type of WSN such as the high cost of the underwater sensor devices; underwater wireless communication with 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; the limited power of the node battery, and the difficulty to replace or recharge it. These challenges lead to look for 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. \\
103 \item \textbf{Multimedia WSNs:}
104 They consist of inexpensive wireless sensor nodes supplied with CMOS (Complementary Metal-Oxide-Silicon) cameras or microphones devices. The nodes are deployed in a pre-guided way to ensure the coverage. Multimedia WSN is capable of retrieving and storing audio, video, and image contents from the physical environment~\cite{ref13,ref14,ref15}. 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 of 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.
106 \item \textbf{Mobile WSNs:}
107 Such a network is composed of mobile sensor nodes that can move autonomously so as to self-organize~\cite{ref16}.
108 %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.
109 Many challenges should be faced in mobile WSNs such as maintaining a sufficient sensing coverage and connectivity; the self-organization; the navigation and control of mobile sensors; mobility management; processing 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; search and rescue. The mobility in WSNs improves the network coverage of the monitored area especially after the initial random deployment because they can relocate themselves to fill coverage holes \cite{ref234,ref235}.
111 \item \textbf{Flying WSNs:}
112 This kind of WSN consists of low-cost wireless sensor nodes, which are embedded in Micro Aerial Vehicles (MAVs). They 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 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. A flying WSN provides a remote sensing and wireless networking platform 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 which is capable of flight; the wireless communication; designing software protocols to achieve semi-autonomous flight; and combining all the subsystems like propulsion, flight control, and wireless networking into a flying WSN.
116 \section{Applications}
118 %\indent The fast development in WSNs has been led to study their different characteristics extensively. However, the WSN is concentrated on various applications.
119 In this section, we describe different academic and commercial applications. A WSN can use 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, presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. Consequently, a wide range of WSN applications can be classified into five classes~\cite{ref22}, as shown in Figure~\ref{WSNAP}.
123 \includegraphics[scale=0.4]{Figures/ch1/WSNAP.eps}
124 \caption{Classification of WSN applications}
128 \begin{enumerate}[(I)]
130 \item \textbf{Health-care Applications:} There is an increasing interest and extensive research in this domain. 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 their health state and to allow medical staff to monitor and control the patient's status expeditiously. The most general used vital signs are electrocardiogram (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 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}.
133 \item \textbf{ Environment and agriculture Applications}
134 \indent Several WSNs applications have been developed for precision agriculture, cattle monitoring, and environmental monitoring. Precision agriculture refers to the science of using innovative and modern technologies to improve the crop production. WSNs are the main technology for developing 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 create a decision support system \cite{ref22}.
135 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}. For instance, 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).
136 \indent Various WSN applications for environmental monitoring have been used in coastline erosion, air quality monitoring, safe drinking water, and contamination control~\cite{ref30,ref22}.
138 %\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).
142 \item \textbf{Public safety and military Applications:}
143 WSNs can be incorporated into military command, control, communication, computing, intelligence,
144 surveillance, reconnaissance, and targeting systems. It permits to estimate the unexpected events such as natural disasters and threats; military surveillance to the battlefield, enemy forces, battle damage, and targeting; and nuclear, biological, and chemical attack detection and reconnaissance~\cite{ref19}.
146 \indent According to Figure~\ref{WSNAP}, the public safety and military applications can be categorized into active intervention and passive supervision~\cite{ref22}. In active intervention systems, the wireless sensors are worn by the agents and the WSN 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, 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.
149 \item \textbf{Transportation Systems Applications:}
150 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 have led to positive influence on mobility safety, ecology as well as economies. As a result, the transportation data collection and communication represent a major role in ITSs~\cite{ref37}.
152 \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 based traffic systems are used to detect, count, and to classify vehicles. In safety applications, the wireless sensors are used to deal with many cases such as driving safety \cite{ref41} and vehicle safety~\cite{ref38}. 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}.
155 \item \textbf{Industry Applications: Manufacturing and Smart Grids:}
156 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. In that case WSNs are incorporated in Supervisory Control and Data Acquisition (SCADA) systems and smart grids~\cite{ref22}. A SCADA system is a computer software by which industrial processes in factories are controlled and supervised. The wireless sensors are used with actuators to control the factory, to detect of liquid/gas leakages, and for 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.
159 %\section{Protocol Design Requirements}
162 \section{The Main Challenges}
164 \indent Many challenges need to be faced in WSNs and have therefore received increasing attention by a large number of researchers during the last few years.
165 %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}.
166 \begin{enumerate} [(I)]
168 \item \textbf{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.
170 \item \textbf{Coverage:} Another major challenge 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.
171 % in executing its tasks as long time as possible.
173 \item \textbf{Routing:} It 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 nodes in WSNs.
175 \item \textbf{Autonomous and Distributed Management:}
176 Since the nature of many WSN applications induces a deployment in a remote or hostile environment, it is important that the wireless sensor nodes work in an autonomous and distributed way to communicate and cooperate, without any human intervention since 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.
178 \item \textbf{Scalability:} Many physical phenomenons require the deployment of a dense WSN. A large number of sensor nodes may be needed for different reasons such as the huge size of the sensed area, the reliability requirement, or network lifetime prolongation. It is necessary that the proposed protocols for WSNs are scalable for these large number of sensor nodes in order to achieve their tasks efficiently.
181 \item \textbf{Reliability:} Many applications require high quality of services, connectivity, routing, data aggregation, etc. These applications need to deploy a large number of inexpensive sensor nodes so as to satisfy their requirements. This large number of the sensor nodes may be prone to 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.
183 \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 dynamic topology changes and sensor node failure due to energy depletion or malfunction.
185 \item \textbf{Heterogeneity:} Many applications require the use of different kinds of sensors modes. Thus WSN protocols should take into account different sensor node capabilities such as communication, processing, sensing, and energy. 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 of WSNs protocols.
187 \item \textbf{Wireless Networking:}
188 %The networking and wireless communication represent another important challenge in WSNs.
189 The communication range of 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 also increased transmission power. However, a long distance can be divided into several small distances using multi-hop communication. 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.
191 \item \textbf{Data Management:}
192 %It represents one of the challenges that contributes in depleting the energy of the sensor nodes in WSNs.
193 The main task of a 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 each sensor node wants 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. Obviously, a suitable data management is required to minimize the corresponding energy consumption.
195 \item \textbf{Security:} The sensitivity of the information collected by WSNs represents the final challenge that should be faced in WSNs. An information is susceptible to malicious intrusions and hacker attacks. As a consequence, it is necessary to provide energy efficient schemes to protect this information during the operation of WSNs.
201 \section{Energy-Efficient Mechanisms of a working WSN}
203 \indent The strong constraint on limiting wireless sensor nodes energy usage requires energy efficient mechanisms to prolong network lifetime. The energy efficient mechanisms can be classified into five categories~\cite{ref22}, as summarized in Figure~\ref{emwsn}.
206 \includegraphics[scale=0.4]{Figures/ch1/WSN-M.eps}
207 \caption{Energy-efficient mechanisms in Wireless Sensor Networks}
211 \subsection{Energy-Efficient Routing}
213 \indent Energy-efficient routing is a significant factor in the design of WSN protocols needed to satisfy the main constraints in hardware, power consumption, and other resources of wireless sensor nodes~\cite{ref42}. Many challenging factors need to be taken into consideration when 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.
216 \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 routing protocol in WSNs. The routing protocols should be concentrated on the remaining power of sensor nodes when 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, they are called energy-aware routing protocols~\cite{ref45,ref46}.
218 \subsubsection{Multipath Routing} Multipath routing represents an efficient strategy that provides reliability, security, and load balancing in order to forward packets in 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 protocols are summarized in~\cite{ref50,ref51}.
220 \subsection{Cluster Architecture}
222 \indent In this strategy, the wireless sensor nodes are grouped into several groups called clusters. Each group of wireless sensor nodes is 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}:
224 \begin{enumerate}[(a)]
225 \item Grouping wireless sensor nodes into clusters lead to decrease the communication range within the cluster. Therefore, the energy needed for communication among the nodes inside the cluster is minimized.
226 \item Minimizing the energy-hungry operations such as collaboration and aggregation to the cluster head.
227 %\item Limiting the number of communications (transmitting and receiving) due to the fusion operation carried out by the cluster head.
228 \item The continuous changing of cluster head according to the residual energy led to balance the energy consumption among wireless sensor nodes inside the cluster.
229 \item Some nodes can be turned off within the same cluster whilst the cluster head manage the responsibilities.
232 \indent In addition, the clustering supports network scalability in WSNs~\cite{ref43,ref44}. It provides an energy-efficient data aggregation by means of grouping the sensor nodes and organizing them hierarchically.
233 %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.
235 %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}.
238 \subsection{Scheduling Schemes}
240 \indent Many scheduling schemes have been suggested 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} sum-up the scheduling schemes in WSNs. In this figure, the scheduling schemes are classified into two main branches~\cite{ref56,ref57}:
242 \item Wake up scheduling aims to manage the wireless sensor node states (sleep/wake up) by selecting a set of time intervals for a sensor node to be awake.
243 \item Topology control in which a set of a wireless sensor nodes is chosen to be awake.
250 \includegraphics[scale=0.5]{Figures/ch1/WSN-S.pdf}
251 \caption{Scheduling schemes in Wireless Sensor Networks}
256 \subsubsection{Wake up Scheduling Schemes}
258 \indent This section describes 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 to send or receive 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 save the energy of node. Some schemes divide the time into equal length durations of time and are called slotted schemes. Other schemes work with the time in a 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. A wake-up schedule represents a set of a wake-up and sleep intervals which are produced for one period. This schedule is replicated for each period and it can be changed by the wake-up scheduling scheme during the different periods of time. The final goal is to permit to exchange data among the wireless sensor nodes during the wake-up interval. As shown in Figure~\ref{wsns}, the requirement for synchronization categorizes the wake-up scheduling into three categories~\cite{ref57}:
260 \begin{enumerate} [(I)]
262 \item \textbf{Synchronous Schemes:} A time synchronization among wireless sensor nodes is required.
263 %Several synchronous approaches have been suggested that based on the time synchronization in their work.
264 The majority of synchronous schemes work in periodic (cyclic) way by preparing the same wake-up schedule for every period unless a change is done by the scheduling algorithm.
265 %On the other hand, the aperiodic schemes do not apply the periodic schedule.
267 \begin{enumerate} [(A)]
268 \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 that an active wireless sensor node performs the communication (sending and receiving). This is from the 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 seems to be hard task. This level of 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 a cooperation~\cite{ref57}:
270 \begin{enumerate} [(i)]
271 \item Neighbor-coordinated is a scheme in which a wireless sensor node generates its own wake-up schedule taking into consideration the wake-up schedules of its neighbor sensor nodes.
272 %The protocols that used this approach like S-MAC protocol, Timeout MAC (T-MAC), Pattern-MAC (PMAC), Dynamic S-MAC (DSMAC), and ESC;
273 \item Path-coordinated is suggested to allow the wireless sensor nodes along the path to collaborate to manage their wake-up schedules in order to permit packets passing on the path without delay.
274 %Some examples used this approach~\cite{ref65,ref66,ref67};
275 \item Network-coordinated: the wireless sensor nodes cooperate 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 is responsible for constructing the wake-up schedule for a subset or all nodes in WSN. In a distributed scheme, every wireless sensor node contributes in the production of their wake-up schedules.
276 %Instances that used this approach in~\cite{ref68,ref69};
277 \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.
278 %Some examples that used these schemes~\cite{ref70,ref71}.
281 \item Aperiodic wakeup scheduling: each wireless sensor node decides its own schedule to wake up or sleep in each slot randomly.
282 %Examples that used this technique are proposed in~\cite{ref72,ref73}.
286 \item \textbf{Asynchronous Schemes:}
287 %The time among the wireless sensor nodes does not need synchronization.
288 A wireless sensor node wakes up to send packets without taking into account whether the receiving sensor nodes are waked up and ready to receive. These schemes do not need time synchronization which consumes energy~\cite{ref74}. They do not need to exploit the limited resources (processing, memory, and radio) of the sensor nodes because there are no shared wake up schedules to be exchanged or saved in the memory. Therefore, exchanging the packets among the wireless sensor nodes, which are not aware of each other's wake-up schedules, is a major challenge in asynchronous schemes. These schemes can been categorized into three groups~\cite{ref57}:
290 \begin{enumerate} [(A)]
291 \item Transmitter-initiated: a special frame is sent by the transmitting sensor node to inform the receiving sensor node that it has a data frame to send. If the receiving sensor node is hearing the special frame during one of its wake up intervals, the receiving node waits for sending the data frame by sender to receive it. The major advantage of these schemes is the low memory and processing requirements whilst the major disadvantages are low-duty-cycle and the non-deterministic sleep latency.
292 %Examples on these schemes in~\cite{ref75,ref76}.
294 \item Receiver-initiated: during each wake-up time interval of a receiving wireless sensor node, it sends a special frame to inform the senders, 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 waits for receiving special frame from neighboring sensor nodes. The waiting sensor node is sending its data frame when it receives 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.
295 %The works that proposed in~\cite{ref77,ref78} represents some instances for these approaches.
297 \item Combinatorial or random: during the wake-up duration, one or several data packets can be exchanged among the wireless sensor nodes. The exchanged data packets among the wireless sensor nodes are increased when the wake-up time is 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
300 %The proposed works in~\cite{ref81,ref82,ref83} give an instances for these schemes.
303 \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.
304 %The protocols in~\cite{ref79,ref80} are an examples on these schemes.
309 \subsubsection{Topology Control Schemes}
311 \indent The topology control schemes deal with the redundancy in WSNs. The WSN is sometimes deployed with high density and in a random way, where a large number of wireless sensor nodes are usually supposed to be thrown 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, the idea is to benefit from the saved energy 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~\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}:
313 \begin{enumerate} [(I)]
314 \item \textbf{Location Driven Protocols:} Wireless sensor nodes are turned on or off based on their location; for example, Geographical Adaptive Fidelity (GAF) protocol~\cite{GAF}.
316 %These schemes are called network coverage to describe 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}.
318 \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 in Span protocol~\cite{ref85}.
322 \subsection{Data-Driven Schemes}
324 \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 reduce the energy consumption in WSNs.
325 %Several data-driven schemes have been proposed in~\cite{ref86,ref87,ref88,ref89,ref90}.
326 Data driven schemes are classified into two main approaches~\cite{ref59,ref22}.
328 %\begin{enumerate} [(I)]
329 \subsubsection{Data Reduction Schemes}
330 Data reduction schemes deal with reducing the amount of data to be transmitted to a sink. They can be divided into stochastic approaches, time series forecasting, and algorithmic approaches. In stochastic approaches, physical phenomena are transformed using stochastic characterization. The aggregation by these protocols requires high processing. Therefore, it is feasible only on powerful sensor nodes with a big battery. In time series forecasting, the old values of periodic sampling can be used to forecast a future value in the same series.
331 %In algorithmic approaches, sensed phenomena is described using heuristic or state transition model.
333 \subsubsection{Energy Efficient Data Acquisition Schemes}
334 They concentrate on the energy consumption reduction in the sensing unit. These schemes are divided into adaptive sampling, hierarchical sampling, and model-based active sampling. In adaptive sampling, the amount of data acquired from the transducer can be reduced by spatial or temporal correlation between data. These approaches are more efficient to be used in centralized fusion, but they consume more energy due to requiring a high processing. Hierarchical sampling is more efficient when there are different types of sensors installed on the nodes. These approaches are more energy efficient and application specific. The model-based approaches are similar to data prediction schemes. These approaches aim to decrease the data samples by using computed models and to conserve the energy by means of data acquisition.
337 \subsection{Battery Repletion}
339 \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}.
341 \begin{enumerate} [i)]
342 \item{Energy Harvesting:} In energy harvesting, several sources of environmental energy have been developed so as to enable the wireless sensors to acquire energy from the surrounding environment. These energy sources are solar, wind energy, vibration based energy harvesting, radio signals for scavenging RF power, thermoelectric generators, and shoe-mounted piezoelectric generator to power artificial organs~\cite{ref59}.
344 \item{Wireless Charging:} In wireless charging, the power can be transmitted between the devices without requiring a connection between the transmitter and the receiver. These techniques participate in increasing the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed in two ways: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.
348 \subsection{Radio Optimization}
350 \indent In wireless sensor node, the radio is the most energy-consuming unit. 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
351 direction; and cognitive radio and cooperative communications schemes~\cite{ref22}.
353 \subsection{Relay nodes and Sink Mobility}
354 \indent The relay node placement and the mobility of the sink can be considered as energy-efficient strategies to minimize the energy consumption.
355 %\begin{enumerate} [(I)]
356 \subsubsection{Relay node placement}
357 In WSN, some wireless sensor nodes in a certain region may die and this creates a hole in the WSN. This problem can be solved by placing the wireless sensor nodes in sensing field by using an optimal distribution or by deploying a small number of relay wireless sensor nodes with powerful capabilities. The major goal of relay nodes is the communication with other wireless sensor nodes or relay nodes~\cite{ref52}. This solution can enhance the power balancing and avoid overloaded wireless sensor nodes in a particular region of a WSN.
359 \subsubsection{Sink Mobility}
360 In WSNs including a static sink, the wireless sensor nodes which are near the sink drain their power more rapidly compared with other sensor nodes, and this leads to WSN disconnection and limited network lifetime~\cite{ref53}. Sending all the data to the sink maximizes the overload on the sensor nodes near to the sink. In order to overcome this problem and prolong the network lifetime, we can use a mobile sink which moves within the area of interest to collect the sensory data from the static sensor nodes over a single hop communication. A mobile sink avoids the multi-hop communication and conserves the energy at the static sensor nodes near to the base station, extending the lifetime of WSN~\cite{ref54,ref55}.
369 \section{Network Lifetime}
371 %\indent 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.
373 %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.
375 \indent The network lifetime is defined in different contexts and used as a metric to evaluate the performance of the 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:
377 \begin{enumerate} [i.]
379 \item The time spent until the death of the first wireless sensor node ( or cluster head ) in the network due to its energy depletion~\cite{ref162,ref163}.
380 %\item The time spent by WSN and has at least a specific set $\beta$ of alive sensor nodes in WSN~\cite{ref164,ref165}.
381 \item The time spent until the death of all wireless sensor nodes in WSN because they have depleted their energy~\cite{ref124}.
382 \item For k-coverage, the time during which the area of interest is covered by at least $k$ sensor nodes~\cite{DESK}.
383 \item For 100 $\%$ coverage, the time spent by WSN in covering each target or the whole area by at least one sensor node~\cite{ref167}.
384 \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 the time spent by WSN until the coverage ratio becomes less than a predetermined threshold $\alpha$~\cite{ref169}.
385 \item The working time spent by the network before either the coverage ratio or delivery ratio become less than a predetermined threshold~\cite{ref170}.
386 \item The number of the successful data gathering trips~\cite{ref53}.
387 \item The number of sent packets~\cite{ref174}.
388 \item The percentage of wireless sensor nodes that have a route to the sink~\cite{ref170}.
389 \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}.
390 \item The elapsed time until losing the connectivity or the coverage~\cite{ref171}.
391 \item The time spent until arriving to a ratio lower than the acceptable event detection ratio in the network~\cite{ref124}.
392 \item The time during which the application requirement is satisfied~\cite{ref172}.
395 \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 particular circumstances of the environment.
396 %Accordingly, the current definitions are applicable for the WSNs that meet a particular conditions.
397 Many parameters affecting the network lifetime should be taken into account~\cite{ref92}: heterogeneity, node mobility, topology changes, application characteristics, quality of service, and completeness.
399 The network lifetime has been defined in this dissertation as the time elapsed until the coverage ratio becomes less than a predetermined threshold $\alpha$.
402 \section{Coverage in Wireless Sensor Networks }
404 %\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,
406 A major research challenge 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{ref102}:
407 \begin{enumerate}[(i)]
408 \item \textbf{Area coverage}~\cite{ref97,ref101} where every point inside an area has to be monitored. The work presented in this dissertation deals with this type of coverage.
409 \item \textbf{Target coverage}~\cite{ref98,ref101} where the main objective is to cover only a finite number of discrete points called targets.
410 \item \textbf{Barrier coverage}~\cite{ref99,ref100} where the main goal is to detect targets as they cross a barrier, which is usually a long belt region such as one can be found in intrusion detection and border surveillance applications.
413 \indent The sensing quality and capability can be assessed by a sensing coverage model obtained through the identification of a mathematical relationship between the point and the sensor node in the sensing field. In the real world, there are sometimes obstacles in the environment that affect the sensing range \cite{ref104}. Therefore, several sensing coverage models have been suggested according to application requirements and physical working environment such as~\cite{ref103}: boolean sector coverage, boolean disk coverage, attenuated disk coverage, truncated attenuated disk, detection coverage, and estimation coverage models. However, two main sensing coverage models have been used for simulating the performance of wireless sensors~\cite{ref104,ref105,ref106}:
415 \begin{enumerate}[(A)]
416 \item \textbf{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, the sensor node is not able to detect any point that is outside its sensing range. 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 at the position $(x_i,y_i)$. For any point P at the position $(x,y)$, 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
418 C_{xy}\left(s_i \right) = \left \{
420 1& \mbox{if $d(s_i,P)$ $<$ $R_s$,} \\
421 0 & \mbox{otherwise}\\
426 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.
429 \item \textbf{Probabilistic Sensing Model:} In reality, an event detection by a sensor node is imprecise. Hence, the coverage $C_{xy}$ requires to be represented in a probabilistic way. The probabilistic sensing model is more practical and can be used as an extension of the binary disc sensing model. 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
432 C_{xy}\left(s_i \right) = \left \{
434 1 & \mbox{if $d(s_i,P)$ $ \leqslant $ $R_s - R_u$} \\
435 \emph{e^{-\lambda\alpha^{\beta}}} & \mbox{if $R_s - R_u$ $ < $ $d(s_i,P)$ $ < $ $R_s + R_u$} \\
436 0 & \mbox{if $R_s + R_u$ $ \leq $ $d(s_i,P)$}\\
441 where $R_u$ is a measure of the uncertainty in sensor detection, $\alpha = d(s_i,P) - (R_s - R_u)$. $\lambda$ and $\beta$ are parameters that measure detection probability when a point P is at a distance greater than $R_u$, but within a distance from the sensor node $s_i$.
445 The coverage protocols proposed in this dissertation use the binary disc sensing model for each wireless sensor node in a WSN because it is widely used in the literature. Moreover, it is easy to formulate linear programs with it, whereas the probabilistic model is more complex. % and it is difficult to use it to create integer programs.
448 %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.
453 \section{Design Issues for Coverage Problems}
455 \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}:
457 \begin{enumerate}[(i)]
458 \item $\textbf{Coverage Type:}$ Is the WSN dedicated to monitor a whole area, to observe a set of targets, or to look for a barrier breach?
460 \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~\cite{ref107}. The method of placement can be selected based on the type of sensors, application, and the environment. In the deterministic placement, the deployment can be achieved in a friendly environment with a small number of sensor nodes. The random placement is preferred for a large number of sensor nodes or when the area of interest is inaccessible or hostile. The sensor network can be either dense or sparse. On the one hand, 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 low number of sensor nodes.
463 \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-covered if it is covered by at least K sensor nodes. Some applications need a high reliability to achieve their tasks. Therefore, the sensing field is deployed densely so as to perform a K-coverage for this field. The simple coverage problem consists of a coverage degree equal to one (i.e., K=1), where every point in the sensing field is covered by at least one sensor.
465 \item $\textbf{Coverage Ratio}$ is the percentage of the 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 is said as partial coverage.
467 \item $\textbf{Network Connectivity}$ ensures the existence of a path from any sensor node in WSN to the sink. A connected WSN ensures the sending of the sensed data from one sensor node to another sensor node directly toward the sink.
468 %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}.
470 %Activity based Scheduling schedules the activation and deactivation of sensor nodes during the network lifetime.
472 \item $\textbf{Activity based Scheduling}$ schedules the activation and deactivation of sensor nodes during the network lifetime. The basic objective is to decide which sensors are in which 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, using only local neighbor information. In centralized algorithms, a central controller (a node or base station) informs every sensor of the time intervals to be activated.
473 \textbf{This dissertation deals with activity based scheduling to ensure the best coverage}.
477 \section{Energy Consumption Model}
479 %\indent The WSNs have been received a lot of interests because the low energy consumption of the sensor nodes.
480 %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.
481 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 data acquisition. The main tasks of each of these states include:
483 \begin{enumerate}[(i)]
485 \item Computation: processing needed to execute any algorithm inside the sensor node. The processing that is required to physical communication and networking protocols is included in reception and transmission.
487 \item Transmission: processing for address determination, packetization, encoding, framing, and maybe queuing; supply for the baseband and RF circuitry.
489 \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.
491 \item Listening: Similar to reception except that the signal processing chain stops at the detection.
493 \item Data Acquisition: sensing, processing sensed data, analog to digital conversion, preprocessing, and maybe storing.
495 \item Sleeping: a low power level allowing the sensor node to stay alive.
501 %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.
504 %\subsection{Radio Energy Dissipation Model:}
505 %\label{ch1:sec9:subsec1}
506 \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.
509 \includegraphics[scale=0.4]{Figures/ch1/RDM.eps}
510 \caption{Radio energy dissipation model}
514 \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 across a distance d, the radio is
518 E_{tx}\left(K,d \right) = \left \{
520 \emph{ KE_{elec} + K \varepsilon_{fs}d^2} & \mbox{if $d < d_0$} \\
521 \emph{ KE_{elec} + K \varepsilon_{mp}d^4} & \mbox{if $d \geqslant d_0$}\\
526 while to receive a K-bit packet, the radio is
529 E_{rx}\left(k,d \right) = \emph{ KE_{elec} }.
533 \noindent 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 to $E_{DA}$ = 5 nJ/bit.
535 \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 and sensing units.
536 \textbf{In this dissertation, we have based the energy consumption model on \cite{ref112}. This model will be detailed in Section \ref{ch4:sec:04:03}}.
537 %\subsection{Our Energy Consumption Model:}
538 %\label{ch1:sec9:subsec2}
545 \indent In this chapter, an overview of the wireless sensor networks has been presented. Unlike traditional ad-hoc networks, WSNs are collaborative and very oriented toward a specific application domain. The structure of the typical wireless sensor network and the main components of the sensor nodes have been detailed. Several types of wireless sensor networks are described. Various fields of applications covering a wide spectrum including health, home, environmental, military, and industrial applications have been presented. As shown, since sensor nodes have limited battery life; since it is impossible to replace batteries, especially in remote and hostile environments; the limited power of a battery represents the critical challenge in WSNs. The main challenges in WSNs have been explained. Energy efficiency is the primary challenge to increase the network lifetime. Therefore, energy efficient solutions have been proposed in order to handle that challenge. Many energy efficient mechanisms have been illustrated, which are aimed to reduce the energy consumption of the different parts of the wireless sensor nodes. The definition of the network lifetime has been presented in different contexts. The problem of the coverage in WSNs is also explained.
546 %One of the main scientific research challenges in WSNs is how to build energy efficient coverage protocols.
547 This chapter highlights the main design issues that need to be considered when designing an energy efficient coverage protocol for WSNs. In addition, energy consumption models have been discussed.
