\setbeamertemplate{section in toc}[sections numbered]
\setbeamertemplate{subsection in toc}[subsections numbered]
-
+\pagenumbering{roman}
\AtBeginSection[]
{
\begin{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 02 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame} {Problem Definition, Solution, and Objectives}
+\begin{frame} {Problem definition and solution}
\vspace{-3.5em}
\begin{figure}
- \includegraphics[width=0.475\textwidth]{Figures/6}
+ \includegraphics[width=0.495\textwidth]{Figures/6}
\hfill
% \includegraphics[width=0.475\textwidth]{Figures/8}
% \hfill
- \includegraphics[width=0.475\textwidth]{Figures/10}
+ \includegraphics[width=0.495\textwidth]{Figures/10}
% \hfill
% \includegraphics[width=0.475\textwidth]{Figures/13}
\end{figure}
- \begin{block}{\textcolor{white}{ MAIN QUESTION?}}
- How to reduce the redundancy while coverage preservation for prolong the network lifetime continuously and effectively when monitoring a certain area of interest?
+ \begin{block}{\textcolor{white}{MAIN QUESTION}}
+ \textcolor{black}{How to minimize the energy consumption and extend the network lifetime when covering a certain area?}
\end{block}
\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 03 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{Problem Definition, Solution, and Objectives}
+\begin{frame}{Problem definition and solution}
-\begin{block}{\textcolor{white}{OUR SOLUTION}}
-The area of interest is divided into subregions using a divide-and conquer method and then combine two efficient techniques :
+\begin{block}{\textcolor{white}{OUR SOLUTION $\blacktriangleright$ Distributed optimization process}}
+\begin{enumerate} [i)]
+\item \bf \textcolor{black}{Division into subregions}
+\item \bf \textcolor{black}{For each subregion}
+\end{enumerate}
+
\begin{itemize}
- \item Leader Election for each subregion.
- % \item Activity Scheduling based optimization is planned for each subregion.
+ \item \bf \textcolor{magenta}{Leader election}
+ \item \bf \textcolor{magenta}{Activity Scheduling based optimization}
\end{itemize}
- \end{block}
+ \end{block}
+\vspace{-1.5em}
\begin{figure}
- \includegraphics[width=0.475\textwidth]{Figures/div}
- \hfill
\includegraphics[width=0.475\textwidth]{Figures/div2}
+ \hfill
+ \includegraphics[width=0.475\textwidth]{Figures/act2}
\end{figure}
\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 03.1 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{Problem Definition, Solution, and Objectives}
-
-\begin{block}{\textcolor{white}{OUR SOLUTION}}
- \begin{itemize}
- %\item Leader Election for each subregion.
- \item Activity Scheduling based optimization is planned for each subregion.
- \end{itemize}
-
- \end{block}
-\begin{figure}
- \includegraphics[width=0.775\textwidth]{Figures/act}
-
-\end{figure}
-
-\end{frame}
+%\begin{frame}{Problem Definition, Solution, and Objectives}
+%
+%\begin{block}{\textcolor{white}{OUR SOLUTION}}
+% \begin{itemize}
+% %\item Leader Election for each subregion.
+% \item \bf \textcolor{magenta}{Activity Scheduling based optimization is planned for each subregion.}
+% \end{itemize}
+%
+% \end{block}
+%\begin{figure}
+% \includegraphics[width=0.775\textwidth]{Figures/act}
+%
+%\end{figure}
+%
+%\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 03.2 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{Problem Definition, Solution, and Objectives}
-
-\begin{block}{\textcolor{white}{Dissertation Objectives}}
-Develop energy-efficient distributed optimization protocols that should be able to:
- \begin{itemize}
- \item Schedule node activities by optimize both coverage and lifetime.
- \item Combine two efficient techniques: leader election and sensor activity scheduling.
- \item Perform a distributed optimization process.
- \end{itemize}
-
- \end{block}
-
-
-\end{frame}
+%\begin{frame}{Problem Definition, Solution, and Objectives}
+%
+%\begin{block}{\bf \textcolor{white}{Dissertation Objectives}}
+%\bf \textcolor{black}{Develop energy-efficient distributed optimization protocols that should be able to:}
+% \begin{itemize}
+% \item \bf \textcolor{blue}{Schedule node activities by optimize both coverage and lifetime.}
+% \item \bf \textcolor{blue}{Combine two efficient techniques: leader election and sensor activity scheduling.}
+% \item \bf \textcolor{blue}{Perform a distributed optimization process.}
+% \end{itemize}
+%
+% \end{block}
+%
+%
+%\end{frame}
%%%%%%%%%%%%%%%%%%%%
\begin{femtoBlock}
{Sensor \\}
\begin{itemize}
- \item Electronic Low-cost tiny device.
- \item Sense, process and transmit data.
- \item Limited energy, memory and processing capabilities.
+ \item Electronic low-cost tiny device
+ \item Sense, process and transmit data
+ \item Limited energy, memory and processing capabilities
\end{itemize}
\end{femtoBlock}
\begin{figure}[!t]
\includegraphics[height = 2cm]{Figures/sn.jpg}
\end{figure}
-
-
- % \begin{femtoBlock} {}% {SOME APPLICATIONS OF WSNs \\}
-
-% \includegraphics[height =1 cm]{1.png}
-% \includegraphics[height =1cm]{2.png}\\
-% \includegraphics[height =1cm]{5.jpg}
-% \includegraphics[height = 1cm]{traffic.jpg}
-% \includegraphics[height = 1cm]{3.png}
-%
-
- % \end{femtoBlock}
+
\end{columns}
\includegraphics[height = 5cm]{Figures/WSN-M.pdf}
\end{figure}
+ \vspace{-1.0em}
+ \bf \textcolor{blue} {Our approach includes cluster architecture and scheduling schemes}
\end{frame}
%\begin{frame}{Energy-Efficient Mechanisms of a working WSN}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 10 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{Network Lifetime}
+\begin{frame}{Network lifetime}
\vspace{-1.5em}
-\begin{block}{\textcolor{white} {Some network lifetime defintions:}}
+\begin{block}{\textcolor{white} {Some definitions}}
+\small
\begin{enumerate}[i)]
-\item \small Time spent until death of the first sensor ( or cluster head ).
-\item Time spent until death of all wireless sensor nodes in WSN.
-\item Time spent by WSN in covering each target by at least one sensor.
-\item Time during which the area of interest is covered by at least k nodes.
-\item Elapsed time until losing the connectivity or the coverage.
+\item \textcolor{black} {Time spent until death of the first sensor (or cluster head)}
+\item \textcolor{black} {Time spent until death of all wireless sensor nodes in WSN}
+\item \textcolor{black} {Time spent by WSN in covering each target by at least one sensor}
+\item \textcolor{black} {Time during which the area of interest is covered by at least k nodes}
+\item \textcolor{black} {Elapsed time until losing the connectivity or the coverage}
+\item \bf \textcolor{red} {Time elapsed until the coverage ratio becomes less than a predetermined threshold $\alpha$}
\end{enumerate}
\end{block}
-\begin{block}{\textcolor{white} {Network lifetime In this dissertation:}}
-Time elapsed until the coverage ratio becomes less than a predetermined threshold $\alpha$.
-\end{block}
+%\begin{block}{\textcolor{white} {Network lifetime In this dissertation:}}
+%\textcolor{blue} {Time elapsed until the coverage ratio becomes less than a predetermined threshold $\alpha$.}
+%\end{block}
\end{frame}
%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Coverage in Wireless Sensor Networks}
-\begin{block} <1-> {\textcolor{white} {Coverage Definition:}}
-\textcolor{blue} {Coverage} reflects how well a sensor field is monitored efficiently using as less energy as possible.
+\begin{block} <1-> {\textcolor{white} {Coverage definition}}
+\textcolor{blue} {Coverage} reflects how well a sensor field is monitored efficiently using as less energy as possible
\end{block}
-\begin{block} <2-> {\textcolor{white} {Coverage Types:}}
-\begin{enumerate}
-\item \small \textcolor{blue} {Area coverage:} every point inside an area has to be monitored.
-\item \textcolor{blue} {Target coverage:} is to cover only a finite number of discrete points called targets.
+\begin{block} <2-> {\textcolor{white} {Coverage types}}
+\begin{enumerate}[i)]
+\item \small \textcolor{red} {Area coverage $\blacktriangleright$ every point inside an area has to be monitored}
+\item \textcolor{blue} {Target coverage} $\blacktriangleright$ only a finite number of discrete points called targets have to be monitored
-\item \textcolor{blue} {Barrier coverage:} is to detect targets as they cross a barrier such as in intrusion detection and border surveillance applications.
+\item \textcolor{blue} {Barrier coverage} $\blacktriangleright$ detection of targets as they cross a barrier such as in intrusion detection and border surveillance applications
\end{enumerate}
\end{block}
-\begin{block} <3-> {\textcolor{white} {Coverage type in this dissertation:}}
-The work presented in this dissertation deals with area coverage.
-\end{block}
+%\begin{block} <3-> {\textcolor{white} {Coverage type in this dissertation:}}
+%The work presented in this dissertation deals with \textcolor{red} {area coverage}.
+%\end{block}
\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 11 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{Existing Works}
+\begin{frame}{Existing works}
\vspace{-0.3em}
-\begin{block} {\textcolor{white} {Coverage Approaches:}}
-Most existing coverage approaches in literature classified into
-\begin{enumerate}[A)]
-\item Full centralized coverage algorithms.
+\begin{block} {\textcolor{white} {Coverage approaches}}
+%Most existing coverage approaches in literature classified into
+\begin{enumerate}[i)]
+\item \textcolor{blue} { Full centralized coverage algorithms}
\begin{itemize}
- \item Optimal or near optimal solution.
- \item low computation power for the sensors (except for base station).
- \item High communication overhead.
- \item Not scalable for large WSNs.
+ \item Optimal or near optimal solution
+ \item Low computation power for the sensors (except for base station)
+ \item Higher energy consumption for communication in large WSN
+ \item Not scalable for large WSNs
\end{itemize}
-\item Full distributed coverage algorithms.
+\item \textcolor{blue} {Full distributed coverage algorithms}
+ \begin{itemize}
+ \item Lower quality solution
+ \item Less energy consumption for communication in large WSN
+ \item Reliable and scalable for large WSNs
+ \end{itemize}
+ \item \textcolor{red} {Hybrid approaches}
\begin{itemize}
- \item Lower quality solution.
- \item High communication overhead especially for dense WSNs.
- \item Reliable and scalable for large WSNs.
+ \item \textcolor{red} {Globally distributed and locally centralized}
\end{itemize}
+
\end{enumerate}
\end{block}
-\begin{block} {\textcolor{white} {Coverage protocols in this dissertation:}}
-The protocols presented in this dissertation combine between the two above approaches.
-\end{block}
+%\begin{block} {\textcolor{white} {Coverage protocols in this dissertation:}}
+%The protocols presented in this dissertation combine between the two above approaches.
+%\end{block}
\end{frame}
+\begin{frame}{Existing works $\blacktriangleright$ DESK algorithm (Vu et al.)}
+\vspace{-1.5em}
+\begin{figure}[!t]
+ \includegraphics[height = 4.0cm]{Figures/DESK.eps}
+ \end{figure}
+ \vspace{-2.5em}
+
+ \begin{itemize}
+ \item Requires only one-hop neighbor information (fully distributed)
+ \item Each sensor decides its status (Active or Sleep) based on the perimeter coverage model, without optimization
+
+\end{itemize}
-%%%%%%%%%%%%%%%%%%%%
-%% SLIDE 12 %%
-%%%%%%%%%%%%%%%%%%%%
-\section{\small {Distributed Lifetime Coverage Optimization Protocol (DiLCO)}}
+%\tiny \bf \textcolor{blue}{DESK is chosen for comparison because it works into rounds fashion similar to our approaches, as well as DESK is a full distributed coverage approach.}
-%%%%%%%%%%%%%%%%%%%%
-%% SLIDE 13 %%
-%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small DiLCO Protocol $\blacktriangleright$ Assumptions and Network Model:}
-\vspace{-0.5cm}
+\end{frame}
-\begin{femtoBlock} {} %{Assumptions and Network Model:}
-
- \begin{columns}[c]
-
- \column{.50\textwidth}
-
- \vspace{-1.0cm}
+\begin{frame}{Existing works $\blacktriangleright$ GAF algorithm (Xu et al.)}
+
+\vspace{-3.3em}
+ \begin{columns}[c]
+
+\column{.58\textwidth}
+
+ \begin{figure}[!t]
+ \includegraphics[height = 2.7cm]{Figures/GAF1.eps}
+ \end{figure}
+ \vspace{-2.5em}
+ \begin{figure}[!t]
+ \includegraphics[height = 3.3cm]{Figures/GAF2.eps}
+ \end{figure}
+
+ \column{.52\textwidth}
+ \vspace{1.2em}
+\small
+ \begin{itemize}
+ \item Distributed energy-based scheduling approach
+ \item Uses geographic location information to divide the area into a fixed square grids
+ \item Nodes are in one of three sates $\blacktriangleright$ discovery, active, or sleep
+ \item Only one node staying active in grid
+ \item The fixed grid is square with r units on a side
+ \item Nodes cooperate within each grid to choose the active node
+ \end{itemize}
- \begin{enumerate} [$\divideontimes$]
- \item Static Wireless Sensors.
- \item Uniform deployment.
- \item High density deployment.
- \item Homogeneous in terms of:
+
+
+% \begin{itemize}
+% \item \tiny enat: estimated node active time
+% \item enlt: estimated node lifetime
+% \item Td,Ta, Ts: discovery, active, and sleep timers
+% \item Ta = enlt/2
+% \item Ts = [enat/2, enat]
+% \end{itemize}
+
+
+
+\end{columns}
+
+\vspace{1.0em}
+
+%\tiny \bf \textcolor{blue}{GAF is chosen for comparison because it is famous and easy to implement, as well as many authors referred to it in many publications.}
+\end{frame}
+
+\section{\small {The main scheme for our protocols}}
+
+
+\begin{frame}{Assumptions for our protocols}
+\vspace{-0.1cm}
+
+\begin{enumerate} [$\divideontimes$]
+ \item Static wireless sensor, homogeneous in terms of
\begin{itemize}
- \item Sensing, Communication, and Processing capabilities
+ \item Sensing, communication, and processing capabilities
\end{itemize}
- \item Heterogeneous Energy.
- \item Its $R_c\geq 2R_s$.
- \item Multi-hop communication.
- \item Know Its location by:
+ \item Heterogeneous initial energy
+ \item High density uniform deployment
+ \item $R_c\geq 2R_s$ complete coverage $\Rightarrow$ connectivity (proved by Zhang and Zhou)
+
+ \item Multi-hop communication
+ \item Known location by
\begin{itemize}
- \item Embedded GPS or
- \item Location Discovery Algorithm.
+ \item Embedded GPS or location discovery algorithm
\end{itemize}
- \end{enumerate}
-
-
-
- \column{.50\textwidth}
- \begin{enumerate} [$\divideontimes$]
- \item Using two kinds of packet:
+
+ \item Using two kinds of packets
\begin{itemize}
- \item INFO packet.
- \item ActiveSleep packet.
+ \item INFO packet
+ \item ActiveSleep packet
\end{itemize}
- \item Five status for each node:
+ \item Five status for each node
\begin{itemize}
- \item LISTENING, ACTIVE, SLEEP, COMPUTATION, and COMMUNICATION.
+ \item \small LISTENING, ACTIVE, SLEEP, COMPUTATION, and COMMUNICATION
\end{itemize}
- \end{enumerate}
-
- \begin{femtoBlock} { \small Primary point coverage model}
- \vspace{-1.2cm}
- \begin{center}
- \includegraphics[height = 4.0cm]{Figures/fig21.pdf}
-
- \end{center}
- \end{femtoBlock}
+ \end{enumerate}
- \end{columns}
- \end{femtoBlock}
-
\end{frame}
-%%%%%%%%%%%%%%%%%%%%
-%% SLIDE 14 %%
-%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small DiLCO Protocol $\blacktriangleright$ Main Idea}
-%\vspace{-3.2cm}
-\begin{femtoBlock} {}%{Main Idea:\\}
-\centering
-\includegraphics[height = 2.5cm]{Figures/OneSensingRound.jpg}
-\vspace{1.2cm}
-\begin{enumerate}
-\item \textcolor{blue}{ \textbf{INFORMATION EXCHANGE:}}\\
-Sensors exchanges through multi-hop communication, their:
-\begin{itemize}
-\item Position coordinates,
-\item current remaining energy,
-\item sensor node ID, and
-\item number of its one-hop live neighbors.
-\end{itemize}
+\begin{frame}{Assumptions for our protocols}
+ \vspace{-0.5cm}
+\begin{center}
+ \includegraphics[height = 7.0cm]{Figures/Pmodels.pdf}
+\end{center}
+\end{frame}
-\end{enumerate}
-\end{femtoBlock}
+
+
+\begin{frame}{General scheme}
+\vspace{-0.2cm}
+\begin{figure}[ht!]
+ \includegraphics[width=110mm]{Figures/GeneralModel.jpg}
+ \end{figure}
+
+\begin{itemize}
+\item DiLCO and PeCO $\blacktriangleright$ one round sensing ($T=1$)
+\item MuDiLCO $\blacktriangleright$ multiple rounds sensing ($T=1\cdots T$)
+\end{itemize}
+
\end{frame}
-%%%%%%%%%%%%%%%%%%%%
-%% SLIDE 14.1 %%
-%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small DiLCO Protocol $\blacktriangleright$ Main Idea}
-%\vspace{-3.2cm}
-\begin{femtoBlock} {}%{Main Idea:\\}
+\begin{frame}{General scheme}
+ \vspace{-0.2cm}
+\begin{enumerate} [i)]
+\item \textcolor{blue}{\textbf{INFORMATION EXCHANGE}} $\blacktriangleright$ Sensors exchange through multi-hop communication, their
+\begin{itemize}
+\item \textcolor{magenta}{Position coordinates}, \textcolor{violet}{current remaining energy}, \textcolor{cyan}{sensor node ID}, and \textcolor{red}{number of its one-hop live neighbors}
+
+\end{itemize}
-\begin{enumerate} [2.]
-\item \textcolor{blue}{ \textbf{ LEADER ELECTION:}}\\
-The selection criteria are, in order of importance:
+\item \textcolor{blue}{\textbf{LEADER ELECTION}} $\blacktriangleright$ The selection criteria are, in order
\begin{itemize}
-\item larger number of neighbors,
-\item larger remaining energy, and then in case of equality,
-\item larger ID.
+\item Larger number of neighbors
+\item Larger remaining energy, and then in case of equality
+\item Larger ID
\end{itemize}
-\end{enumerate}
-\begin{enumerate} [3.]
-\item \textcolor{blue}{ \textbf{ DECISION:}} \\
-Leader solves an integer program(see next slide) to:
+
+
+\item \textcolor{blue}{\textbf{DECISION}} $\blacktriangleright$ Leader solves an integer program to
\begin{itemize}
-\item Select which sensors will be activated in the sensing phase.
-\item Send Active-Sleep packet to each sensor in the subregion.
+\item Select which sensors will be activated in the sensing phase
+\item Send Active-Sleep packet to each sensor in the subregion
\end{itemize}
-\end{enumerate}
-\begin{enumerate} [4.]
-\item \textcolor{blue}{ \textbf{ SENSING:}} \\
-Based on Active-Sleep Packet Information:
+
+
+\item \textcolor{blue}{\textbf{SENSING}} $\blacktriangleright$ Based on Active-Sleep Packet Information
\begin{itemize}
-\item Active sensors will execute their sensing task.
-\item Sleep sensors will wait a time equal to the period of sensing to wakeup.
+\item Active sensors will execute their sensing task
+\item Sleep sensors will wait a time equal to the period of sensing to wakeup
\end{itemize}
-
\end{enumerate}
-
-\end{femtoBlock}
+
\end{frame}
+
%%%%%%%%%%%%%%%%%%%%
-%% SLIDE 15 %%
+%% SLIDE 12 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small DiLCO Protocol $\blacktriangleright$ Coverage Problem Formulation}
-\begin{femtoBlock} { }
-\noindent Our coverage optimization problem can then be formulated as follows:
-\begin{equation*} \label{eq:ip2r}
-\left \{
-\begin{array}{ll}
-\min \sum_{p \in P} (w_{\theta} \Theta_{p} + w_{U} U_{p})&\\
-\textrm{subject to :}&\\
-\sum_{j \in J} \alpha_{jp} X_{j} - \Theta_{p}+ U_{p} =1, &\forall p \in P\\
-%\label{c1}
-%\sum_{t \in T} X_{j,t} \leq \frac{RE_j}{e_t} &\forall j \in J \\
-%\label{c2}
-\Theta_{p}\in \mathbb{N}, &\forall p \in P\\
-U_{p} \in \{0,1\}, &\forall p \in P \\
-X_{j} \in \{0,1\}, &\forall j \in J
-\end{array}
-\right.
-\end{equation*}
+\section{\small {Distributed Lifetime Coverage Optimization Protocol (DiLCO)}}
-\begin{itemize}
-\item $X_{j}$ : indicates whether or not the sensor $j$ is actively sensing (1
- if yes and 0 if not);
-\item $\Theta_{p}$ : {\it overcoverage}, the number of sensors minus one that
- are covering the primary point $p$;
-\item $U_{p}$ : {\it undercoverage}, indicates whether or not the primary point
- $p$ is being covered (1 if not covered and 0 if covered).
-\end{itemize}
-\end{femtoBlock}
+%%%%%%%%%%%%%%%%%%%%
+%% SLIDE 15 %%
+%%%%%%%%%%%%%%%%%%%%
+\begin{frame}{\small DiLCO protocol $\blacktriangleright$ Coverage problem formulation}
+\vspace{0.2cm}
+\centering
+\includegraphics[height = 7.2cm]{Figures/modell1.pdf}
\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 16 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small DiLCO Protocol $\blacktriangleright$ DiLCO Protocol Algorithm}
+\begin{frame}{\small DiLCO protocol $\blacktriangleright$ DiLCO protocol algorithm}
%\begin{femtoBlock} {}
\centering
%\includegraphics[height = 7.2cm]{Figures/algo.jpeg}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 18 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small DiLCO Protocol $\blacktriangleright$ Simulation Framework}
+\begin{frame}{\small DiLCO protocol $\blacktriangleright$ Simulation framework}
\vspace{-0.8cm}
\small
\begin{table}[ht]
-\caption{Relevant parameters for network initializing.}
+\caption{Relevant parameters for simulation}
\centering
\begin{tabular}{c|c}
\hline
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 19 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small DiLCO Protocol $\blacktriangleright$ Energy Model \& Performance Metrics }
+\begin{frame}{\small DiLCO protocol $\blacktriangleright$ Energy model \& performance metrics }
%\vspace{-1.8cm}
-\begin{femtoBlock} {Energy Consumption Model}
+\begin{femtoBlock} {Energy consumption model}
\vspace{-1.0cm}
\begin{table}[h]
%\centering
\end{femtoBlock}
\vspace{-0.5cm}
-\begin{femtoBlock} {Performance Metrics}
+\begin{femtoBlock} {Performance metrics}
\small
-\begin{enumerate}[$\mapsto$]
-\item {{\bf Network Lifetime}}
+\begin{enumerate}[$\blacktriangleright$]
+
\item {{\bf Coverage Ratio (CR)}}
-\item {{\bf Energy Consumption}}
-\item{{\bf Number of Active Sensors Ratio (ASR)}}
-\item {{\bf Execution Time}}
+\item {{\bf Number of Active Sensors Ratio (ASR)}}
+\item {{\bf Energy consumption}}
+\item {{\bf Network lifetime}}
+%\item {{\bf Execution Time}}
%\item {{\bf Stopped Simulation Runs}}
\end{enumerate}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 20 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{ \small DiLCO Protocol $\blacktriangleright$ Performance Comparison}
+\begin{frame}{ \small DiLCO protocol $\blacktriangleright$ Performance comparison}
\vspace{-0.5cm}
\begin{figure}[h!]
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 20 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{ \small DiLCO Protocol $\blacktriangleright$ Performance Comparison}
+\begin{frame}{ \small DiLCO protocol $\blacktriangleright$ Performance comparison}
\vspace{-0.5cm}
\begin{figure}[h!]
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 22 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{ \small DiLCO Protocol $\blacktriangleright$ Performance Comparison}
+\begin{frame}{ \small DiLCO protocol $\blacktriangleright$ Performance comparison}
\vspace{-0.5cm}
\begin{figure}%[h!]
\begin{columns}[c]
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 23 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{ \small DiLCO Protocol $\blacktriangleright$ Performance Comparison}
+\begin{frame}{ \small DiLCO protocol $\blacktriangleright$ Performance comparison}
\vspace{-0.5cm}
\begin{figure}%[h!]
\begin{columns}[c]
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 28 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small MuDiLCO Protocol $\blacktriangleright$ Main Idia}
-\vspace{-0.2cm}
-\begin{figure}[ht!]
- \includegraphics[width=110mm]{Figures/GeneralModel.jpg}
-\caption{MuDiLCO protocol.}
-\label{fig2}
-\end{figure}
-\end{frame}
+%\begin{frame}{\small MuDiLCO Protocol $\blacktriangleright$ Main Idea}
+%\vspace{-0.2cm}
+%\begin{figure}[ht!]
+% \includegraphics[width=110mm]{Figures/GeneralModel.jpg}
+%\caption{MuDiLCO protocol.}
+%\label{fig2}
+%\end{figure}
+%\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 29 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small MuDiLCO Protocol $\blacktriangleright$ Multiround Coverage Problem Formulation}
+\begin{frame}{\small MuDiLCO protocol $\blacktriangleright$ Multiround coverage problem Formulation}
\vspace{0.2cm}
-\small
-Our coverage optimization problem can then be formulated as follows
-\vspace{-0.2cm}
-\begin{equation*}
- \min \sum_{t=1}^{T} \sum_{p=1}^{P} \left(W_{\theta}* \Theta_{t,p} + W_{U} * U_{t,p} \right) \label{eq15}
-\end{equation*}
-
-Subject to
-\vspace{-0.2cm}
-\begin{equation*}
- \sum_{j=1}^{|J|} \alpha_{j,p} * X_{t,j} = \Theta_{t,p} - U_{t,p} + 1 \label{eq16} \hspace{6 mm} \forall p \in P, t = 1,\dots,T
-\end{equation*}
-
-\begin{equation*}
- \sum_{t=1}^{T} X_{t,j} \leq \lfloor {RE_{j}/E_{th}} \rfloor \hspace{6 mm} \forall j \in J, t = 1,\dots,T
- \label{eq144}
-\end{equation*}
-
-\begin{equation*}
-X_{t,j} \in \lbrace0,1\rbrace, \hspace{10 mm} \forall j \in J, t = 1,\dots,T \label{eq17}
-\end{equation*}
-
-\begin{equation*}
-U_{t,p} \in \lbrace0,1\rbrace, \hspace{10 mm}\forall p \in P, t = 1,\dots,T \label{eq18}
-\end{equation*}
-
-\begin{equation*}
- \Theta_{t,p} \geq 0 \hspace{10 mm}\forall p \in P, t = 1,\dots,T \label{eq178}
-\end{equation*}
-
-
-
-
+\centering
+\includegraphics[height = 7.2cm]{Figures/modell2.pdf}
\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 30 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small MuDiLCO Protocol $\blacktriangleright$ MuDiLCO Protocol Algorithm}
-%\vspace{0.2cm}
-\begin{femtoBlock} {}
-\centering
-%\includegraphics[height = 7.2cm]{Figures/algo2.jpeg}
-\includegraphics[height = 7.2cm]{Figures/Algo2.png}
-\end{femtoBlock}
-\end{frame}
+%\begin{frame}{\small MuDiLCO Protocol $\blacktriangleright$ MuDiLCO Protocol Algorithm}
+%%\vspace{0.2cm}
+%\begin{femtoBlock} {}
+%\centering
+%\includegraphics[height = 7.2cm]{Figures/Algo2.png}
+%\end{femtoBlock}
+%\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 31 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small MuDiLCO Protocol $\blacktriangleright$ Results Analysis and Comparison}
+\begin{frame}{\small MuDiLCO protocol $\blacktriangleright$ Results analysis and comparison}
\vspace{-0.5cm}
\begin{figure}[h!]
\centering
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 32 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small MuDiLCO Protocol $\blacktriangleright$ Results Analysis and Comparison}
+\begin{frame}{\small MuDiLCO protocol $\blacktriangleright$ Results analysis and comparison}
\vspace{-0.5cm}
\begin{figure}[h!]
\centering
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 34 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small MuDiLCO Protocol $\blacktriangleright$ Results Analysis and Comparison}
-\vspace{-0.5cm}
-\begin{figure}[h!]
-\centering
-\includegraphics[scale=0.5]{Figures/R1/T.pdf}
-\caption{Execution Time (in seconds)}
-\label{fig77}
-\end{figure}
-\end{frame}
+%\begin{frame}{\small MuDiLCO Protocol $\blacktriangleright$ Results Analysis and Comparison}
+%\vspace{-0.5cm}
+%\begin{figure}[h!]
+%\centering
+%\includegraphics[scale=0.5]{Figures/R1/T.pdf}
+%\caption{Execution Time (in seconds)}
+%\label{fig77}
+%\end{figure}
+%\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 35 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small MuDiLCO Protocol $\blacktriangleright$ Results Analysis and Comparison}
+\begin{frame}{\small MuDiLCO protocol $\blacktriangleright$ Results analysis and comparison}
\vspace{-0.5cm}
\begin{figure}%[h!]
\begin{columns}[c]
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 36 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small MuDiLCO Protocol $\blacktriangleright$ Results Analysis and Comparison}
+\begin{frame}{\small MuDiLCO protocol $\blacktriangleright$ Results analysis and comparison}
\vspace{-0.5cm}
\begin{figure}%[h!]
\begin{columns}[c]
-\section{\small {Perimeter-based Coverage Optimization (PeCO) to Improve Lifetime in WSNs
-}}
+\section{\small {Perimeter-based Coverage Optimization (PeCO)}}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 45 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small PeCO Protocol $\blacktriangleright$ Assumptions and Models}
+\begin{frame}{\small PeCO protocol $\blacktriangleright$ Assumptions and models}
\vspace{-0.5cm}
\begin{figure}%[h!]
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 46 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small PeCO Protocol $\blacktriangleright$ Assumptions and Models}
+\begin{frame}{\small PeCO protocol $\blacktriangleright$ Assumptions and models}
-\vspace{-0.5cm}
+\vspace{-1.2cm}
\begin{figure}%[h!]
-\begin{columns}[c]
- \column{.50\textwidth}
-\includegraphics[scale=0.33]{Figures/ch6/expcm2.jpg}
-\footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~(a)\\
-\column{.50\textwidth}
-\includegraphics[scale=0.38]{Figures/tbl.jpeg}
-\footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~(b) \\
-\end{columns}
-\caption{(a) Maximum coverage levels for perimeter of sensor node $0$. and (b) Coverage intervals and contributing sensors for sensor node 0.}
- \label{pcm2sensors}
+%\begin{columns}[c]
+% \column{.50\textwidth}
+\includegraphics[scale=0.6]{Figures/ch6/expcm2.jpg}
+%\footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~(a)\\
+%\column{.50\textwidth}
+%\includegraphics[scale=0.38]{Figures/tbl.jpeg}
+%\footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~(b) \\
+%\end{columns}
+%\caption{(a) Maximum coverage levels for perimeter of sensor node $0$. and (b) Coverage intervals and contributing sensors for sensor node 0.}
+% \label{pcm2sensors}
\end{figure}
+\vspace{-0.5cm}
+\textcolor {red} {For example, the interval between 3R to 4R is covered by 4 sensors (0,1,2,4), it means the coverage level is 4}
\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 47 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small PeCO Protocol $\blacktriangleright$ PeCO Protocol Algorithm}
+\begin{frame}{\small PeCO protocol $\blacktriangleright$ PeCO protocol algorithm}
\vspace{-0.7cm}
%\includegraphics[height = 7.2cm]{Figures/algo6.jpeg}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 48 %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small PeCO Protocol $\blacktriangleright$ Perimeter-based Coverage Problem Formulation}
-\vspace{-1.1cm}
+\begin{frame}{\small PeCO protocol $\blacktriangleright$ Perimeter-based coverage problem formulation}
+\vspace{-0.72cm}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.5]{Figures/ch6/formula6.png}
+\includegraphics[scale=0.5]{Figures/modell3.pdf}
\end{figure}
\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small PeCO Protocol $\blacktriangleright$ Performance Evaluation and Analysis}
+\begin{frame}{\small PeCO protocol $\blacktriangleright$ Performance evaluation and analysis}
\vspace{-0.5cm}
\begin{figure}[h!]
\centering
\label{fig333}
\end{figure}
+
\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small PeCO Protocol $\blacktriangleright$ Performance Evaluation and Analysis}
+\begin{frame}{\small PeCO protocol $\blacktriangleright$ Performance evaluation and analysis}
\vspace{-0.5cm}
\begin{figure}[h!]
\centering
%%%%%%%%%%%%%%%%%%%%
%% SLIDE %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small PeCO Protocol $\blacktriangleright$ Performance Evaluation and Analysis}
+\begin{frame}{\small PeCO protocol $\blacktriangleright$ Performance evaluation and analysis}
\vspace{-0.5cm}
\begin{figure}%[h!]
\begin{columns}[c]
%%%%%%%%%%%%%%%%%%%%
%% SLIDE %%
%%%%%%%%%%%%%%%%%%%%
-\begin{frame}{\small PeCO Protocol $\blacktriangleright$ Performance Evaluation and Analysis}
+\begin{frame}{\small PeCO protocol $\blacktriangleright$ Performance evaluation and analysis}
\vspace{-0.5cm}
\begin{figure}%[h!]
\begin{columns}[c]
\includegraphics[scale=0.35]{Figures/ch6/R/LT50.eps}
\footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(b) \\
\end{columns}
-\caption{Network Lifetime for (a)~$Lifetime_{95}$ and (b)~$Lifetime_{50}$.}
+\caption{Network lifetime for (a)~$Lifetime_{95}$ and (b)~$Lifetime_{50}$.}
\label{fig3LT}
\end{figure}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE %%
%%%%%%%%%%%%%%%%%%%%
-\section{\small {Conclusion and Perspectives}}
+\section{\small {Conclusion and perspectives}}
%%%%%%%%%%%%%%%%%%%%
\item Two-step approaches are proposed to optimize both coverage and lifetime performances, where:
\begin{itemize}
-\item Sensing field is divided into smaller subregions using divide-and-conquer method.
-\item One of the proposed optimization protocols is applied in each subregion in a distributed parallel way.
+\item Sensing field is divided into smaller subregions using divide-and-conquer method
+\item One of the proposed optimization protocols is applied in each subregion in a distributed parallel way
\end{itemize}
-\item The proposed protocols (DiLCO, MuDiLCO, PeCO) combine two efficient mechanisms:
+\item The proposed protocols (DiLCO, MuDiLCO, PeCO) combine two efficient mechanisms
\begin{itemize}
\item Network leader election, and
-\item Sensor activity scheduling based optimization.
+\item Sensor activity scheduling based optimization
\end{itemize}
-\item Our protocols are periodic where each period consists of 4
-phases:
+\item Our protocols are periodic where each period consists of 4 phases
\begin{itemize}
-\item Information exchange,
-\item Network leader election,
-\item Decision based optimization, and
+\item Information exchange
+\item Network leader election
+\item Decision based optimization
\item Sensing.
\end{itemize}
\end{enumerate}
\begin{frame}{Conclusion}
\begin{enumerate} [$\blacktriangleright$]
-\item DiLCO and PeCO provide a schedule for one round per period.
-\item MuDiLCO provides a schedule for multiple rounds per period.
-\item Comparison results show that DiLCO, MuDiLCO, and PeCO protocols:
+\item DiLCO and PeCO provide a schedule for one round per period
+\item MuDiLCO provides a schedule for multiple rounds per period
+\item Comparison results show that DiLCO, MuDiLCO, and PeCO protocols
\begin{itemize}
- \item maintain the coverage for a larger number of rounds.
- \item use less active nodes to save energy efficiently during sensing.
- \item are more powerful against network disconnections.
- \item perform the optimization with suitable execution times.
- \item consume less energy.
- \item prolong the network lifetime.
+ \item Maintain the coverage for a larger number of rounds
+ \item Use less active nodes to save energy efficiently during sensing
+ \item Are more powerful against network disconnections
+ \item Perform the optimization with suitable execution times
+ \item Consume less energy
+ \item Prolong the network lifetime
\end{itemize}
\end{enumerate}
\end{frame}
+\begin{frame}{Conclusion}
+\tiny
+\begin{block}{\textcolor{white}{Journal Articles}}
+\begin{enumerate}[$\lbrack$1$\rbrack$]
+\item Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier. Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks. \textit{Engineering Optimization, 2015, (Submitted)}.
+
+\item Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier. Multiround Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks. \textit{Ad Hoc Networks, 2015, (Submitted)}.
+
+\item Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier. Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks. \textit{Journal of Supercomputing , 2015, (Submitted)}.
+\end{enumerate}
+\end{block}
+
+\begin{block}{\textcolor{white}{Technical Reports}}
+
+\begin{enumerate}[$\lbrack$1$\rbrack$]
+\item Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el
+Distributed lifetime coverage optimization protocol in wireless sensor networks. Technical Report DISC2014-X, University of Franche-Comte - FEMTO-ST Institute, DISC Research Department, Octobre 2014.
+\end{enumerate}
+\end{block}
+
+\begin{block}{\textcolor{white}{Conference Articles}}
+\begin{enumerate}[$\lbrack$1$\rbrack$]
+\item Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el
+Coverage and lifetime optimization in heterogeneous energy wireless sensor networks. In ICN 2014, The Thirteenth International Conference on Networks, pages 49–54, 2014.
+\end{enumerate}
+\end{block}
+
+\end{frame}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 52 %%
%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Perspectives}
\begin{enumerate} [$\blacktriangleright$]
-\item The optimal number of subregions will be investigated.
-\item Design a heterogeneous integrated optimization protocol to integrate coverage, routing, and data aggregation protocols.
-\item Extend PeCO protocol so that the schedules are planned for multiple
-sensing periods.
-\item We plan to consider particle swarm optimization or evolutionary algorithms to obtain quickly near optimal solutions.
-\item Improve our mathematical models to take into account heterogeneous sensors from both energy and node characteristics point of views.
-\item The cluster head will be selected in a distributed way and based on local information.
+\item Investigate the optimal number of subregions
+\item Design a heterogeneous integrated optimization protocol to integrate coverage, routing, and data aggregation protocols
+\item Extend PeCO protocol so that the schedules are planned for multiple sensing periods
+\item Consider particle swarm optimization or evolutionary algorithms to obtain quickly near optimal solutions
+\item Improve our mathematical models to take into account heterogeneous sensors from both energy and node characteristics point of views
+%\item The cluster head will be selected in a distributed way and based on local information.
\end{enumerate}
