X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/blobdiff_plain/e832185b393938b6d60c1a5a0a2bebd99854443c..39b48f032a13fc6524febe806cb05a43d808f416:/bare_conf.tex

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 %
 % paper title
 % can use linebreaks \\ within to get better formatting as desired
-\title{Coverage and Lifetime Optimization in Heterogeneous Energy Wireless Sensor Networks}
+\title{Coverage and Lifetime Optimization \\
+in Heterogeneous Energy Wireless Sensor Networks}
 
-\author{\IEEEauthorblockN{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier}
+\author{\IEEEauthorblockN{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, 
+and Rapha\"el Couturier}
 \IEEEauthorblockA{FEMTO-ST Institute, UMR 6174 CNRS \\
 University of Franche-Comt\'e  \\
 Belfort, France\\
-Email: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}}
+Email: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, 
+raphael.couturier$\rbrace$@univ-fcomte.fr}}
 
 \maketitle
 
-
 \begin{abstract}
 One of  the fundamental challenges in Wireless  Sensor Networks (WSNs)
-is  the coverage  preservation  and  the extension  of  the  network  lifetime
+is the coverage preservation and the extension of the network lifetime
 continuously  and  effectively  when  monitoring a  certain  area  (or
-region) of interest. In this paper, a coverage optimization protocol to
-improve the lifetime in  heterogeneous energy wireless sensor networks
-is proposed.   The area of  interest is first divided  into subregions
-using a  divide-and-conquer method and then the scheduling  of sensor node
-activity  is  planned for  each  subregion.   The proposed  scheduling
-considers  rounds during  which  a small  number  of nodes,  remaining
-active  for  sensing, is  selected  to  ensure  coverage.  Each  round
-consists  of   four  phases:  (i)~Information   Exchange,  (ii)~Leader
+region) of  interest. In this paper, a  coverage optimization protocol
+to  improve  the  lifetime  in heterogeneous  energy  wireless  sensor
+networks  is proposed.   The area  of interest  is first  divided into
+subregions using  a divide-and-conquer method and  then the scheduling
+of sensor node  activity is planned for each  subregion.  The proposed
+scheduling  considers rounds  during which  a small  number  of nodes,
+remaining active  for sensing, is  selected to ensure  coverage.  Each
+round consists  of four phases:  (i)~Information Exchange, (ii)~Leader
 Election, (iii)~Decision,  and (iv)~Sensing.  The  decision process is
-carried  out  by  a  leader  node, which  solves  an  integer  program.
+carried  out  by a  leader  node,  which  solves an  integer  program.
 Simulation  results show that  the proposed  approach can  prolong the
 network lifetime and improve the coverage performance.
 \end{abstract}
 
 \begin{IEEEkeywords}
-Wireless Sensor Networks, Area Coverage, Network lifetime, Optimization, Scheduling.
+Wireless   Sensor   Networks,   Area   Coverage,   Network   lifetime,
+Optimization, Scheduling.
 \end{IEEEkeywords}
 %\keywords{Area Coverage, Network lifetime, Optimization, Distributed Protocol}
  
 \IEEEpeerreviewmaketitle
 
-
 \section{Introduction}
 
-\noindent The fast developments in the low-cost sensor devices and wireless communications have allowed the emergence the WSNs. WSN includes a large number of small , limited-power sensors  that can sense, process and transmit
- data over a wireless communication . They communicate with each other by using multi-hop wireless communications , cooperate together to monitor the area of interest, and the measured data can be reported
- to a monitoring center
-called, sink, for analysis it~\cite{Ammari01, Sudip03}. There are several applications used the WSN including health, home, environmental, military,and industrial applications~\cite{Akyildiz02}.
-The coverage  problem is one of the fundamental challenges in WSNs~\cite{Nayak04} that consists in monitoring efficiently and  continuously the area of interest. The limited energy of sensors represents the main challenge in the WSNs design~\cite{Ammari01}, where it is difficult to replace and/or
- recharge their batteries because the the area of interest nature (such as hostile environments) and the cost. So, it is necessary that a WSN deployed with high density  because spatial redundancy can then be exploited to increase the lifetime of the network . However, turn on all the sensor nodes, which monitor the same region at the same time leads to decrease the lifetime of the network. To extend the lifetime of the network, the main idea is to take advantage of the overlapping sensing regions of some sensor nodes to save energy by turning off some of them during the sensing phase~\cite{Misra05}. WSNs require energy-efficient solutions to improve the network lifetime that is constrained by the limited power of each sensor node ~\cite{Akyildiz02}. 
-In  this  paper,  we  concentrate on  the area
+\noindent  The fast developments  in the  low-cost sensor  devices and
+wireless  communications  have allowed  the  emergence  the WSNs.  WSN
+includes  a large  number of  small ,  limited-power sensors  that can
+sense, process and transmit data  over a wireless communication . They
+communicate with each other by using multi-hop wireless communications
+, cooperate together to monitor the area of interest, and the measured
+data can be reported to a monitoring center called, sink, for analysis
+it~\cite{Ammari01, Sudip03}.  There are several  applications used the
+WSN  including health,  home,  environmental, military,and  industrial
+applications~\cite{Akyildiz02}.   The coverage problem  is one  of the
+fundamental  challenges   in  WSNs~\cite{Nayak04}  that   consists  in
+monitoring  efficiently and  continuously  the area  of interest.  The
+limited energy  of sensors represents  the main challenge in  the WSNs
+design~\cite{Ammari01},  where  it  is  difficult  to  replace  and/or
+recharge their batteries because the the area of interest nature (such
+as hostile environments) and the cost.  So, it is necessary that a WSN
+deployed  with high  density because  spatial redundancy  can  then be
+exploited to increase  the lifetime of the network  . However, turn on
+all the sensor  nodes, which monitor the same region  at the same time
+leads to decrease the lifetime  of the network. To extend the lifetime
+of the network, the main idea  is to take advantage of the overlapping
+sensing regions  of some  sensor nodes to  save energy by  turning off
+some  of them  during the  sensing phase~\cite{Misra05}.  WSNs require
+energy-efficient  solutions to  improve the  network lifetime  that is
+constrained   by    the   limited   power   of    each   sensor   node
+~\cite{Akyildiz02}.   In  this  paper,  we  concentrate  on  the  area
 coverage  problem,  with  the  objective  of  maximizing  the  network
 lifetime  by using  an adaptive  scheduling. The  area of  interest is
 divided into subregions and an activity scheduling for sensor nodes is
-planned for  each subregion. 
- In fact, the nodes in a  subregion can be seen as a cluster where
-  each node sends  sensing data to the cluster head  or the sink node.
-  Furthermore, the activities in a subregion/cluster can continue even
-  if another cluster stops due to too many node failures.
-Our scheduling  scheme considers rounds,  where a round starts  with a
-discovery  phase  to  exchange  information  between  sensors  of  the
-subregion,  in order to  choose in  a suitable manner  a sensor  node to
-carry  out a coverage  strategy. This  coverage strategy  involves the
-solving of  an integer  program, which provides  the activation  of the
-sensors for the sensing phase of the current round.
+planned for each subregion.  In fact,  the nodes in a subregion can be
+seen as  a cluster where each  node sends sensing data  to the cluster
+head   or  the   sink  node.    Furthermore,  the   activities   in  a
+subregion/cluster can  continue even if  another cluster stops  due to
+too many node failures.  Our scheduling scheme considers rounds, where
+a round starts with a  discovery phase to exchange information between
+sensors of  the subregion, in order  to choose in a  suitable manner a
+sensor node to  carry out a coverage strategy.  This coverage strategy
+involves  the  solving  of  an  integer program,  which  provides  the
+activation of the sensors for the sensing phase of the current round.
 
 The remainder of the paper is organized as follows.  The next section
 % Section~\ref{rw}
 reviews the related work in the field.  Section~\ref{pd} is devoted to
 the    scheduling     strategy    for    energy-efficient    coverage.
-Section~\ref{cp} gives the coverage model formulation, which is used to
-schedule  the  activation  of  sensors.  Section~\ref{exp}  shows  the
-simulation  results obtained  using  the discrete  event simulator OMNeT++  \cite{varga}. They  fully demonstrate  the usefulness  of the
-proposed  approach.   Finally, we  give  concluding  remarks and  some
-suggestions for future works in Section~\ref{sec:conclusion}.
-
+Section~\ref{cp} gives  the coverage model formulation,  which is used
+to schedule  the activation  of sensors.  Section~\ref{exp}  shows the
+simulation results obtained using the discrete event simulator OMNeT++
+\cite{varga}. They  fully demonstrate  the usefulness of  the proposed
+approach.  Finally,  we give  concluding remarks and  some suggestions
+for future works in Section~\ref{sec:conclusion}.
 
 \section{Related works}
 \label{rw}
-\indent In this section, we only review some recent work with the coverage lifetime maximization  problem, where the  objective is to  optimally schedule sensors'  activities in
-order to  extend network lifetime  in WSNS. Authors  in \cite{chin2007}  propose  a novel
-distributed  heuristic named  Distributed  Energy-efficient Scheduling
-for k-coverage  (DESK) so  that the energy  consumption among  all the
-sensors  is balanced,  and  network lifetime  is  maximized while  the
-coverage requirement  is being  maintained.  This algorithm  works in
-round, requires only  1-sensing-hop-neighbor information, and a sensor
-decides  its status  (active/sleep)  based on  the perimeter  coverage
-model, which  proposed in \cite{Huang:2003:CPW:941350.941367}. 
-Shibo et al.\cite{Shibo} studied the coverage problem, which is formulated as a minimum weight submodular set cover problem. To address this problem, 
- a distributed truncated greedy algorithm (DTGA) is proposed. They exploited from the  
-temporal and spatialcorrelations among the data sensed by different sensor nodes and leverage
-prediction to extend the WSNs lifetime.
-Bang et al. \cite{Bang} proposed a coverage-aware clustering protocol(CACP), which used computation method for the optimal cluster size to minimize the average energy consumption rate per unit area. They defied in this protocol a cost metric that prefer the redundant sensors 
-with higher power as best candidates for cluster heads and select the active sensors that cover the area of interest more efficiently.
-Zhixin et al. \cite{Zhixin} propose a Distributed Energy-
-Efficient Clustering with Improved Coverage(DEECIC) algorithm
-which aims at clustering with the least number of cluster
-heads to cover the whole network and assigning a unique ID
-to each node based on local information. In addition, this
-protocol periodically updates cluster heads according to the
-joint information of nodes $â $residual energy and distribution.
-Although DEECIC does not require knowledge of a node's
-geographic location, it guarantees full coverage of the
-network. However, the protocol does not make any activity
-scheduling to set redundant sensors in passive mode in order
-to conserve energy. C. Liu and G. Cao \cite{Changlei} studied how to
-schedule sensor active time to maximize their coverage during a specified network lifetime. Their objective is to maximize the spatial-temporal coverage by scheduling sensors activity after they have been deployed. They proposed both centralized and distributed algorithms. The distributed parallel optimization protocol can ensure each sensor to converge to local optimality without conflict with each other. S. Misra et al. \cite{Misra} proposed a localized algorithm for coverage in sensor
-networks. The algorithm conserve the energy while ensuring the network coverage by activating the subset of sensors, with the minimum overlap area.The proposed method preserves
-the network connectivity by formation of the network backbone. L. Zhang et al. \cite{Zhang} presented a novel distributed clustering algorithm
-called Adaptive Energy Efficient Clustering (AEEC) to maximize network lifetime. In this study, they are introduced an optimization, which includes restricted global re-clustering,
-intra-cluster node sleeping scheduling and adaptive
-transmission range adjustment to conserve the energy, while connectivity and coverage is ensured. J. A. Torkestani \cite{Torkestani} proposed a learning automata-based energy-efficient coverage protocol
- named as LAEEC to construct the degree-constrained connected dominating set (DCDS) in WSNs. He shows that the correct choice of the degree-constraint of DCDS balances the network load on the active nodes and leads to enhance the coverage and network lifetime.
+\indent In this section, we only review some recent works dealing with
+the coverage lifetime maximization  problem, where the objective is to
+optimally  schedule  sensors'  activities  in  order  to  extend  WSNs
+lifetime.
+
+In \cite{chin2007}  is proposed a novel  distributed heuristic, called
+Distributed Energy-efficient  Scheduling for k-coverage  (DESK), which
+ensures that the energy consumption  among the sensors is balanced and
+the lifetime  maximized while the coverage  requirement is maintained.
+This  heuristic   works  in  rounds,  requires   only  1-hop  neighbor
+information,  and each  sensor decides  its status  (active  or sleep)
+based    on    the    perimeter    coverage    model    proposed    in
+\cite{Huang:2003:CPW:941350.941367}.    More    recently,   Shibo   et
+al. \cite{Shibo}  expressed the coverage  problem as a  minimum weight
+submodular  set cover  problem  and proposed  a Distributed  Truncated
+Greedy Algorithm  (DTGA) to  solve it. They  take advantage  from both
+temporal  and spatial  correlations between  data sensed  by different
+sensors,  and   leverage  prediction,  to  improve   the  lifetime.  A
+Coverage-Aware  Clustering Protocol (CACP),  which uses  a computation
+method  to  find  the  cluster  size  minimizing  the  average  energy
+consumption rate  per unit area, has  been proposed by Bang  et al. in
+\cite{Bang}. Their protocol is based on a cost metric that selects the
+redundant  sensors with higher  power as  best candidates  for cluster
+heads and the active sensors that  cover the area of interest the more
+efficiently.
+
+% TO BE CONTINUED
+
+Zhixin et  al. \cite{Zhixin}  propose a Distributed  Energy- Efficient
+Clustering  with  Improved Coverage(DEECIC)  algorithm  which aims  at
+clustering with the  least number of cluster heads  to cover the whole
+network  and  assigning  a unique  ID  to  each  node based  on  local
+information. In  addition, this protocol  periodically updates cluster
+heads according to the joint  information of nodes $â $residual energy
+and  distribution.  Although DEECIC  does not  require knowledge  of a
+node's  geographic  location,  it  guarantees  full  coverage  of  the
+network. However,  the protocol does not make  any activity scheduling
+to set redundant sensors in passive mode in order to conserve energy.
+
+C.  Liu and  G. Cao  \cite{Changlei}  studied how  to schedule  sensor
+active  time to  maximize their  coverage during  a  specified network
+lifetime. Their objective is to maximize the spatial-temporal coverage
+by  scheduling sensors activity  after they  have been  deployed. They
+proposed both centralized  and distributed algorithms. The distributed
+parallel optimization  protocol can ensure each sensor  to converge to
+local optimality without conflict with each other.
+
+S.  Misra  et al.  \cite{Misra}  proposed  a  localized algorithm  for
+coverage in  sensor networks. The algorithm conserve  the energy while
+ensuring  the network coverage  by activating  the subset  of sensors,
+with  the  minimum  overlap  area.The proposed  method  preserves  the
+network connectivity by formation of the network backbone.
+
+L. Zhang et al.  \cite{Zhang} presented a novel distributed clustering
+algorithm  called  Adaptive  Energy  Efficient  Clustering  (AEEC)  to
+maximize  network lifetime.  In  this study,  they  are introduced  an
+optimization,   which   includes   restricted  global   re-clustering,
+intra-cluster node sleeping scheduling and adaptive transmission range
+adjustment to conserve the  energy, while connectivity and coverage is
+ensured.
+
+J. A. Torkestani  \cite{Torkestani} proposed a learning automata-based
+energy-efficient  coverage protocol  named as  LAEEC to  construct the
+degree-constrained connected  dominating set (DCDS) in  WSNs. He shows
+that the correct choice of  the degree-constraint of DCDS balances the
+network load on the active nodes and leads to enhance the coverage and
+network lifetime.
  
-The main contribution of our approach addresses three main questions to build a
-scheduling strategy. We  give a brief answer to  these three questions
-to describe our  approach before going into details  in the subsequent
-sections.
+The main  contribution of our approach addresses  three main questions
+to build a scheduling strategy:
 %\begin{itemize}
 %\item 
 {\bf How must the  phases for information exchange, decision and
