X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/blobdiff_plain/faf0c7c8185aab9116f46f2a4b2cf8860af8171d:/paperkd.tex..856f104be1ac9636e82c13f72052ec7850f8be2b:/bare_conf.tex?ds=inline

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@@ -1,46 +1,81 @@
-\documentclass[a4paper,twoside]{article}
+
+\documentclass[conference]{IEEEtran}
+
+
+\ifCLASSINFOpdf
+  
+\else
+  
+\fi
+
+\hyphenation{op-tical net-works semi-conduc-tor}
 
 \usepackage{float}
 \usepackage{epsfig}
 \usepackage{subfigure}
 \usepackage{calc}
-\usepackage{amssymb}
-\usepackage{amstext}
+ \usepackage{times,amssymb,amsmath,latexsym}
+\usepackage{graphics}
+\usepackage{graphicx}
 \usepackage{amsmath}
-\usepackage{amsthm}
+\usepackage{txfonts}
+\usepackage{algorithmic}
+\usepackage[T1]{fontenc}
+\usepackage{tikz}
+%\usepackage{algorithm}
+%\usepackage{algpseudocode}
+%\usepackage{algorithmwh}
+\usepackage{subfigure}
+\usepackage{float}
+\usepackage{xspace}
+\usepackage[linesnumbered,ruled,vlined,commentsnumbered]{algorithm2e}
+\usepackage{epsfig}
+\usepackage{caption}
 \usepackage{multicol}
-\usepackage{pslatex}
-\usepackage{apalike}
-\usepackage{SciTePress}
-\usepackage{algorithmic,algorithm}
-\usepackage[small]{caption}
 
-\subfigtopskip=0pt
-\subfigcapskip=0pt
-\subfigbottomskip=0pt
 
 \begin{document}
 
-%title{Efficient heuristic building disjoint cover sets \\for target coverage problem in wireless sensor networks}
-\title{ in wireless sensor networks}
-\author{\authorname{Ali Khadum\sup{1},Karine Deschinkel\sup{1},Michel Salomon\sup{1}, RaphaÃ«l Couturier \sup{1}}
-\affiliation{\sup{1}FEMTO-ST Institute, UMR CNRS, University of Franche-Comte, Belfort, France}
-\email{\{ali.khadum, karine.deschinkel, michel.salomon, raphael.couturier\}@univ-fcomte.fr}
+\title{Distributed Coverage Optimization Protocol to Improve the Lifetime in Heterogeneous Energy Wireless Sensor Networks}
+
+
+% author names and affiliations
+% use a multiple column layout for up to three different
+% affiliations
+\author{\IEEEauthorblockN{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon and Raphael Couturier }
+\IEEEauthorblockA{FEMTO-ST Institute, UMR CNRS, University of Franche-Comte, Belfort, France \\
+Email:$\lbrace$ali.idness, karine.deschinkel, michel.salomon,raphael.couturier$\rbrace$@edu.univ-fcomte.fr 
 }
-\author{\authorname{~~}
-\affiliation{~~~~~}
-\email{~~~~~~~}
+%\email{\{ali.idness, karine.deschinkel, michel.salomon, raphael.couturier\}@univ-fcomte.fr}
+%\and
+%\IEEEauthorblockN{Homer Simpson}
+%\IEEEauthorblockA{FEMTO-ST Institute, UMR CNRS, University of Franche-Comte, Belfort, France}
+%\and
+%\IEEEauthorblockN{James Kirk\\ and Montgomery Scott}
+%\IEEEauthorblockA{FEMTO-ST Institute, UMR CNRS, University of Franche-Comte, Belfort, France}
 }
 
-\keywords{area coverage, wireless sensor networks, lifetime optimization.}
-%\vspace{3cm}
-\abstract{}
 
-\onecolumn \maketitle \normalsize 
-%\vspace{3cm}
-\section{\uppercase{Introduction}}
-\label{sec:introduction}
-%\vspace{1cm}
+\maketitle
+
+
+\begin{abstract}
+%\boldmath
+One of the fundamental challenges in Wireless Sensor Networks (WSNs) is Coverage preservation and extension of network lifetime continuously and effectively during monitoring a certain geographical area.In this paper 
+a distributed coverage optimization protocol to improve the lifetime in in Heterogeneous Energy Wireless Sensor Networks is proposed. The area of interest is divided into subregions using Divide-and-conquer method and an activity scheduling for sensor nodes is planned for each subregion.Our protocol is distributed in each subregion. It divides the network lifetime into activity rounds. In each round a small
+number of active nodes is selected to ensure coverage.Each round includes four phases: INFO Exchange, Leader election, decision and sensing.Simulation results show that the proposed protocol can prolong the network
+lifetime and improve network coverage effectively.
+
+
+\end{abstract}
+
+ %\keywords{Area Coverage, Wireless Sensor Networks, lifetime Optimization, Distributed Protocol.}
+
+ 
+ \IEEEpeerreviewmaketitle
+
+
+\section{Introduction}
 \noindent Recent years have witnessed significant advances in wireless sensor
 networks which emerge as one of the most promising technologies for
 the 21st century~\cite{asc02}. In fact, they present huge potential in
@@ -60,7 +95,7 @@ the lifetime of the network. In such a high density network, if all sensor nodes
 were to be activated at the same time, the lifetime would be reduced. Consequently, 
 future software may need to adapt appropriately to achieve acceptable quality of service for applications.  
 In this paper we concentrate on area coverage problem, with the objective of maximizing the network lifetime by using an adaptive scheduling. Area of interest is divided into subregions and an activity scheduling for sensor nodes is planned for each subregion.
-Our scheduling scheme works in period which includes a discovery phase to exchange information between sensors of the subregion, then a sensor is chosen in suitable manner to carry out a coverage strategy.  This coverage strategy involves the resolution of an integer program which provides the activation of the sensors for the $T$ next rounds, where $T$ is a parameter to adjust in efficient way.
+Our scheduling scheme works in period which includes a discovery phase to exchange information between sensors of the subregion, then a sensor is chosen in suitable manner to carry out a coverage strategy.  This coverage strategy involves the resolution of an integer program which provides the activation of the sensors for the $t$ next round.
 
 
 The remainder  of the paper is  organized as follows.
@@ -161,51 +196,49 @@ Our algorithm tends to limit the overcoverage of points of interest to avoid tur
 As mentioned in \cite{pc10}, both centralized and distributed algorithms have their own advantages and disadvantages. Centralized coverage algorithms have the advantage of requiring very low processing power from the sensor nodes which have usually limited processing capabilities. Distributed algorithms are very adaptable to the dynamic and scalable nature of sensors network. Authors in \cite{pc10} concludes that there is a threshold in terms of network size to switch from a localized to a centralized algorithm. Indeed the exchange of messages in large networks may consume  a considerable amount of energy in a localized approach compared to a centralized one. Our work does not consider only one leader to compute and to broadcast the schedule decision to all the sensors. When the size of network increases, the network is divided in many subregions and the decision is made by a leader in each subregion.
 \end{itemize}
 
-%compromis entre centralize et distribue (voir article \cite{pc10})
 
-\section{\uppercase{Distributed coverage model}}
+
+ \section{\uppercase{Distributed coverage model}}
 \label{pd}
-We consider a randomly and uniformly deployed network consisting of static wireless sensors. The wireless sensors are deployed in high density to ensure initially a full coverage of the interested area. We assume that all nodes are homogeneous in terms of energy, communication, and processing capabilities.  The location information is available to the sensor node either through hardware such as embedded GPS or through location discovery algorithms.
-The area of interest can be divided using the divide-and-conquer strategy into smaller area called subregions and then our coverage algorithm will be implemented in each subregion simultaneously. Our algorithm works in period fashion as in figure \ref{fig:4}. 
+We consider a randomly and uniformly deployed network consisting of static wireless sensors. The wireless sensors are deployed in high density to ensure initially a full coverage of the interested area. We assume that all nodes are homogeneous in terms of  communication and processing capabilities and heterogeneous in term of energy.  The location information is available to the sensor node either through hardware such as embedded GPS or through location discovery algorithms.
+The area of interest can be divided using the divide-and-conquer strategy into smaller area called subregions and then our coverage protocol will be implemented in each subregion simultaneously. Our protocol works in rounds fashion as in figure \ref{fig:4}. 
 %Given the interested Area $A$, the wireless sensor nodes set $S=\lbrace  s_1,\ldots,s_N \rbrace $ that are deployed randomly and uniformly in this area such that they are ensure a full coverage for A. The Area A is divided into regions $A=\lbrace A^1,\ldots,A^k,\ldots, A^{N_R} \rbrace$. We suppose that each sensor node $s_i$ know its location and its region. We will have a subset $SSET^k =\lbrace s_1,...,s_j,...,s_{N^k} \rbrace $ , where $s_N = s_{N^1} + s_{N^2} +,\ldots,+ s_{N^k} +,\ldots,+s_{N^R}$. Each sensor node $s_i$ has the same initial energy $IE_i$ in the first time and the current residual energy $RE_i$ equal to $IE_i$  in the first time for each $s_i$ in A. \\ 
 
 \begin{figure}[ht!]
 \centering
-\includegraphics [width=70mm]{Modelgeneral.eps}
+\includegraphics [width=70mm]{FirstModel.eps}
 \caption{Multi-Round Coverage Protocol}
 \label{fig:4}
 \end{figure} 
 
-Each period is divided into 4 phases : INFO Exchange, Leader Election, Decision, and Sensing. The sensing phase works also in rounds. For each round there is exactly one set cover responsible for sensing task. If a working node fails unexpectedly in the interval of time of the round, the sensing task of the network will be affected temporarily, since a new set cover will take charge of the sensing task in the next round. The energy consumption and some other constraints can easily be taken into account since the sensors can update and then exchange the information (including their residual energy) at the beginning of each period. However, the preprocessing phase (discovery, leader selection, decision) are energy consuming for some nodes even when they not join the network to monitor the area. We describe each phase in more detail.
+Each round is divided into 4 phases : INFO Exchange, Leader Election, Decision, and Sensing. For each round there is exactly one set cover responsible for sensing task. This protocol is more reliable against the  unexpectedly node failure because it works into rounds,and if the node failure detected before taking the decision, the node will not participate in decision and if the the node failure obtain after the decision   the sensing task of the network will be affected temporarily only during the period of sensing until starting new round, since a new set cover will take charge of the sensing task in the next round. The energy consumption and some other constraints can easily be taken into account since the sensors can update and then exchange the information (including their residual energy) at the beginning of each round. However, the preprocessing phase (INFO Exchange, leader Election, Decision) are energy consuming for some nodes even when they not join the network to monitor the area. We describe each phase in more detail.
 
+\subsection{\textbf INFO Exchange Phase}
 
+Each sensor node $j$  sends its position, remaining energy $RE_j$, number of local neighbours $NBR_j$  to all wireless sensor nodes in its subregion by using INFO packet and listen to the packets sent from other nodes. After that, each node will have information about all the sensor nodes in the subregion. In our model.
 
-\subsection{\textbf Discovery Phase}
-
-Each sensor node $j$  sends its position, remaining energy $RE_j$, number of local neighbours $NBR_j$  to all wireless sensor nodes in its subregion by using INFO packet and listen to the packets sent from other nodes. After that, each node will have information about all the sensor nodes in the subregion. In our model, the remaining energy corresponds to the time that a sensor can live in the active mode.
+% the remaining energy corresponds to the time that a sensor can live in the active mode.
 
 
 %\subsection{\textbf Working Phase:}
 
 %The working phase works in rounding fashion. Each round include 3 steps described as follow :
 
-\subsection{\textbf Leader Selection Phase}
+\subsection{\textbf Leader Election Phase}
 This step includes choosing the Wireless Sensor Node Leader (WSNL) which will be responsible of executing coverage algorithm  to choose the list of active sensor nodes that contribute in covering the subregion.
 % The sensors in the same region are capable to communicate with each others using a routing protocol provided by the simulator OMNET++ in order to provide multi-hop communication protocol.
 The WSNL will be chosen based on the number of local neighbours $NBR_j$  of sensor node $s_j$ and it's remaining energy $RE_j$.
-If we have more than one node has the same $NBR_j$ and $RE_j$, this leads to choose WSNL based on the largest index among them. Each subregion in the area of interest will select its WSNL independently for each period. 
+If we have more than one node has the same $NBR_j$ and $RE_j$, this leads to choose WSNL based on the largest index among them. Each subregion in the area of interest will select its WSNL independently for each round. 
 
 
 \subsection{\textbf Decision Phase}
-The WSNL will execute the algorithm  PEC to select which sensors will be activated in the next rounds to cover the subregion. WSNL will send Active-Sleep packet to each sensor in the subregion based on algorithm's results. 
+The WSNL will execute the GLPK algorithm to select which sensors will be activated in the next rounds to cover the subregion. WSNL will send Active-Sleep packet to each sensor in the subregion based on algorithm's results. 
 %The main goal in this step after choosing the WSNL is to produce the best representative active nodes set that will take the responsibility of covering the whole region $A^k$ with minimum number of sensor nodes to prolong the lifetime in the wireless sensor network. For our problem, in each round we need to select the minimum set of sensor nodes to improve the lifetime of the network and in the same time taking into account covering the region $A^k$ . We need an optimal solution with tradeoff between our two conflicting objectives.
 %The above region coverage problem can be formulated as a Multi-objective optimization problem and we can use the Binary Particle Swarm Optimization technique to solve it. 
 \\
 
 \subsection{\textbf Sensing Phase}
-This phase can be divided in many rounds. Let be $T$ the number of rounds. $T$ is an adjustable parameter. Active sensors in each round will execute their sensing task.
-%The algorithm will produce the best representative set of the active nodes that will take the mission of covering the sub region $A^k$  in Sensing step .    
-Since that we use a homogeneous wireless sensor network, we will assume that the cost of keeping a node awake for sensing task is the same for all wireless sensor nodes in the network.  
+ 	The algorithm will produce the best representative set of the active nodes that will take the mission of coverage preservation in the subregion during the Sensing phase. Since that we use a homogeneous wireless sensor network, we will assume that the cost of keeping a node awake (or sleep) for sensing task is the same for all wireless sensor nodes in the network.  
 
 
 
@@ -384,9 +417,48 @@ There are two main objectives. We limit overcoverage of principle points in orde
 %\end{itemize}
 \section{\uppercase{Simulation Results}}
 \label{exp}
-In this section, we evaluate the efficiency of PEC through conducting some simulations measuring the network lifetime with different number of sensors and different number of rounds for the sensing phase.
-Coverage ratio measures how much area of a sensor field is covered. In our case, the coverage ratio is regarded as the number of principle points covered among the set of all prinicple points. 
+In this section, we conducted a series of simulations to evaluate the efficiency of our approach
+based on the discrete event simulator OMNeT++ (http://www.omnetpp.org/).we conduct simulations for six
+different densities varying from 50 to 300 nodes. Experimental results were obtained from randomly generated
+networks in which nodes are deployed over a $ 50\times25(m2) $sensing field. For each network deployment, we
+assume that the deployed nodes can fully cover the sensing field with the given sensing range. 100 simulation runs are performed with different network topologies. The results presented hereafter are the average of these 100 runs.Simulation ends when there is at least one active node has no connectivity with the network.Our proposed coverage protocol use the Radio energy dissipation model that defined by~\cite{HeinzelmanCB02} as energy consumption model by each wireless sensor node for transmitting and receiving the packets in the network.The energy of each node in the network is initialized randomly within the range 24-60 joules, and each sensor will consumes 0.2 watts during the period of sensing which it is 60 seconds.Each active node will consumes 12 joules during sensing phase and each sleep node will consumes 0.002 joules.Each sensor node will not participate in the next round if it's remaining energy less than 12 joules. In all experiments the parameters are given by $R_s = 5m $ , $ W_{\Theta} =1$ and $W_{\Psi} = P^2$.
+We evaluate the efficiency of our approach using some performance metrics such as:coverage ratio, number of
+active nodes ratio, energy saving ratio, number of rounds, network lifetime and execution time of our approach.Coverage ratio measures how much area of a sensor field is covered. In our case, the coverage ratio is regarded as the number of principle points covered among the set of all principle points within the field.In our simulation the sensing field is sub divided into two subregions each one equal to $ 25\times25(m2)  $ of the sensing field.
+
+\subsection{The impact of the Number of Rounds on Coverage Ratio:} 
+In this experiment, we study the impact of the number of rounds on the coverage ratio and for different sizes for sensor network.For each Sensor network size we will take the average of coverage ratio per round and for 100 simulation.Fig. 3 show the impact of the number of rounds on coverage ratio for different network sizes and for two subregions.
+
+ \begin{figure}[h!]
+%\centering
+% \begin{multicols}{6}
+\centering
+%\includegraphics[scale=0.10]{fig21.pdf}\\~ ~ ~(a)
+%\includegraphics[scale=0.10]{fig22.pdf}\\~ ~ ~(b)
+\includegraphics[scale=0.5]{CR2R2L_1.eps}\\~ ~ ~(a)
+\includegraphics[scale=0.5]{CR2R2L_2.eps}\\~ ~ ~(b)
+%\includegraphics[scale=0.10]{fig26.pdf}\\~ ~ ~(e)
+%\includegraphics[scale=0.10]{fig27.pdf}\\~ ~ ~(f)
+%\end{multicols} 
+\caption{The impact of the Number of Rounds on Coverage Ratio.(a):subregion 1. (b): subregion 2 }
+\label{fig3}
+\end{figure} 
+
+As shown Fig. 3 (a) and (b) our protocol can give a full average coverage ratio in the first rounds and then it decreases when the number of rounds increases due to dead nodes.Although some nodes are dead, sensor activity scheduling choose other nodes to ensure the coverage of interest area. Moreover, when we have a dense sensor network, it leads to maintain the full coverage for larger number of rounds.
+
+\subsection{The impact of the Number of Rounds on Energy Saving Ratio:} 
+
+\subsection{The impact of the Number of Rounds on Active Sensor Ratio:} 
+
+\subsection{The impact of Number of Sensors on Number of Rounds:} 
+
+\subsection{The impact of Number of Sensors on Network Lifetime:} 
+
+\subsection{The impact of Number of Sensors on Execution Time:}
+
+\subsection{Performance Comparison:}
 \label{Simulation Results}
+
+
 \section{\uppercase{Conclusions}}
 \label{sec:conclusion}
 In this paper, we have addressed the problem of lifetime optimization in wireless sensor networks. This is a very 
@@ -399,9 +471,191 @@ To cope with this problem,
 
 
 
-\bibliographystyle{apalike}
-{\small
-\bibliography{bibliocap1}}
-\vfill
+
+
+% use section* for acknowledgement
+\section*{Acknowledgment}
+
+
+
+
+\begin{thebibliography}{1}
+
+
+\bibitem{wns07}
+  J. Wang, C. Niu, and R. Shen,~\emph{Randomized approach for target coverage scheduling in directional sensor network},~ICESS2007,~pp. 379-390,~2007.
+  
+
+
+\bibitem{dw60}
+  G.~B. Dantzig and P.~ Wolfe,~\emph {Decomposition principle for linear programs},~Operations Research,~pp. 101-111,~ 1960.
+ 
+
+\bibitem{asc02}
+ I.~ Akyildiz ,~ W.~ Su ,~ Y.~Sankarasubramniam and E. Cayirci,~ emph{A survey on sensor networks},~
+ IEEE Comm. Magazine,~pp. 102-114,~2002.
+ 
+
+\bibitem{pc10}
+ T.~V.~ Padmavathy and M. Chitra,~emph{Extending the Network Lifetime of Wireless Sensor Networks Using Residual Energy ExtractionâHybrid Scheduling Algorithm},~Int. J. of Communications, Network and System Sciences ,~ VOL.~3,No. ~1,pp.~98-106,~2010.
+  
+
+\bibitem{Slijepcevic01powerefficient}
+    S.~ Slijepcevic and M.~ Potkonjak,~emph{Power Efficient Organization of Wireless Sensor Networks},~
+    IEEE International conference on Communications,~pp.~472--476,~2001.
+
+\bibitem{Cardei:2005:IWS:1160086.1160098}
+ M.~Cardei, and D.~ Du, ~emph {Improving wireless sensor network lifetime through power aware organization},~
+ Wireless Networks,~ vol.~11,~ issue~3,~pp.~333--340,~May~2005.
+ 
+ 
+
+\bibitem{Cardei05energy-efficienttarget}
+ M.~ Cardei ,~ M.~ T.~ Thai,~ Y.~ Li and W.~ Wu,~ emph{Energy-efficient target coverage in wireless sensor networks},IEEE Infocom,~pp.~1976--1984,~2005.
+
+
+\bibitem{b04}
+ P.~ Berman,~ G.~ Calinescu ,~ C.~ Shah and A.~ Zelikovsky,~emph{Power efficient monitoring management in sensor networks},~Wireless Communications and Networking Conference,~ WCNC. ~2004,~2004.
+
+\bibitem{Berman05efficientenergy}
+   P.~ Berman ,~ G.~ Calinescu ,~ C.~ Shah and A. Zelikovsky,~emph {Efficient energy management in sensor networks},~ Ad Hoc and Sensor Networks. Nova Science Publishers,~Nova Science Publisher,~2005.
+
+\bibitem{1240799}
+ J.~ Lu and T.~ Suda,~ emph{Coverage-aware self-scheduling in sensor networks},~  Proceedings. 2003 IEEE 18th Annual Workshop on Computer Communications CCW 2003,~pp.~117-123,~2003.
+
+
+\bibitem{Prasad:2007:DAL:1782174.1782218}
+ S.~K.~Prasad, and A.~Dhawan,~emph{Distributed algorithms for lifetime of wireless sensor networks based on dependencies among cover sets},~Proceedings of the 14th international conference on High performance computing ,~HiPC'07,pp.~381--392,~2007.
+ 
+
+
+\bibitem{Zorbas:2010:SCP:1715927.1715956}
+ D.~ Zorbas ,~ D. Glynos ,~ P.~ Kotzanikolaou and C.~ Douligeris,~emph {Solving coverage problems in wireless sensor networks using cover sets},~ Ad Hoc Netw.,~ vol.~8,issue~4,~pp.~400--415,June~ 2010.
+ 
+
+\bibitem{Garey:1990:CIG:574848}
+ M.~R.~ Garey and D.~S.~ Johnson,~ emph {Computers and Intractability; A Guide to the Theory of NP-Completeness},~ W. H. Freeman  Co.,~1990.
+ 
+
+\bibitem{Caprara98algorithmsfor}
+   A.~ Caprara ,~ M.~ Fischetti and P.~ Toth ,~ emph{Algorithms for the Set Covering Problem},~ Annals of Operations Research,~  vol.~98,~1998.
+   
+   
+\bibitem{DBLP:journals/eor/AlfieriBBC07}
+ A.~Alfieri ,~ A.~ Bianco,~ P.~ Brandimarte and  C. Chiasserini,~emph {Maximizing system lifetime in wireless sensor networks},~European Journal of Operational Research,~ vol.~181,~  No.~1,~pp.390-402,~2007.
+ 
+
+\bibitem{Gu:2009:QTC:1673018.1673025}
+ Y.~ Gu,~ Y.~Ji,~ J. ~Li and B.~ Zhao,~emph {QoS-aware target coverage in wireless sensor networks},~Wirel. Commun. Mob. Comput.,~ vol.~9,~issue~12,~pp.~1645--1659,December~ 2009. 
+ 
+
+\bibitem{springerlink:10.1007/BF01594937}
+  Y.~ Ye,~emph{An O($n^3L$)) potential reduction algorithm for linear programming},~Mathematical Programming,~vol.~50,~issue~1,~  pp.~239-258,~ 1991.
+  
+
+
+\bibitem{glpk}
+  A.~ Mahkorin,~emph {GNU Linear Programming Kit, Reference Manual},~2010.
+
+\bibitem{Garg98fasterand}
+    N.~ Garg and J.~ K\"{o}nemann,~emph {Faster and simpler algorithms for multicommodity flow and other fractional packing problems},~In Proceedings of the 39th Annual Symposium on Foundations of Computer Science,pp.~300--309,~ 1998.
+    
+
+\bibitem{Kuhn55}
+  H.~ W.~ Kuhn,~emph{The Hungarian Method for the assignment problem},~Naval Research Logistics Quarterly,~vol.~2,~pp.~83--97,~1955.
+
+\bibitem{Cardei:2006:ECP:1646656.1646898}
+M.~Cardei and J.~ Wu,~emph {Energy-efficient coverage problems in wireless ad-hoc sensor networks},~Comput. Commun.,~ vol.~29,~ no.~4,~pp.~413--420,~February 2006.
+ 
+
+\bibitem{Manju2011}
+  M.~ Chaudhary and A.~ K. ~ Pujari,~emph{High-Energy-First (HEF) Heuristic for Energy-Efficient Target Coverage Problem},~International Journal of Ad hoc, Sensor and Uniquitous computing (IJASUC),~ vol.~2,
+ ~no. ~1,~ 2011.
+   
+ 
+
+\bibitem{Abrams:2004:SKA:984622.984684}
+Z.~ Abrams,~ A. ~ Goel and S.~ Plotkin, ~ emph{Set k-cover algorithms for energy efficient monitoring in wireless sensor networks},~ Proceedings of the 3rd international symposium on Information processing in sensor networks,~pp.~ 424--432,~ 2004.
+ 
+
+
+\bibitem{Zorbas2007}
+D.~ Zorbas ,~ D.~ Glynos ,~ P.~ Kotzanikolaou and C.~ Douligeris,~emph {B\{GOP\}: an adaptive coverage algorithm for wireless sensor networks},~Proceedings of the 13th European Wireless Conference,~Paris,~ France,~2007. 
+
+\bibitem{cardei02}
+ M.~ Cardei ,~D.~ MacCallum ,~M.~ X.~ Cheng ,~M.~ Min ,~X.~ Jia ,~D.~ Li and
+               D.~ Du,~emph {Wireless Sensor Networks with Energy Efficient Organization},~Journal of Interconnection Networks,~  vol.~3,~  no.~3-4,pp.~213-229,~2002.
+  
+ 
+
+\bibitem{cardei05}
+  M.~ Cardei ,~ J.~ Wu, N.~ Lu and M.~O.~ Pervaiz,~emph{Maximum Network Lifetime with Adjustable Range},~WiMob,~2005.
+  
+
+\bibitem{cardei05bis}
+  M.~ Cardei ,~ M.~ T.~ Thai ,~Y.~ Li and W.~ Wu,~emph {Energy-efficient target coverage in wireless sensor networks},~INFOCOM,~pp.~1976-1984,~2005.
+  
+ 
+
+\bibitem{berman04}
+   P.~ Berman and G.~ Calinescu,~emph {Power Efficient Monitoring Management in Sensor Networks},~Proceedings of IEEE Wireless Communication and Networking Conference (WCNC'04),~pp.~2329--2334,~2004.
+
+
+\bibitem{garg98}
+ N.~ Garg and J.~ Koenemann,~emph {Faster and Simpler Algorithms for Multicommodity Flow and other Fractional Packing Problems.},~Proceedings of the 39th Annual Symposium on Foundations of Computer Science,~pp.~300--309,~1998.
+ 
+
+\bibitem{Gallais06}
+ A.~ Gallais,~ J.~  Carle ,~D. ~ Simplot-Ryl and I.~ Stojmenovic,~emph {Localized Sensor Area Coverage with Low Communication Overhead},~Proceedings of the Fourth Annual IEEE International Conference on Pervasive Computing and Communications,pp.~328--337,~2006. 
+
+\bibitem{Tian02}
+ D. ~ Tian and N.~ D.~ Georganas, ~emph {A coverage-preserving node scheduling scheme for large wireless sensor networks},~Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications,~pp.~ 32--41,~2002.
+  
+
+\bibitem{Ye03}
+F. ~Ye,G.~ Zhong,~J.~ Cheng,~S.~ Lu and L. ~ Zhang,~emph {PEAS: A Robust Energy Conserving Protocol for Long-lived Sensor Networks},~ Proceedings of the 23rd International Conference on Distributed Computing Systems ICDCS'03,~pp.~ 28--37,~2003.
+
+ 
+
+\bibitem{Huang:2003:CPW:941350.941367}
+ C. ~ Huang and Y. ~ Tseng,~emph {The coverage problem in a wireless sensor network},~Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications WSNA '03,~San Diego, CA, USA,~pp.~115-121,~ 2003.
+ 
+
+
+
+\bibitem{Zhang05}
+ H.~ Zhang and J.~ C.~ Hou,~emph {Maintaining Sensing Coverage and Connectivity in Large Sensor
+               Networks},~Ad Hoc {\&} Sensor Wireless Networks,~ vol.~1,no.~ 1-2,~2005.
+
+
+\bibitem{HeinzelmanCB02}
+ W.~ B.~ Heinzelman ,~ A.~ P.~ Chandrakasan and H.~ Balakrishnan,~emph {An application-specific protocol architecture for wireless microsensor networks},~IEEE Transactions on Wireless Communications,~ vol. ~1,~no. ~4,pp.~ 660-670,~2002.
+
+
+
+\bibitem{pedraza2006}
+	F. ~Pedraza,~A.~ L.~ Medaglia and A. ~ Garcia,~emph {Efficient coverage algorithms for wireless sensor networks},~Systems and Information Engineering Design Symposium,~ pp.~ 78 --83,~2006.
+
+
+\bibitem{chin2007}
+C.~ T. ~ Vu, ~ emph {An Energy-Efficient Distributed Algorithm for k-Coverage Problem in Wireless Sensor Networks},~GeorgiaState University,~2007.
+
+
+
+
+
+
+
+
+
+\end{thebibliography}
+
+
+ 
+
+
+
+% that's all folks
 \end{document}
 
+
