\setbeamertemplate{section in toc}[sections numbered]
\setbeamertemplate{subsection in toc}[subsections numbered]
-
+\pagenumbering{roman}
\AtBeginSection[]
{
\begin{frame}
\begin{frame} {Problem Definition, Solution, and Objectives}
\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?
+ \textcolor{black}{How to minimize the energy consumption and extend the network lifetime when covering a certain area?}
\end{block}
\end{frame}
%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Problem Definition, Solution, and Objectives}
-\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: distributed optimization process}}
+\bf \textcolor{black}{Division into subregions}\\
+\bf \textcolor{black}{For each subregion:}
\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}
+
+ \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:}}
+\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: every point inside an area has to be monitored.}
+\item \textcolor{blue} {Target coverage:} 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:} 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 High communication overhead especially for dense WSNs.
- \item Reliable and scalable for large WSNs.
+ \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 \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: 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: 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: 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 Its $R_c\geq 2R_s$ for imply connectivity among active nodes during complete coverage (hypothesis 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:
\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}{Our general scheme}
+\vspace{-0.2cm}
+\begin{figure}[ht!]
+ \includegraphics[width=110mm]{Figures/GeneralModel.jpg}
+ \end{figure}
+
+\begin{itemize}
+\item DiLCO and PeCO $\blacktriangleright$ use one round sensing ($T=1$)
+\item MuDiLCO $\blacktriangleright$ uses 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}{Our 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}
\vspace{-0.8cm}
\small
\begin{table}[ht]
-\caption{Relevant parameters for network initializing.}
+\caption{Relevant parameters for simulation.}
\centering
\begin{tabular}{c|c}
\hline
\begin{femtoBlock} {Performance Metrics}
\small
\begin{enumerate}[$\mapsto$]
-\item {{\bf Network Lifetime}}
+
\item {{\bf Coverage Ratio (CR)}}
+\item{{\bf Number of Active Sensors Ratio (ASR)}}
\item {{\bf Energy Consumption}}
-\item{{\bf Number of Active Sensors Ratio (ASR)}}
-\item {{\bf Execution Time}}
+\item {{\bf Network Lifetime}}
+%\item {{\bf Execution Time}}
%\item {{\bf Stopped Simulation Runs}}
\end{enumerate}
%%%%%%%%%%%%%%%%%%%%
%% 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}
%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%
\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 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}
%%%%%%%%%%%%%%%%%%%%
-\section{\small {Perimeter-based Coverage Optimization (PeCO) to Improve Lifetime in WSNs
-}}
+\section{\small {Perimeter-based Coverage Optimization (PeCO)}}
%%%%%%%%%%%%%%%%%%%%
%% SLIDE 48 %%
%%%%%%%%%%%%%%%%%%%%
\begin{frame}{\small PeCO Protocol $\blacktriangleright$ Perimeter-based Coverage Problem Formulation}
-\vspace{-1.1cm}
+\vspace{-0.7cm}
\begin{figure}[h!]
\centering
-\includegraphics[scale=0.5]{Figures/ch6/formula6.png}
+\includegraphics[scale=0.49]{Figures/modell3.pdf}
\end{figure}
\end{frame}
\label{fig333}
\end{figure}
+
\end{frame}
%%%%%%%%%%%%%%%%%%%%
\begin{itemize}
\item Information exchange,
\item Network leader election,
-\item Decision based optimization, and
+\item Decision based optimization,
\item Sensing.
\end{itemize}
\end{enumerate}
\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 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 We plan to consider particle swarm optimization or evolutionary algorithms to obtain quickly near optimal solutions.
+\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.
+%\item The cluster head will be selected in a distributed way and based on local information.
\end{enumerate}
