\section{Description of the DiLCO Protocol}
\label{ch4:sec:02}
-\noindent In this section, we introduce the DiLCO protocol which is distributed on each subregion in the area of interest. It is based on two efficient
-techniques: network leader election and sensor activity scheduling for coverage preservation and energy conservation, applied periodically to efficiently
-maximize the lifetime in the network.
+\noindent In this section, we introduce the DiLCO protocol which is distributed on each subregion in the area of interest. It is based on two efficient
+techniques: network leader election and sensor activity scheduling for coverage preservation and energy conservation, applied periodically to efficiently maximize the lifetime in the network.
\subsection{Assumptions and Network Model}
\label{ch4:sec:02:01}
-\noindent We consider a sensor network composed of static nodes distributed independently and uniformly at random. A high density deployment ensures a high
-coverage ratio of the interested area at the start. The nodes are supposed to have homogeneous characteristics from a communication and a processing point of
-view, whereas they have heterogeneous energy provisions. Each node has access to its location thanks, either to a hardware component (like a GPS unit), or a
-location discovery algorithm. Furthermore, we assume that sensor nodes are time synchronized in order to properly coordinate their operations to achieve complex sensing tasks~\cite{ref157}. The two sensor nodes have been supposed a neighbors if the euclidean distance between them is at most equal to 2$R_s$.
+\noindent We consider a sensor network composed of static nodes distributed independently and uniformly at random. A high-density deployment ensures a high coverage ratio of the interested area at the start. The nodes are supposed to have homogeneous characteristics from a communication and a processing point of view, whereas they have heterogeneous energy provisions. Each node has access to its location thanks, either to a hardware component (like a GPS unit) or a location discovery algorithm. Furthermore, we assume that sensor nodes are time synchronized in order to properly coordinate their operations to achieve complex sensing tasks~\cite{ref157}. The two sensor nodes have been supposed neighbors if the euclidean distance between them is at most equal to 2$R_s$.
-\indent We consider a boolean disk coverage model which is the most widely used sensor coverage model in the literature. Thus, since a sensor has a constant
-sensing range $R_s$, every space points within a disk centered at a sensor with the radius of the sensing range is said to be covered by this sensor. We also
-assume that the communication range $R_c$ is at least twice the sensing range $R_s$ (i.e., $R_c \geq 2R_s$). In fact, Zhang and Hou~\cite{ref126} proved that if the transmission range fulfills the previous hypothesis, a complete coverage of a convex area implies connectivity among the working nodes in the active mode. We assume that each sensor node can directly transmit its measurements to a mobile sink node. For example, a sink can be an unmanned aerial vehicle (UAV) is flying regularly over the sensor field to collect measurements from sensor nodes. A mobile sink node collects the measurements and transmits them to the base station.
+\indent We consider a boolean disk coverage model which is the most widely used sensor coverage model in the literature. Thus, since a sensor has a constant sensing range $R_s$, every space points within a disk centered at a sensor with the radius of the sensing range is said to be covered with this sensor. We also assume that the communication range $R_c$ is at least twice the sensing range $R_s$ (i.e., $R_c \geq 2R_s$). In fact, Zhang and Hou~\cite{ref126} proved that if the transmission range fulfills the previous hypothesis, a complete coverage of a convex area implies connectivity among the working nodes in the active mode. We assume that each sensor node can directly transmit its measurements toward a mobile sink node. For example, a sink can be an unmanned aerial vehicle (UAV) is flying regularly over the sensor field to collect measurements from sensor nodes. A mobile sink node collects the measurements and transmits them to the base station.
During the execution of the DiLCO protocol, two kinds of packet will be used:
\subsection{Primary Point Coverage Model}
\label{ch4:sec:02:02}
-\indent Instead of working with the coverage area, we consider for each
-sensor a set of points called primary points. We also assume that the
-sensing disk defined by a sensor is covered if all the primary points of
-this sensor are covered. By knowing the position (point center: ($p_x,p_y$)) of a wireless
-sensor node and its $R_s$, we calculate the primary points directly
-based on the proposed model. We use these primary points (that can be
-increased or decreased if necessary) as references to ensure that the
-monitored region of interest is covered by the selected set of
-sensors, instead of using all the points in the area.
+\indent Instead of working with the coverage area, we consider for each sensor a set of points called primary points. We also assume that the sensing disk defined by a sensor is covered if all the primary points of this sensor are covered. By knowing the position (point center: ($p_x,p_y$)) of a wireless sensor node and it's $R_s$, we calculate the primary points directly based on the proposed model. We use these primary points (that can be increased or decreased if necessary) as references to ensure that the monitored region of interest is covered by the selected set of sensors, instead of using all the points in the area.
\indent We can calculate the positions of the selected primary
points in the circle disk of the sensing range of a wireless sensor
node (see figure~\ref{fig1}) as follows:\\
+
$(p_x,p_y)$ = point center of wireless sensor node\\
$X_1=(p_x,p_y)$ \\
$X_2=( p_x + R_s * (1), p_y + R_s * (0) )$\\
\subsection{Main Idea}
\label{ch4:sec:02:03}
-\noindent We start by applying a divide-and-conquer algorithm to partition the
+\noindent We start by applying a divide-and-conquer algorithm to partition the
area of interest into smaller areas called subregions and then our protocol is
executed simultaneously in each subregion.
\begin{figure}[ht!]
\centering
-\includegraphics[scale=0.60]{Figures/ch4/FirstModel.pdf} % 70mm
+\includegraphics[scale=0.80]{Figures/ch4/FirstModel.pdf} % 70mm
\caption{DiLCO protocol}
\label{FirstModel}
\end{figure}
\subsection{Energy Consumption Model}
\label{ch4:sec:04:03}
-\indent In this dissertation, we used an energy consumption model proposed by~\cite{ref111} and based on \cite{ref112} with slight modifications. The energy consumption for sending/receiving the packets is added, whereas the part related to the sensing range is removed because we consider a fixed sensing range.
+\indent In this dissertation, we used an energy consumption model proposed by~\cite{DESK} and based on \cite{ref112} with slight modifications. The energy consumption for sending/receiving the packets is added, whereas the part related to the sensing range is removed because we consider a fixed sensing range.
\indent For our energy consumption model, we refer to the sensor node Medusa~II which uses an Atmels AVR ATmega103L microcontroller~\cite{ref112}. The typical architecture of a sensor is composed of four subsystems: the MCU subsystem which is capable of computation, communication subsystem (radio) which is responsible for transmitting/receiving messages, the sensing subsystem that collects data, and the power supply which powers the complete sensor node \cite{ref112}. Each of the first three subsystems can be turned on or off depending on the current status of the sensor. Energy consumption (expressed in milliWatt per second) for the different status of the sensor is summarized in Table~\ref{table1}.
\parskip 0pt
\begin{figure}[h!]
\centering
- \includegraphics[scale=0.6] {Figures/ch4/R1/CR.pdf}
+ \includegraphics[scale=0.8] {Figures/ch4/R1/CR.pdf}
\caption{Coverage ratio for 150 deployed nodes}
\label{Figures/ch4/R1/CR}
\end{figure}
Figure~\ref{Figures/ch4/R1/ASR} shows the average active nodes ratio for 150 deployed nodes.
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R1/ASR.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R1/ASR.pdf}
\caption{Active sensors ratio for 150 deployed nodes }
\label{Figures/ch4/R1/ASR}
\end{figure}
Figure~\ref{Figures/ch4/R1/SR} illustrates the percentage of stopped simulation runs per round for 150 deployed nodes.
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R1/SR.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R1/SR.pdf}
\caption{Percentage of stopped simulation runs for 150 deployed nodes }
\label{Figures/ch4/R1/SR}
\end{figure}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R1/EC95.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R1/EC95.pdf}
\caption{Energy Consumption for Lifetime95}
\label{Figures/ch4/R1/EC95}
\end{figure}
As shown in Figures~\ref{Figures/ch4/R1/EC95} and ~\ref{Figures/ch4/R1/EC50}, DiLCO-2 consumes more energy than the other versions of DiLCO, especially for large sizes of network. This is easy to understand since the bigger the number of sensors involved in the integer program, the larger the time computation to solve the optimization problem as well as the higher energy consumed during the communication.
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R1/EC50.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R1/EC50.pdf}
\caption{Energy Consumption for Lifetime50}
\label{Figures/ch4/R1/EC50}
\end{figure}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R1/T.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R1/T.pdf}
\caption{Execution Time (in seconds)}
\label{Figures/ch4/R1/T}
\end{figure}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R1/LT95.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R1/LT95.pdf}
\caption{Network Lifetime for $Lifetime95$}
\label{Figures/ch4/R1/LT95}
\end{figure}
Comparison shows that DiLCO-16 protocol, which uses 16 leaders, is the best one because it is used less number of active nodes during the network lifetime compared with DiLCO-32 protocol. It also means that distributing the protocol in each node and subdividing the sensing field into many subregions, which are managed independently and simultaneously, is the most relevant way to maximize the lifetime of a network.
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R1/LT50.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R1/LT50.pdf}
\caption{Network Lifetime for $Lifetime50$}
\label{Figures/ch4/R1/LT50}
\end{figure}
\parskip 0pt
\begin{figure}[h!]
\centering
- \includegraphics[scale=0.6] {Figures/ch4/R2/CR.pdf}
+ \includegraphics[scale=0.8] {Figures/ch4/R2/CR.pdf}
\caption{Coverage ratio for 150 deployed nodes}
\label{Figures/ch4/R2/CR}
\end{figure}
Figure~\ref{Figures/ch4/R2/ASR} shows the average active nodes ratio for 150 deployed nodes.
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R2/ASR.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R2/ASR.pdf}
\caption{Active sensors ratio for 150 deployed nodes }
\label{Figures/ch4/R2/ASR}
\end{figure}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R2/SR.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R2/SR.pdf}
\caption{Percentage of stopped simulation runs for 150 deployed nodes }
\label{Figures/ch4/R2/SR}
\end{figure}
In this experiment, we study the effect of increasing the primary points to represent the area of the sensor on the energy consumed by the wireless sensor network for different network densities. Figures~\ref{Figures/ch4/R2/EC95} and ~\ref{Figures/ch4/R2/EC50} illustrate the energy consumption for different network sizes for $Lifetime95$ and $Lifetime50$.
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R2/EC95.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R2/EC95.pdf}
\caption{Energy Consumption with $95\%-Lifetime$}
\label{Figures/ch4/R2/EC95}
\end{figure}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R2/EC50.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R2/EC50.pdf}
\caption{Energy Consumption with $Lifetime50$}
\label{Figures/ch4/R2/EC50}
\end{figure}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R2/T.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R2/T.pdf}
\caption{Execution Time(s) vs The Number of Sensors }
\label{Figures/ch4/R2/T}
\end{figure}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R2/LT95.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R2/LT95.pdf}
\caption{Network Lifetime for $Lifetime95$}
\label{Figures/ch4/R2/LT95}
\end{figure}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R2/LT50.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R2/LT50.pdf}
\caption{Network Lifetime for $Lifetime50$}
\label{Figures/ch4/R2/LT50}
\end{figure}
\parskip 0pt
\begin{figure}[h!]
\centering
- \includegraphics[scale=0.6] {Figures/ch4/R3/CR.pdf}
+ \includegraphics[scale=0.8] {Figures/ch4/R3/CR.pdf}
\caption{Coverage ratio for 150 deployed nodes}
\label{Figures/ch4/R3/CR}
\end{figure}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R3/ASR.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R3/ASR.pdf}
\caption{Active sensors ratio for 150 deployed nodes }
\label{Figures/ch4/R3/ASR}
\end{figure}
runs per round for 150 deployed nodes.
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R3/SR.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R3/SR.pdf}
\caption{Percentage of stopped simulation runs for 150 deployed nodes }
\label{Figures/ch4/R3/SR}
\end{figure}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R3/EC95.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R3/EC95.pdf}
\caption{Energy Consumption with $95\%-Lifetime$}
\label{Figures/ch4/R3/EC95}
\end{figure}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R3/EC50.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R3/EC50.pdf}
\caption{Energy Consumption with $Lifetime50$}
\label{Figures/ch4/R3/EC50}
\end{figure}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R3/LT95.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R3/LT95.pdf}
\caption{Network Lifetime for $Lifetime95$}
\label{Figures/ch4/R3/LT95}
\end{figure}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.6]{Figures/ch4/R3/LT50.pdf}
+\includegraphics[scale=0.8]{Figures/ch4/R3/LT50.pdf}
\caption{Network Lifetime for $Lifetime50$}
\label{Figures/ch4/R3/LT50}
\end{figure}
