\section{Types of Wireless Sensor Networks}
\label{ch1:sec:03}
-According to the physical phenomena for which the WSN is developed, several WSNs are deployed on the ground, underground and underwater, which suffer from different conditions and challenges. WSNs can be classified into six types, where five types of them presented in~\cite{ref4,ref5} and we added the sixth type. This dissertation is used the terrestrial WSN. Figure~\ref{wsnt} gives an examples for WSNs types.
+According to the physical phenomena for which the WSN is developed, several WSNs are deployed on the ground, underground and underwater, which suffer from different conditions and challenges. WSNs can be classified into six types, where five types of them presented in~\cite{ref4,ref5} and we added the sixth type. Figure~\ref{wsnt} gives an examples for WSNs types.
\begin{figure}[h!]
\centering
\includegraphics[scale=0.5]{Figures/ch1/typesWSN.pdf}
\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. The work in this dissertation concentrate on this type of 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. The work in this dissertation concentrate on this type of WSNs. This dissertation focused 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, so it needs a certain type of devices so as to provide a robust wireless communication underground, menace to devices come from unsuitable underground conditions, replace or recharge the battery seems to be impossible, and the WSN deployment is high costly.
\end{enumerate}
-
\section{Energy-Efficient Mechanisms in Wireless Sensor Networks}
\label{ch1:sec:06}
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.
\subsection{Energy-Efficient Routing}
The energy-efficient routing is a significant factor to 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}. There are many challenging factors need to be taken into consideration during designing a routing protocol for WSN, like: Limited energy capacity, Node deployment, Sensor location, Dynamic network, Hardware resource constraints, Data aggregation and gathering, Latency, Scalability, and Fault tolerance.
-\begin{enumerate} [(I)]
+
-\item \textbf{Energy as a routing metric:} lifetime maximization can be achieved by using the residual power of wireless sensor node as a routing metric and take it into account during executing the routing protocol in WSNs. So, the routing protocols should concentrate on the remaining power of sensor nodes during taking the decision to select the next hop toward the destination and not depend on the shortest path solution. It prioritizes routes on the basis of an energy metric (sometimes with other routing metrics) so it is called energy-aware routing protocols~\cite{ref45,ref46}.
+\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 and take it into account during executing the routing protocol in WSNs. So, the routing protocols should concentrate on the remaining power of sensor nodes during taking the decision to select the next hop toward the destination and not depend on the shortest path solution. It prioritizes routes on the basis of an energy metric (sometimes with other routing metrics) so it is called energy-aware routing protocols~\cite{ref45,ref46}.
-\item \textbf{Multipath routing:} efficient strategy that can provides reliability, security and load balancing in order to forward packets in a limited energy and constrained resources(computation, communication, and storage) networks like WSNs~\cite{ref50}. The single path routing is simple and scalable but it is not efficient for energy constrained networks such as WSNs . There are many multipath routing protocol are summarized in~\cite{ref50,ref51}.
+\subsubsection{Multipath Routing} efficient strategy that can provides reliability, security and load balancing in order to forward packets in a limited energy and constrained resources(computation, communication, and storage) networks like WSNs~\cite{ref50}. The single path routing is simple and scalable but it is not efficient for energy constrained networks such as WSNs . There are many multipath routing protocol are summarized in~\cite{ref50,ref51}.
-\end{enumerate}
+
-\subsection{Cluster architectures}
+\subsection{Cluster Architectures}
In this strategy, the wireless sensor nodes are grouped into several groups that called clusters, each group of wireless sensor nodes are managed by a single sensor node, which is called cluster head. The cluster head takes the responsibility of manging the activities of the wireless sensor nodes with the cluster and it communicates and coordinates with other cluster heads or the base station in the WSN. This mechanism conserves the energy in WSNs by means of~\cite{ref43,ref22}:
\begin{enumerate}[(a)]
\item The continuous changing of cluster head according to residual energy led to balancing 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}
-In addition, the clustering supports network scalability in WSNs~\cite{ref43,ref44}.
-
+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. There are 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 and CH selection, synchronization, data aggregation, repair mechanisms, and Quality of Service (QoS)~\cite{ref161}.
\end{figure}
-\subsubsection{Wake up Scheduling Schemes:}
+\subsubsection{Wake up Scheduling Schemes}
This section demonstrates the scheduling schemes from 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 sends or receives the packets. Whilst 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 that called slotted schemes; on the other hand, the other schemes works with the time in continuous way that 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 multiples of slots. The wake up schedule represents a set of a wake up and sleep intervals, which are produced for one period. Those 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 those wake up schedule is to permit to exchange of 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:
\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:}
+\subsubsection{Topology Control Schemes}
The topology control schemes are dealing with the redundancy in the WSNs. The WSN are 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)]
\end{enumerate}
-\subsection{Data-Driven Schemes:}
+\subsection{Data-Driven Schemes}
Data driven approaches aim to decrease the amount of data sent to the sink whilst maintaining the accuracy of sensing within acceptable level. So, removing unwanted data during the transmission and restriction the sensing tasks during data acquisition can be participating in reduce the energy consumption in WSNs.
%Several data-driven schemes have been proposed in~\cite{ref86,ref87,ref88,ref89,ref90}.
Data driven schemes classified into two main approaches~\cite{ref59,ref22}:
-\begin{enumerate} [(I)]
-\item \textbf{Data reduction schemes} that 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 aggregating by these protocols require high processing so 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.
-\item \textbf{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 a high energy due to requiring a high processing. In hierarchical sampling, are more efficient when there are different types of sensor 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}
+%\begin{enumerate} [(I)]
+\subsubsection{Data Reduction Schemes} that 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 aggregating by these protocols require high processing so 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 a high energy due to requiring a high processing. In hierarchical sampling, are more efficient when there are different types of sensor 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}
+In the last years, extensive researches have been focused on energy harvesting and wireless charging techniques. These solutions are representing alternate energy sources to recharge wireless sensor batteries without human intervention and instead of depending on the limited power supplied by a typical batteries~\cite{ref91,ref59}.
+\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}.
-\subsection{Battery Repletion:}
-In the last years, extensive researches have been focused on energy harvesting and wireless charging techniques. These solutions are representing alternate energy sources to recharge wireless sensor batteries without human intervention and instead of depending on the limited power supplied by a typical batteries~\cite{ref91,ref59}. In energy harvesting, several sources of environmental energy have been developed so as to enable the wireless sensors to acquire energy from the surrounding environment like solar, wind energy, vibration based energy harvesting, radio signals for scavenging RF power, Thermoelectric generators, and shoe-mounted piezoelectric generator to power artificial organs~\cite{ref59}. In wireless charging, the wireless power can be transmitted between the devices without requiring to the connection between the transmitter and the receiver. These techniques are participating in increasing the the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed by using two manners: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.
+\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 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}
direction; and cognitive radio and Cooperative communications schemes~\cite{ref22}.
\subsection{Relay nodes and Sink Mobility}
+The relay nodes 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 be died and this will leads to create a hole in the WSN. This problem can be solved by placing the wireless sensor nodes in sensing field using optimal distribution or by deploying a small number of relay wireless sensor nodes with a powerful capabilities whose major goal is the communication with other wireless sensor nodes or relay nodes~\cite{ref52}. This solution can enhance the power balancing and avoiding the overloaded wireless sensor nodes in a particular region in WSN.
-\begin{enumerate} [(I)]
-\item \textbf{Relay node placement:} in WSN, some wireless sensor nodes in a certain region may be died and this will leads to create a hole in the WSN. This problem can be solved by placing the wireless sensor nodes in sensing field using optimal distribution or by deploying a small number of relay wireless sensor nodes with a powerful capabilities whose major goal is the communication with other wireless sensor nodes or relay nodes~\cite{ref52}. This solution can enhance the power balancing and avoiding the overloaded wireless sensor nodes in a particular region in WSN.
+\subsubsection{Sink Mobility}
+In WSNs that included a static sink, the wireless sensor nodes, which are near the sink drain their power more rapidly compared with other sensor nodes that leads to WSN disconnection and limited network lifetime~\cite{ref53}. This is happening due to sending all the data in WSN to the sink that maximizes the overload on the wireless sensor nodes close to sink. In order to overcome this problem and prolong the network lifetime; it is necessary to use a mobile sink to move within the area of WSN so as to collect the sensory data from the static sensor nodes over a single hop communication. The mobile sink avoids the multi-hop communication and conserves the energy at the static sensor nodes close the base station, extending the lifetime of WSN~\cite{ref54,ref55}.
-\item \textbf{Sink Mobility:} in WSNs that included a static sink, the wireless sensor nodes, which are near the sink drain their power more rapidly compared with other sensor nodes that leads to WSN disconnection and limited network lifetime~\cite{ref53}. This is happening due to sending all the data in WSN to the sink that maximizes the overload on the wireless sensor nodes close to sink. In order to overcome this problem and prolong the network lifetime; it is necessary to use a mobile sink to move within the area of WSN so as to collect the sensory data from the static sensor nodes over a single hop communication. The mobile sink avoids the multi-hop communication and conserves the energy at the static sensor nodes close the base station, extending the lifetime of WSN~\cite{ref54,ref55}.
-
-\end{enumerate}
+%\end{enumerate}
