\begin{figure}[h!]
\centering
-\includegraphics[scale=0.5]{Figures/ch1/twsn2.pdf}
+\includegraphics[scale=0.52]{Figures/ch1/twsn2.pdf}
\caption{ Components of a typical wireless sensor node.}
\label{twsn}
\end{figure}
\label{wsn}
\end{figure}
-The sensor node use software layer that logically locates between the node's hardware and the application called, An operating system (OS)~\cite{ref18}. 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 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.
+The sensor node use a software layer called, Operating System (OS), is logically locates 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 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}
\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. 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 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.
+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.
\end{enumerate}
%\section{Protocol Design Requirements}
\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.
\item \textbf{Autonomous and Distributed Management:}
-Since the nature of many WSN applications induce 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.
+Since the nature of many WSN applications induce 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.
\item \textbf{Scalability:} Many physical phenomenons require the deployment of a dense WSN. A large number of sensor nodes maybe 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.
-
-\item \textbf{Reliability:} Many applications require high quality of coverage, 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.
+\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.
\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.
\section{Energy-Efficient Mechanisms of a working WSN}
\label{ch1:sec:06}
-\indent The strong constraint on limiting wireless sensor nodes energy usage demand 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}.
+\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}.
\begin{figure}[h!]
\centering
\includegraphics[scale=0.4]{Figures/ch1/WSN-M.eps}
\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 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}:
\begin{enumerate} [(i)]
-\item Neighbor-coordinated is in which a wireless sensor node generates its own wake-up schedule taking into consideration the wake-up schedules of its neighbor sensor nodes.
+\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.
%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 in order to permit packets passing on the path without delay.
%Some examples used this approach~\cite{ref65,ref66,ref67};
\item \textbf{Asynchronous Schemes:}
%The time among the wireless sensor nodes does not need synchronization.
-The wireless sensor node wakes up to send packets without taking into account whether the receiving sensor nodes are waked up and ready to receive. These schemes do not need time synchronization which consumes energy~\cite{ref74}. They do not need to exploit the limited resources (processing, memory, and radio) of the sensor nodes because there are no shared wake up schedules to be exchanged or saved in the memory. Therefore, exchanging the packets among the wireless sensor nodes, which are not aware of each other's wake-up schedules, is have considered as a major challenge in asynchronous schemes. These schemes can been categorized into three groups~\cite{ref57}:
+The wireless sensor node wakes up to send packets without taking into account whether the receiving sensor nodes are waked up and ready to receive. These schemes do not need time synchronization which consumes energy~\cite{ref74}. They do not need to exploit the limited resources (processing, memory, and radio) of the sensor nodes because there are no shared wake up schedules to be exchanged or saved in the memory. Therefore, exchanging the packets among the wireless sensor nodes, which are not aware of each other's wake-up schedules, is a major challenge in asynchronous schemes. These schemes can been categorized into three groups~\cite{ref57}:
\begin{enumerate} [(A)]
\item Transmitter-initiated: a special frame is sent by the transmitting sensor node to inform the receiving sensor node that it has a data frame to send. If the receiving sensor node is hearing the special frame during one of its wake up intervals, the receiving node waits for sending the data frame by sender to receive it. The major advantage of these schemes is the low memory and processing requirements whilst the major disadvantages are low-duty-cycle and the non-deterministic sleep latency.
%\begin{enumerate} [(I)]
\subsubsection{Data Reduction Schemes}
-Data reduction schemes deal with reducing the amount of data to be transmitted to a sink. They can be divided into stochastic approaches, time series forecasting, and algorithmic approaches. In stochastic approaches, physical phenomena are transformed using stochastic characterization. The aggregation by these protocols requires high processing. Therefore, it is feasible only on powerful sensor nodes with a big battery. In time series forecasting, the old values of periodic sampling can be used to forecast a future value in the same series. In algorithmic approaches, sensed phenomena is described using heuristic or state transition model.
+Data reduction schemes deal with reducing the amount of data to be transmitted to a sink. They can be divided into stochastic approaches, time series forecasting, and algorithmic approaches. In stochastic approaches, physical phenomena are transformed using stochastic characterization. The aggregation by these protocols requires high processing. Therefore, it is feasible only on powerful sensor nodes with a big battery. In time series forecasting, the old values of periodic sampling can be used to forecast a future value in the same series.
+%In algorithmic approaches, sensed phenomena is described using heuristic or state transition model.
\subsubsection{Energy Efficient Data Acquisition Schemes}
They concentrate on the energy consumption reduction in the sensing unit. These schemes are divided into adaptive sampling, hierarchical sampling, and model-based active sampling. In adaptive sampling, the amount of data acquired from the transducer can be reduced by spatial or temporal correlation between data. These approaches are more efficient to be used in centralized fusion, but they consume more energy due to requiring a high processing. Hierarchical sampling is more efficient when there are different types of sensors installed on the nodes. These approaches are more energy efficient and application specific. The model-based approaches are similar to data prediction schemes. These approaches aim to decrease the data samples by using computed models and to conserve the energy by means of data acquisition.
\indent In the last years, extensive researches have been focused on energy harvesting and wireless charging techniques. These solutions represent alternate energy sources to recharge wireless sensor batteries without human intervention~\cite{ref91,ref59}.
-\subsubsection{Energy Harvesting} In energy harvesting, several sources of environmental energy have been developed so as to enable the wireless sensors to acquire energy from the surrounding environment. These energy sources are solar, wind energy, vibration based energy harvesting, radio signals for scavenging RF power, thermoelectric generators, and shoe-mounted piezoelectric generator to power artificial organs~\cite{ref59}.
+\begin{enumerate} [i)]
+\item{Energy Harvesting:} In energy harvesting, several sources of environmental energy have been developed so as to enable the wireless sensors to acquire energy from the surrounding environment. These energy sources are solar, wind energy, vibration based energy harvesting, radio signals for scavenging RF power, thermoelectric generators, and shoe-mounted piezoelectric generator to power artificial organs~\cite{ref59}.
+
+\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}.
-\subsubsection{Wireless Charging}In wireless charging, the power can be transmitted between the devices without requiring a connection between the transmitter and the receiver. These techniques participate in increasing the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed in two ways: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.
+\end{enumerate}
\subsection{Radio Optimization}
In WSN, some wireless sensor nodes in a certain region may die and this creates a hole in the WSN. This problem can be solved by placing the wireless sensor nodes in sensing field by using an optimal distribution or by deploying a small number of relay wireless sensor nodes with powerful capabilities. The major goal of relay nodes is the communication with other wireless sensor nodes or relay nodes~\cite{ref52}. This solution can enhance the power balancing and avoid overloaded wireless sensor nodes in a particular region of a WSN.
\subsubsection{Sink Mobility}
-In WSNs including a static sink, the wireless sensor nodes which are near the sink drain their power more rapidly compared with other sensor nodes, and this leads to WSN disconnection and limited network lifetime~\cite{ref53}. Sending all the data to the sink maximizes the overload on the sensor nodes near to the sink. In order to overcome this problem and prolong the network lifetime, a solution is to use a mobile sink moving within the area of interest so as to collect the sensory data from the static sensor nodes over a single hop communication. A mobile sink avoids the multi-hop communication and conserves the energy at the static sensor nodes near to the base station, extending the lifetime of WSN~\cite{ref54,ref55}.
+In WSNs including a static sink, the wireless sensor nodes which are near the sink drain their power more rapidly compared with other sensor nodes, and this leads to WSN disconnection and limited network lifetime~\cite{ref53}. Sending all the data to the sink maximizes the overload on the sensor nodes near to the sink. In order to overcome this problem and prolong the network lifetime, we can use a mobile sink which moves within the area of interest to collect the sensory data from the static sensor nodes over a single hop communication. A mobile sink avoids the multi-hop communication and conserves the energy at the static sensor nodes near to the base station, extending the lifetime of WSN~\cite{ref54,ref55}.
\item \textbf{Barrier coverage}~\cite{ref99,ref100} where the main goal is to detect targets as they cross a barrier, which is usually a long belt region such as one can be found in intrusion detection and border surveillance applications.
\end{enumerate}
-\indent The sensing quality and capability can be assessed by a sensing coverage model of trained through the identification of a mathematical relationship between the point and the sensor node in the sensing field. In the real world, there are sometimes obstacles in the environment that affect the sensing range \cite{ref104}. Therefore, several sensing coverage models have been suggested according to application requirements and physical working environment such as~\cite{ref103}: boolean sector coverage, boolean disk coverage, attenuated disk coverage, truncated attenuated disk, detection coverage, and estimation coverage Models. However, two main sensing coverage models have been used for simulating the performance of wireless sensors~\cite{ref104,ref105,ref106}:
+\indent The sensing quality and capability can be assessed by a sensing coverage model obtained through the identification of a mathematical relationship between the point and the sensor node in the sensing field. In the real world, there are sometimes obstacles in the environment that affect the sensing range \cite{ref104}. Therefore, several sensing coverage models have been suggested according to application requirements and physical working environment such as~\cite{ref103}: boolean sector coverage, boolean disk coverage, attenuated disk coverage, truncated attenuated disk, detection coverage, and estimation coverage models. However, two main sensing coverage models have been used for simulating the performance of wireless sensors~\cite{ref104,ref105,ref106}:
\begin{enumerate}[(A)]
-\item \textbf{Binary Disc Sensing Model:}
-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
+\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
\begin{equation}
C_{xy}\left(s_i \right) = \left \{
\begin{array}{l l}
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{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
+\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
\begin{equation}
C_{xy}\left(s_i \right) = \left \{
\end{enumerate}
-\section{Energy Consumption Modeling}
+\section{Energy Consumption Model}
\label{ch1:sec:9}
%\indent The WSNs have been received a lot of interests because the low energy consumption of the sensor nodes.
%One of the most critical issues in WSNs is to reduce the energy consumption of the limited power battery of the sensor nodes so as to prolong the network lifetime as long as possible.
\section{Conclusion}
\label{ch1:sec:10}
-\indent In this chapter an overview of the wireless sensor networks has been presented. Unlike traditional ad-hoc networks, WSNs are collaborative and very oriented toward a specific application domain. The structure of the typical wireless sensor network and the main components of the sensor nodes have been detailed. Several types of wireless sensor networks are described. Various fields of applications covering a wide spectrum including health, home, environmental, military, and industrial applications have been presented. As shown, since sensor nodes have limited battery life; since it is impossible to replace batteries, especially in remote and hostile environments; the limited power of a battery represents the critical challenge in WSNs. The main challenges in WSNs have been explained. Energy efficiency is the primary challenge to increase the network lifetime. Therefore, energy efficient solutions have been proposed in order to handle that challenge. Many energy efficient mechanisms have been illustrated, which are aimed to reduce the energy consumption of the different parts of the wireless sensor nodes. The definition of the network lifetime has been presented in different contexts. The problem of the coverage in WSNs is also explained.
+\indent In this chapter, an overview of the wireless sensor networks has been presented. Unlike traditional ad-hoc networks, WSNs are collaborative and very oriented toward a specific application domain. The structure of the typical wireless sensor network and the main components of the sensor nodes have been detailed. Several types of wireless sensor networks are described. Various fields of applications covering a wide spectrum including health, home, environmental, military, and industrial applications have been presented. As shown, since sensor nodes have limited battery life; since it is impossible to replace batteries, especially in remote and hostile environments; the limited power of a battery represents the critical challenge in WSNs. The main challenges in WSNs have been explained. Energy efficiency is the primary challenge to increase the network lifetime. Therefore, energy efficient solutions have been proposed in order to handle that challenge. Many energy efficient mechanisms have been illustrated, which are aimed to reduce the energy consumption of the different parts of the wireless sensor nodes. The definition of the network lifetime has been presented in different contexts. The problem of the coverage in WSNs is also explained.
%One of the main scientific research challenges in WSNs is how to build energy efficient coverage protocols.
This chapter highlights the main design issues that need to be considered when designing an energy efficient coverage protocol for WSNs. In addition, energy consumption models have been discussed.
\ No newline at end of file
