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67 \frametitle{Presentation outline}
68 \tableofcontents[currentsection]
73 \title{\textbf{Distributed Coverage Optimization Techniques for Improving Lifetime of Wireless Sensor Networks} \\\vspace{0.1cm}\hspace{2cm}\textbf{\textcolor{cyan}{\small PhD Dissertation Defense}}}
74 \author{\textbf{\textcolor{green}{Ali Kadhum IDREES}} \\\vspace{0.5cm} \small Under Supervision: \\\textcolor{cyan}{\small Raphaël COUTURIER, Karine DESCHINKEL \& Michel SALOMON} \\\vspace{0.2cm} \textcolor{blue}{ University of Franche-Comté - FEMTO-ST - DISC Dept. - AND Team} \\\vspace{0.2cm}~~~~~~~~~~~~~~~~\textbf{\textcolor{green}{1 October 2015 }}}
76 %\institute[FEMTO-ST, DISC]{\textit{FEMTO-ST - DISC Departement - AND Team}}
83 % ____ _____ ____ _ _ _____
84 % | _ \| ____| __ )| | | |_ _|
85 % | | | | _| | _ \| | | | | |
86 % | |_| | |___| |_) | |_| | | |
87 % |____/|_____|____/ \___/ |_|
96 \setbeamertemplate{background}{\titrefemto}
105 \setbeamertemplate{background}{\pagefemto}
111 \begin{frame} {Problem definition and solution}
115 \includegraphics[width=0.495\textwidth]{Figures/6}
117 % \includegraphics[width=0.475\textwidth]{Figures/8}
119 \includegraphics[width=0.495\textwidth]{Figures/10}
121 % \includegraphics[width=0.475\textwidth]{Figures/13}
124 \begin{block}{\textcolor{white}{MAIN QUESTION}}
125 \textcolor{black}{How to minimize the energy consumption and extend the network lifetime when covering the area of interest?}
133 \begin{frame}{Problem definition and solution}
135 \begin{block}{\textcolor{white}{OUR SOLUTION $\blacktriangleright$ Distributed optimization process}}
136 \begin{enumerate} [i)]
137 \item \bf \textcolor{black}{Division into subregions}
138 \item \bf \textcolor{black}{For each subregion}
143 \item \bf \textcolor{magenta}{Leader election}
144 \item \bf \textcolor{magenta}{Activity Scheduling based optimization}
150 \includegraphics[width=0.475\textwidth]{Figures/div2}
152 \includegraphics[width=0.475\textwidth]{Figures/act2}
160 %\begin{frame}{Problem Definition, Solution, and Objectives}
162 %\begin{block}{\textcolor{white}{OUR SOLUTION}}
164 % %\item Leader Election for each subregion.
165 % \item \bf \textcolor{magenta}{Activity Scheduling based optimization is planned for each subregion.}
170 % \includegraphics[width=0.775\textwidth]{Figures/act}
179 %\begin{frame}{Problem Definition, Solution, and Objectives}
181 %\begin{block}{\bf \textcolor{white}{Dissertation Objectives}}
182 %\bf \textcolor{black}{Develop energy-efficient distributed optimization protocols that should be able to:}
184 % \item \bf \textcolor{blue}{Schedule node activities by optimize both coverage and lifetime.}
185 % \item \bf \textcolor{blue}{Combine two efficient techniques: leader election and sensor activity scheduling.}
186 % \item \bf \textcolor{blue}{Perform a distributed optimization process.}
199 \frametitle{Presentation outline}
201 \tableofcontents[section,subsection]
209 \section{\small {State of the Art}}
215 \begin{frame}{Wireless Sensor Networks (WSNs)}
219 \column{.58\textwidth}
222 \includegraphics[height = 3cm]{Figures/WSNT.jpg}
230 \item Electronic low-cost tiny device
231 \item Sense, process and transmit data
232 \item Limited energy, memory and processing capabilities
236 \column{.52\textwidth}
239 \includegraphics[height = 4.5cm]{Figures/WSN.jpg}
243 \includegraphics[height = 2cm]{Figures/sn.jpg}
257 \begin{frame}{Types of Wireless Sensor Networks}
262 %\column{.52\textwidth}
264 % \item Terrestrial WSNs.
265 % \item Underground WSNs.
266 % \item Underwater WSNs.
267 % \item Multimedia WSNs.
272 % \column{.58\textwidth}
274 \includegraphics[height = 7cm]{Figures/typesWSN.pdf}
284 \begin{frame}{Applications}
288 \includegraphics[height = 7cm]{Figures/WSNAP.pdf}
296 \begin{frame}{Energy-efficient mechanisms of a working WSN}
301 % \includegraphics[height = 5cm]{Figures/WSN-M.pdf}
302 \includegraphics[height = 4.8cm]{Figures/EEM.eps}
305 %\bf \textcolor{blue} {Our approach includes cluster architecture and scheduling schemes}
308 %\begin{frame}{Energy-Efficient Mechanisms of a working WSN}
312 % \includegraphics[height = 7cm]{Figures/WSN-S.pdf}
319 \begin{frame}{Network lifetime}
322 { Some definitions\\}
323 \begin{enumerate}[i)]
324 \item \textcolor{black} {Time spent until death of the first sensor (or cluster head)}
325 \item \textcolor{black} {Time spent until death of all wireless sensor nodes in WSN}
326 %\item \textcolor{black} {Time spent by WSN in covering each target by at least one sensor}
327 \item \textcolor{black} {Time spent in covering area of interest by at least k nodes}
328 \item \textcolor{black} {Elapsed time until losing the connectivity or the coverage}
329 \item \bf \textcolor{red} {Elapsed time until the coverage ratio becomes less than a predetermined threshold $\alpha$}
343 \begin{frame}{Coverage in Wireless Sensor Networks}
345 \begin{block} <1-> {\textcolor{white} {Coverage definition}}
346 \textcolor{blue} {Coverage} reflects how well a sensor field is monitored efficiently using as less energy as possible
351 %\begin{block} <2-> {\textcolor{white} {Coverage types}}
352 \begin{block} {\bf \textcolor{white} {Coverage types}}
353 \begin{enumerate}[i)]
354 \item \small \textcolor{red} {Area coverage $\blacktriangleright$ every point inside an area has to be monitored}
355 \item \textcolor{blue} {Target coverage} $\blacktriangleright$ only a finite number of discrete points called targets has to be monitored
357 \item \textcolor{blue} {Barrier coverage} $\blacktriangleright$ detection of targets as they cross a barrier such as in intrusion detection and border surveillance applications
363 %\begin{block} <3-> {\textcolor{white} {Coverage type in this dissertation:}}
364 %The work presented in this dissertation deals with \textcolor{red} {area coverage}.
372 \begin{frame}{Existing works}
374 \begin{block} {\textcolor{white} {Coverage approaches}}
375 %Most existing coverage approaches in literature classified into
376 \begin{enumerate}[i)]
377 \item \textcolor{blue} { Full centralized coverage algorithms}
379 \item Optimal or near optimal solution
380 \item Low computation power for the sensors (except for base station)
381 \item Higher energy consumption for communication in large WSN
382 \item Not scalable for large WSNs
384 \item \textcolor{blue} {Full distributed coverage algorithms}
386 \item Lower quality solution
387 \item Decision process is localized inside sensor and may requires a high computation power for dense WSNs
388 \item Less energy consumption for communication in large WSN
389 \item Reliable and scalable for large WSNs
391 \item \textcolor{red} {Hybrid approaches}
393 \item \textcolor{red} {Globally distributed and locally centralized}
401 %\begin{block} {\textcolor{white} {Coverage protocols in this dissertation:}}
402 %The protocols presented in this dissertation combine between the two above approaches.
408 \begin{frame}{Existing works $\blacktriangleright$ DESK algorithm (Vu et al.)}
411 \includegraphics[height = 5.0cm]{Figures/DESKp.eps}
416 \item Requires only one-hop neighbor information (fully distributed)
417 \item Each sensor decides its status (Active or Sleep) based on the perimeter coverage model, without optimization
422 %\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.}
427 \begin{frame}{Existing works $\blacktriangleright$ GAF algorithm (Xu et al.)}
432 \column{.58\textwidth}
435 \includegraphics[height = 2.7cm]{Figures/GAF1.eps}
439 \includegraphics[height = 3.3cm]{Figures/GAF2.eps}
442 \column{.52\textwidth}
446 \item Distributed energy-based scheduling approach
447 \item Uses geographic location information to divide the area into a fixed square grids
448 \item Nodes are in one of three sates $\blacktriangleright$ discovery, active, or sleep
449 \item Only one node staying active in grid
450 \item The fixed grid is square with r units on a side
451 \item Nodes cooperate within each grid to choose the active node
457 % \item \tiny enat: estimated node active time
458 % \item enlt: estimated node lifetime
459 % \item Td,Ta, Ts: discovery, active, and sleep timers
461 % \item Ts = [enat/2, enat]
470 %\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.}
473 \section{\small {The main scheme for our protocols}}
476 \begin{frame}{Assumptions for our protocols}
479 \begin{enumerate} [$\divideontimes$]
480 \item Static wireless sensor, homogeneous in terms of
484 \item Processing capabilities
486 \item Heterogeneous initial energy
487 \item High density uniform deployment
490 \item Complete coverage $\Rightarrow$ connectivity (proved by Zhang and Zhou)
492 \item Multi-hop communication
499 \begin{frame}{Assumptions for our protocols}
502 \begin{enumerate} [$\divideontimes$]
503 \item Known location by
506 \item location discovery algorithm
509 \item Using two kinds of packets
512 \item ActiveSleep packet
514 \item Five status for each node
532 \begin{frame}{Assumptions for our protocols}
535 \includegraphics[height = 7.0cm]{Figures/Pmodelsn.pdf}
543 \begin{frame}{General scheme}
546 \includegraphics[width=110mm]{Figures/GeneralModel.jpg}
550 \item DiLCO and PeCO $\blacktriangleright$ one round sensing ($T=1$)
551 \item MuDiLCO $\blacktriangleright$ multiple rounds sensing ($t=1, \cdots, T$)
557 \begin{frame}{General scheme}
559 \begin{enumerate} [i)]
560 \item \textcolor{blue}{\textbf{INFORMATION EXCHANGE}} $\blacktriangleright$ Sensors exchange through multi-hop communication, their
562 \item Position coordinates, current remaining energy, sensor node ID, and number of its one-hop live neighbors
567 \item \textcolor{blue}{\textbf{LEADER ELECTION}} $\blacktriangleright$ The selection criteria are, in order
569 \item Larger number of neighbors
570 \item Larger remaining energy
576 \item \textcolor{blue}{\textbf{DECISION}} $\blacktriangleright$ Leader solves an integer program to
578 \item Select which sensors will be activated in the sensing phase
579 \item Send Active-Sleep packet to each sensor in the subregion
583 \item \textcolor{blue}{\textbf{SENSING}} $\blacktriangleright$ Based on Active-Sleep Packet Information
585 \item Active sensors will execute their sensing task
586 \item Sleep sensors will wait a time equal to the period of sensing to wakeup
598 \section{\small {Distributed Lifetime Coverage Optimization Protocol (DiLCO)}}
604 \begin{frame}{\small DiLCO protocol $\blacktriangleright$ Coverage problem formulation}
607 \includegraphics[height = 7.2cm]{Figures/modell1.pdf}
615 \begin{frame}{\small DiLCO protocol $\blacktriangleright$ DiLCO protocol algorithm}
616 %\begin{femtoBlock} {}
618 %\includegraphics[height = 7.2cm]{Figures/algo.jpeg}
619 \includegraphics[height = 7.2cm]{Figures/Algo1.png}
630 \begin{frame}{\small DiLCO protocol $\blacktriangleright$ Simulation framework}
634 \caption{Relevant parameters for simulation}
638 Parameter & Value \\ [0.5ex]
640 Sensing Field & $(50 \times 25)~m^2 $ \\
641 Nodes Number & 50, 100, 150, 200 and 250~nodes \\
642 Initial Energy & 500-700~joules \\
643 Sensing Period & 60 Minutes \\
644 $E_{th}$ & 36 Joules\\
649 Modeling Language & A Mathematical Programming Language (AMPL) \\
650 Optimization Solver & GNU linear Programming Kit (GLPK) \\
651 Network Simulator & Discrete Event Simulator OMNeT++
662 \begin{frame}{\small DiLCO protocol $\blacktriangleright$ Energy model \& performance metrics }
664 \begin{femtoBlock} {Energy consumption model}
669 %\caption{Power consumption values}
671 \begin{tabular}{|l||cccc|}
673 {\bf Sensor status} & MCU & Radio & Sensing & {\it Power (mW)} \\
675 LISTENING & On & On & On & 20.05 \\
676 ACTIVE & On & Off & On & 9.72 \\
677 SLEEP & Off & Off & Off & 0.02 \\
678 COMPUTATION & On & On & On & 26.83 \\
680 \multicolumn{4}{|l}{Energy needed to send or receive a 2-bit content message} & 0.515 \\
687 \begin{femtoBlock} {Performance metrics}
689 \begin{enumerate}[$\blacktriangleright$]
691 \item {{\bf Coverage Ratio (CR)}}
692 \item {{\bf Active Sensors Ratio (ASR)}}
693 \item {{\bf Energy consumption $(Lifetime_{95}$, $Lifetime_{50})$}}
694 \item {{\bf Network lifetime $(Lifetime_{95}$, $Lifetime_{50})$}}
695 %\item {{\bf Execution Time}}
696 %\item {{\bf Stopped Simulation Runs}}
707 \begin{frame}{ \small DiLCO protocol $\blacktriangleright$ Performance comparison}
712 \includegraphics[scale=0.5] {Figures/R3/CR.eps}
713 \caption{Coverage ratio for 150 deployed nodes}
714 \label{Figures/ch4/R3/CR}
726 \begin{frame}{ \small DiLCO protocol $\blacktriangleright$ Performance comparison}
731 \includegraphics[scale=0.5]{Figures/R3/ASR.eps}
732 \caption{Active sensors ratio for 150 deployed nodes }
733 \label{Figures/ch4/R3/ASR}
741 %\begin{frame}{ \small DiLCO Protocol $\blacktriangleright$ Performance Comparison}
745 %\includegraphics[scale=0.5]{Figures/R3/SR.eps}
746 %\caption{Percentage of stopped simulation runs for 150 deployed nodes }
747 %\label{Figures/ch4/R3/SR}
755 \begin{frame}{ \small DiLCO protocol $\blacktriangleright$ Performance comparison}
759 \column{.50\textwidth}
760 \includegraphics[scale=0.35]{Figures/R3/EC95.eps}
761 \footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(a)\\
762 \column{.50\textwidth}
763 \includegraphics[scale=0.35]{Figures/R3/EC50.eps}
764 \footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(b) \\
766 \caption{Energy consumption for (a) $Lifetime_{95}$ and (b) $Lifetime_{50}$}
767 \label{Figures/ch4/R3/EC}
777 \begin{frame}{ \small DiLCO protocol $\blacktriangleright$ Performance comparison}
781 \column{.50\textwidth}
782 \includegraphics[scale=0.35]{Figures/R3/LT95.eps}
783 \footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(a)\\
784 \column{.50\textwidth}
785 \includegraphics[scale=0.35]{Figures/R3/LT50.eps}
786 \footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(b) \\
788 \caption{Network lifetime for (a) $Lifetime_{95}$ and (b) $Lifetime_{50}$}
789 \label{Figures/ch4/R3/LT}
802 \section{\small{Multiround Distributed Lifetime Coverage Optimization Protocol (MuDiLCO)}}
808 %\begin{frame}{\small MuDiLCO Protocol $\blacktriangleright$ Main Idea}
811 % \includegraphics[width=110mm]{Figures/GeneralModel.jpg}
812 %\caption{MuDiLCO protocol.}
821 \begin{frame}{\small MuDiLCO protocol $\blacktriangleright$ Multiround coverage problem formulation}
825 \includegraphics[height = 7.2cm]{Figures/modell2.pdf}
832 %\begin{frame}{\small MuDiLCO Protocol $\blacktriangleright$ MuDiLCO Protocol Algorithm}
834 %\begin{femtoBlock} {}
836 %\includegraphics[height = 7.2cm]{Figures/Algo2.png}
844 \begin{frame}{\small MuDiLCO protocol $\blacktriangleright$ Performance comparison}
848 \includegraphics[scale=0.5] {Figures/R1/CR.pdf}
849 \caption{Average coverage ratio for 150 deployed nodes}
858 \begin{frame}{\small MuDiLCO protocol $\blacktriangleright$ Performance comparison}
862 \includegraphics[scale=0.5]{Figures/R1/ASR.pdf}
863 \caption{Active sensors ratio for 150 deployed nodes}
872 %\begin{frame}{\small MuDiLCO Protocol $\blacktriangleright$ Results Analysis and Comparison}
876 %\includegraphics[scale=0.5]{Figures/R1/SR.pdf}
877 %\caption{Cumulative percentage of stopped simulation runs for 150 deployed nodes }
885 %\begin{frame}{\small MuDiLCO Protocol $\blacktriangleright$ Results Analysis and Comparison}
889 %\includegraphics[scale=0.5]{Figures/R1/T.pdf}
890 %\caption{Execution Time (in seconds)}
899 \begin{frame}{\small MuDiLCO protocol $\blacktriangleright$ Performance comparison}
903 \column{.50\textwidth}
904 \includegraphics[scale=0.35]{Figures/R1/EC95.eps}
905 \footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(a)\\
906 \column{.50\textwidth}
907 \includegraphics[scale=0.35]{Figures/R1/EC50.eps}
908 \footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(b) \\
910 \caption{Energy consumption for (a) $Lifetime_{95}$ and (b) $Lifetime_{50}$}
911 \label{Figures/ch4t/R3/EC}
919 \begin{frame}{\small MuDiLCO protocol $\blacktriangleright$ Performance comparison}
923 \column{.50\textwidth}
924 \includegraphics[scale=0.35]{Figures/R1/LT95.eps}
925 \footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(a)\\
926 \column{.50\textwidth}
927 \includegraphics[scale=0.35]{Figures/R1/LT50.eps}
928 \footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(b) \\
930 \caption{Network lifetime for (a) $Lifetime_{95}$ and (b) $Lifetime_{50}$}
931 \label{Figures/ch4/Rh3/EC}
939 \section{\small {Perimeter-based Coverage Optimization (PeCO)}}
945 \begin{frame}{\small PeCO protocol $\blacktriangleright$ Assumptions and models}
950 \column{.50\textwidth}
951 \includegraphics[scale=0.40]{Figures/ch6/pcm.jpg}
952 \footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~(a)\\
953 \column{.50\textwidth}
954 $$\alpha = \arccos \left(\dfrac{Dist(u,v)}{2R_s}
956 \includegraphics[scale=0.30]{Figures/ch6/twosensors.eps}
957 \footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~(b) \\
959 \caption{(a) Perimeter coverage of sensor node 0 and (b) finding the arc of
960 $u$'s perimeter covered by $v$.}
969 \begin{frame}{\small PeCO protocol $\blacktriangleright$ Assumptions and models}
974 % \column{.50\textwidth}
975 \includegraphics[scale=0.6]{Figures/ch6/expcm2.jpg}
976 %\footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~(a)\\
977 %\column{.50\textwidth}
978 %\includegraphics[scale=0.38]{Figures/tbl.jpeg}
979 %\footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~(b) \\
981 %\caption{(a) Maximum coverage levels for perimeter of sensor node $0$. and (b) Coverage intervals and contributing sensors for sensor node 0.}
982 % \label{pcm2sensors}
986 \textcolor {red} {Set of sensors involved in coverage interval of sensor 0 between 5L to 6L $\Rightarrow$ [0,2,5]\\
987 Maximum coverage level: 3
989 %For example, the interval between 3R to 4R is covered by 4 sensors (0,1,2,4), it means the coverage level is 4
998 \begin{frame}{\small PeCO protocol $\blacktriangleright$ PeCO protocol algorithm}
1000 %\includegraphics[height = 7.2cm]{Figures/algo6.jpeg}
1004 \includegraphics[height = 7.2cm]{Figures/ch6/Algo3n.pdf}
1009 %%%%%%%%%%%%%%%%%%%%
1011 %%%%%%%%%%%%%%%%%%%%
1012 \begin{frame}{\small PeCO protocol $\blacktriangleright$ Perimeter-based coverage problem formulation}
1017 \includegraphics[scale=0.5]{Figures/modell3.pdf}
1024 %%%%%%%%%%%%%%%%%%%%
1026 %%%%%%%%%%%%%%%%%%%%
1027 \begin{frame}{\small PeCO protocol $\blacktriangleright$ Performance comparison}
1031 \includegraphics[scale=0.5] {Figures/ch6/R/CR.eps}
1032 \caption{Coverage ratio for 200 deployed nodes.}
1039 %%%%%%%%%%%%%%%%%%%%
1041 %%%%%%%%%%%%%%%%%%%%
1042 \begin{frame}{\small PeCO protocol $\blacktriangleright$ Performance comparison}
1046 \includegraphics[scale=0.5]{Figures/ch6/R/ASR.eps}
1047 \caption{Active sensors ratio for 200 deployed nodes.}
1053 %%%%%%%%%%%%%%%%%%%%
1055 %%%%%%%%%%%%%%%%%%%%
1056 \begin{frame}{\small PeCO protocol $\blacktriangleright$ Performance comparison}
1060 \column{.50\textwidth}
1061 \includegraphics[scale=0.35]{Figures/ch6/R/EC95.eps}
1062 \footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(a)\\
1063 \column{.50\textwidth}
1064 \includegraphics[scale=0.35]{Figures/ch6/R/EC50.eps}
1065 \footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(b) \\
1067 \caption{Energy consumption per period for (a)~$Lifetime_{95}$ and (b)~$Lifetime_{50}$.}
1074 %%%%%%%%%%%%%%%%%%%%
1076 %%%%%%%%%%%%%%%%%%%%
1077 \begin{frame}{\small PeCO protocol $\blacktriangleright$ Performance comparison}
1081 \column{.50\textwidth}
1082 \includegraphics[scale=0.35]{Figures/ch6/R/LT95.eps}
1083 \footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(a)\\
1084 \column{.50\textwidth}
1085 \includegraphics[scale=0.35]{Figures/ch6/R/LT50.eps}
1086 \footnotesize \\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(b) \\
1088 \caption{Network lifetime for (a)~$Lifetime_{95}$ and (b)~$Lifetime_{50}$.}
1094 %%%%%%%%%%%%%%%%%%%%
1096 %%%%%%%%%%%%%%%%%%%%
1097 %\begin{frame}{\small PeCO Protocol $\blacktriangleright$ Performance Evaluation and Analysis}
1099 %\begin{figure} [h!]
1100 %\centering \includegraphics[scale=0.5]{Figures/ch6/R/LTa.eps}
1101 %\caption{Network lifetime for different coverage ratios.}
1108 %%%%%%%%%%%%%%%%%%%%
1110 %%%%%%%%%%%%%%%%%%%%
1111 \section{\small {Conclusion and perspectives}}
1114 %%%%%%%%%%%%%%%%%%%%
1116 %%%%%%%%%%%%%%%%%%%%
1117 \begin{frame}{Conclusion}
1118 \begin{enumerate} [$\blacktriangleright$]
1120 \item Two-step approaches are proposed to optimize both coverage and lifetime performances, where:
1122 \item Sensing field is divided into smaller subregions using divide-and-conquer method
1123 \item One of the proposed optimization protocols is applied in each subregion in a distributed parallel way
1125 \item Our proposed protocols combine two efficient mechanisms
1127 \item Network leader election, and
1128 \item Sensor activity scheduling based optimization
1130 \item Our protocols are periodic where each period consists of 4 phases
1132 %\item Information exchange
1133 %\item Network leader election
1134 %\item Decision based optimization
1145 %%%%%%%%%%%%%%%%%%%%
1147 %%%%%%%%%%%%%%%%%%%%
1148 \begin{frame}{Conclusion}
1149 \begin{enumerate} [$\blacktriangleright$]
1151 \item DiLCO and PeCO provide a schedule for one round per period
1152 \item MuDiLCO provides a schedule for multiple rounds per period
1153 \item Comparison results show that our protocols
1155 \item Maintain the coverage for a larger number of rounds
1156 \item Use less active nodes to save energy efficiently during sensing
1157 \item More powerful against network disconnections
1158 % \item Perform the optimization with suitable execution times
1159 \item Consume less energy
1160 \item Prolong the network lifetime
1166 \begin{frame}{Publications}
1168 \begin{block}{\textcolor{white}{Journal Articles}}
1169 \begin{enumerate}[$\lbrack$1$\rbrack$]
1170 \item Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier. Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks. \textit{\textcolor{red}{Engineering Optimization}, 2015, ($2^{nd}$ Revision Submitted)}.
1172 \item Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier. Multiround Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks. \textit{\textcolor{red}{Ad Hoc Networks}, 2015, ($1^{st}$ Revision Submitted)}.
1174 \item Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier. Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks. \textit{\textcolor{red}{Journal of Supercomputing}, 2015, ($1^{st}$ Revision Submitted)}.
1178 \begin{block}{\textcolor{white}{Technical Reports}}
1180 \begin{enumerate}[$\lbrack$1$\rbrack$]
1181 \item Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el
1182 Distributed lifetime coverage optimization protocol in wireless sensor networks. Technical Report DISC2014-X, University of Franche-Comte - FEMTO-ST Institute, DISC Research Department, Octobre 2014.
1186 \begin{block}{\textcolor{white}{Conference Articles}}
1187 \begin{enumerate}[$\lbrack$1$\rbrack$]
1188 \item Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el
1189 Coverage and lifetime optimization in heterogeneous energy wireless sensor networks. In ICN 2014, The Thirteenth International Conference on Networks, pages 49–54, 2014.
1195 %%%%%%%%%%%%%%%%%%%%
1197 %%%%%%%%%%%%%%%%%%%%
1198 \begin{frame}{Perspectives}
1199 \begin{enumerate} [$\blacktriangleright$]
1200 \item Investigate the optimal number of subregions
1201 \item Design a heterogeneous integrated optimization protocol to integrate coverage, routing, and data aggregation protocols
1202 \item Extend PeCO protocol so that the schedules are planned for multiple rounds per period
1203 \item Consider particle swarm optimization or evolutionary algorithms to obtain quickly near optimal solutions
1204 \item Improve our mathematical models to take into account heterogeneous sensors from both energy and node characteristics point of views
1205 %\item The cluster head will be selected in a distributed way and based on local information.
1212 %%%%%%%%%%%%%%%%%%%%
1214 %%%%%%%%%%%%%%%%%%%%
1215 %\begin{frame}{Mes perspectives}
1219 %%%%%%%%%%%%%%%%%%%%
1221 %%%%%%%%%%%%%%%%%%%%
1225 \textcolor{BleuFemto}{Thank You for Your Attention!}\\\vspace{2cm}
1226 \textcolor{BleuFemto}{Questions?}\\