X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/blobdiff_plain/061f5a89a6425bb554e24d1136ddf783f717a780..e832185b393938b6d60c1a5a0a2bebd99854443c:/bare_conf.tex?ds=inline

diff --git a/bare_conf.tex b/bare_conf.tex
index 2a5e6c4..844f237 100644
--- a/bare_conf.tex
+++ b/bare_conf.tex
@@ -12,6 +12,7 @@
 \hyphenation{op-tical net-works semi-conduc-tor}
 
 
+\usepackage{etoolbox}
 \usepackage{float} 
 \usepackage{epsfig}
 \usepackage{calc}
@@ -74,17 +75,13 @@ network lifetime and improve the coverage performance.
 \end{abstract}
 
 \begin{IEEEkeywords}
-Area Coverage, Network lifetime, Optimization, Scheduling, Distributed Protocol.
+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
@@ -122,232 +119,62 @@ suggestions for future works in Section~\ref{sec:conclusion}.
 
 \section{Related works}
 \label{rw}
-
-\noindent This section is dedicated to the various approaches proposed
-in  the literature  for  the coverage  lifetime maximization  problem,
-where the  objective is to  optimally schedule sensors'  activities in
-order to  extend network lifetime  in a randomly deployed  network. As
-this problem is subject to a wide range of interpretations, we have chosen
-to recall the main definitions and assumptions related to our work.
-
-%\begin{itemize}
-%\item Area Coverage: The main objective is to cover an area. The area coverage requires
-%that the sensing range of working Active nodes cover the whole targeting area, which means any
-%point in target area can be covered~\cite{Mihaela02,Raymond03}.
-
-%\item Target Coverage: The objective is to cover a set of targets. Target coverage means that the discrete target points can be covered in any time. The sensing range of working Active nodes only monitors a finite number of discrete points in targeting area~\cite{Mihaela02,Raymond03}. 
-
-%\item Barrier Coverage An objective to determine the maximal support/breach paths that traverse a sensor field. Barrier coverage is expressed as finding one or more routes with starting position and ending position when the targets pass through the area deployed with sensor nodes~\cite{Santosh04,Ai05}.
-%\end{itemize}
-\subsection{Coverage} 
-%{\bf Coverage}
-
-The most  discussed coverage problems in literature  can be classified
-into two types \cite{ma10}: area coverage (also called full or blanket
-coverage) and target coverage.  An  area coverage problem is to find a
-minimum number of sensors to work, such that each physical point in the
-area is within the sensing range  of at least one working sensor node.
-Target coverage problem  is to cover only a  finite number of discrete
-points  called targets.   This type  of coverage  has  mainly military
-applications. Our work will concentrate on the area coverage by design
-and implementation of a  strategy, which efficiently selects the active
-nodes   that  must   maintain  both   sensing  coverage   and  network
-connectivity and at the same time improve the lifetime of the wireless
-sensor  network.   But,  requiring  that  all physical  points  of  the
-considered region are covered may  be too strict, especially where the
-sensor network is not dense.   Our approach represents an area covered
-by a sensor as a set of primary points and tries to maximize the total
-number  of  primary points  that  are  covered  in each  round,  while
-minimizing  overcoverage (points  covered by  multiple  active sensors
-simultaneously).
-
-\subsection{Lifetime} 
-%{\bf Lifetime}
-
-Various   definitions   exist   for   the   lifetime   of   a   sensor
-network~\cite{die09}.  The main definitions proposed in the literature are
-related to the  remaining energy of the nodes or  to the coverage percentage. 
-The lifetime of the  network is mainly defined as the amount
-of  time during which  the network  can  satisfy its  coverage objective  (the
-amount of  time that the network  can cover a given  percentage of its
-area or targets of interest). In this work, we assume that the network
-is alive  until all  nodes have  been drained of  their energy  or the
-sensor network becomes disconnected, and we measure the coverage ratio
-during the WSN lifetime.  Network connectivity is important because an
-active sensor node without  connectivity towards a base station cannot
-transmit information on an event in the area that it monitors.
-
-\subsection{Activity scheduling} 
-%{\bf Activity scheduling}
-
-Activity scheduling is to  schedule the activation and deactivation of
-sensor nodes.  The  basic objective is to decide  which sensors are in
-what states (active or sleeping mode)  and for how long, so that the
-application  coverage requirement  can be  guaranteed and  the network
-lifetime can be  prolonged. Various approaches, including centralized,
-distributed, and localized algorithms, have been proposed for activity
-scheduling.  In  distributed algorithms, each node  in the network
-autonomously makes decisions on whether  to turn on or turn off itself
-only using  local neighbor information. In  centralized algorithms, a
-central controller  (a node or  base station) informs every  sensors of
-the time intervals to be activated.
-
-\subsection{Distributed approaches}
-%{\bf Distributed approaches}
-
-Some      distributed     algorithms      have      been     developed
-in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02}  to perform the
-scheduling.   Distributed algorithms typically  operate in  rounds for
-a predetermined  duration. At  the  beginning of  each  round, a  sensor
-exchanges information with its neighbors and makes a decision to either
-remain turned  on or to  go to sleep  for the round. This  decision is
-basically made on simple greedy criteria like  the largest uncovered
-area   \cite{Berman05efficientenergy},   maximum   uncovered   targets
-\cite{1240799}.   In \cite{Tian02}, the  scheduling scheme  is divided
-into rounds, where each round  has a self-scheduling phase followed by
-a sensing phase.  Each sensor  broadcasts a message containing the node ID
-and the node location  to its neighbors at the beginning  of each round. A
-sensor determines  its status by  a rule named off-duty  eligible rule,
-which tells  him to  turn off if  its sensing  area is covered  by its
-neighbors. A  back-off scheme is  introduced to let each  sensor delay
-the decision process  with a random period of time,  in order to avoid
-simultaneous conflicting decisions between nodes and  lack of coverage on any area.
-\cite{Prasad:2007:DAL:1782174.1782218}  defines a model  for capturing
-the dependencies  between different cover sets  and proposes localized
-heuristic  based on this  dependency.  The  algorithm consists  of two
-phases, an initial  setup phase during which each  sensor computes and
-prioritizes the  covers and  a sensing phase  during which  each sensor
-first decides  its on/off status, and  then remains on or  off for the
-rest  of the  duration.  Authors  in \cite{chin2007}  propose  a novel
+\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  its perimeter  coverage
-computed  through the k-Non-Unit-disk  coverage algorithm  proposed in
-\cite{Huang:2003:CPW:941350.941367}.
-
-Some other approaches do  not consider a synchronized and predetermined
-period  of time  where the  sensors are  active or  not.  Indeed, each
-sensor  maintains its  own timer  and its  wake-up time is randomized
-\cite{Ye03} or regulated \cite{cardei05} over time.
-%A ecrire \cite{Abrams:2004:SKA:984622.984684}p33
-
-%The scheduling information is disseminated throughout the network and only sensors in the active state are responsible
-%for monitoring all targets, while all other nodes are in a low-energy sleep mode. The nodes decide cooperatively which of them will remain in sleep mode for a certain
-%period of time.
-
- %one way of increasing lifeteime is by turning off redundant nodes to sleep mode to conserve energy while active nodes provide essential coverage, which improves fault tolerance. 
-
-%In this paper we focus on centralized algorithms because distributed algorithms are outside the scope of our work. Note that centralized coverage algorithms have the advantage of requiring very low processing power from the sensor nodes which have usually limited processing capabilities. Moreover, a recent study conducted 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. 
-
-\subsection{Centralized approaches}
-%{\bf Centralized approaches}
-
-Power  efficient  centralized  schemes  differ  according  to  several
-criteria \cite{Cardei:2006:ECP:1646656.1646898},  such as the coverage
-objective  (target coverage  or  area coverage),  the node  deployment
-method (random or deterministic) and the heterogeneity of sensor nodes
-(common sensing range, common battery lifetime). The major approach is
-to divide/organize  the sensors into  a suitable number of  set covers
-where each  set completely covers  an interest region and  to activate
-these set covers successively.
-
-The first algorithms  proposed in the  literature consider that  the cover
-sets  are  disjoint: a  sensor  node appears  in  exactly  one of  the
-generated  cover  sets.    For  instance,  Slijepcevic  and  Potkonjak
-\cite{Slijepcevic01powerefficient}   propose    an   algorithm, which
-allocates sensor nodes in mutually independent sets to monitor an area
-divided into  several fields.  Their algorithm builds  a cover  set by
-including in  priority the sensor  nodes, which cover  critical fields,
-that  is to  say fields  that are  covered by  the smallest  number of
-sensors. The time complexity of  their heuristic is $O(n^2)$ where $n$
-is the number of  sensors. In~\cite{cardei02}, a graph coloring
-technique is described to achieve energy  savings by  organizing the  sensor nodes
-into a maximum number of  disjoint dominating sets, which are activated
-successively. The dominating sets do not guarantee the coverage of the
-whole        region        of        interest.        Abrams        et
-al.~\cite{Abrams:2004:SKA:984622.984684}  design  three  approximation
-algorithms  for a  variation of  the  set k-cover  problem, where  the
-objective is to partition the sensors into covers such that the number
-of covers that  includes an area, summed over  all areas, is maximized.
-Their        work        builds        upon       previous        work
-in~\cite{Slijepcevic01powerefficient} and the  generated cover sets do
-not provide complete coverage of the monitoring zone.
-
-%examine the target coverage problem by disjoint cover sets but relax the requirement that every  cover set monitor all the targets and try to maximize the number of times the targets are covered by the partition. They propose various algorithms and establish approximation ratio.
-
-In~\cite{Cardei:2005:IWS:1160086.1160098},   the  authors   propose  a
-heuristic  to compute  the  disjoint  set covers  (DSC).  In order  to
-compute the maximum number of  covers, they first transform DSC into a
-maximum-flow problem, which  is then formulated  as a  mixed integer
-programming  problem (MIP).  Based on  the solution  of the  MIP, they
-design a heuristic to compute  the final number of covers. The results
-show  a slight  performance  improvement  in terms  of  the number  of
-produced  DSC in comparison  to~\cite{Slijepcevic01powerefficient}, but
-it incurs  higher execution  time due to  the complexity of  the mixed
-integer      programming     solving.       %Cardei      and     Du
-\cite{Cardei:2005:IWS:1160086.1160098} propose a method to efficiently
-compute the maximum  number of disjoint set covers  such that each set
-can  monitor all  targets. They  first  transform the  problem into  a
-maximum  flow   problem, which  is  formulated  as   a  mixed  integer
-programming (MIP). Then their heuristic  uses the output of the MIP to
-compute  disjoint  set  covers.  Results  show  that  this  heuristic
-provides  a   number  of  set  covers  slightly   larger  compared  to
-\cite{Slijepcevic01powerefficient}  but with  a larger  execution time
-due  to the complexity  of the  mixed integer  programming resolution.
-Zorbas  et  al.  \cite{Zorbas2007}  present  B\{GOP\},  a  centralized
-coverage   algorithm  introducing   sensor   candidate  categorization
-depending on their  coverage status and the notion  of critical target
-to  call  targets   that  are  associated  with  a   small  number  of
-sensors. The total running time of their heuristic is $0(m n^2)$ where
-$n$ is the number of sensors,  and $m$ the number of targets. Compared
-to    algorithm's    results     of    Slijepcevic    and    Potkonjak
-\cite{Slijepcevic01powerefficient},  their   heuristic  produces  more
-cover sets with a slight growth rate in execution time.
-%More recently Manju and Pujari\cite{Manju2011}
-
-In the  case of non-disjoint algorithms  \cite{Manju2011}, sensors may
-participate  in more  than  one cover  set.   In some  cases, this  may
-prolong  the lifetime  of the  network in  comparison to  the disjoint
-cover set algorithms, but  designing algorithms for non-disjoint cover
-sets  generally induces a  higher order  of complexity.   Moreover, in
-case of a sensor's  failure, non-disjoint scheduling policies are less
-resilient and less  reliable because a sensor may  be involved in more
-than one  cover sets.  For instance,  Cardei et al.~\cite{cardei05bis}
-present a  linear programming (LP)  solution and a greedy  approach to
-extend the  sensor network lifetime  by organizing the sensors  into a
-maximal  number of  non-disjoint cover  sets. Simulation  results show
-that by allowing sensors to  participate in multiple sets, the network
-lifetime         increases        compared         with        related
-work~\cite{Cardei:2005:IWS:1160086.1160098}.   In~\cite{berman04}, the
-authors  have formulated  the lifetime  problem and  suggested another
-(LP)  technique to  solve this  problem. A  centralized  solution  based      on      the     Garg-K\"{o}nemann
-algorithm~\cite{garg98}, provably near
-the optimal solution,    is also proposed.
-
-\subsection{Our contribution}
-%{\bf Our contribution}
-
-There are  three main questions, which should be addressed  to build a
+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.
+ 
+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.
-\begin{itemize}
-\item {\bf How must the  phases for information exchange, decision and
+%\begin{itemize}
+%\item 
+{\bf How must the  phases for information exchange, decision and
   sensing be planned over time?}   Our algorithm divides the time line
   into a number  of rounds. Each round contains  4 phases: Information
   Exchange, Leader Election, Decision, and Sensing.
 
-\item {\bf What are the rules to decide which node has to be turned on
+%\item 
+{\bf What are the rules to decide which node has to be turned on
   or off?}  Our algorithm tends to limit the overcoverage of points of
   interest  to avoid  turning on  too many sensors covering  the same
   areas  at the  same time,  and tries  to prevent  undercoverage. The
   decision  is  a  good   compromise  between  these  two  conflicting
   objectives.
 
-\item {\bf Which  node should make such a  decision?}  As mentioned in
+%\item 
+{\bf Which  node should make such a  decision?}  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
@@ -362,7 +189,7 @@ sections.
   the scheduling decision  to all the sensors.  When  the network size
   increases,  the network  is  divided into  many  subregions and  the
   decision is made by a leader in each subregion.
-\end{itemize}
+%\end{itemize}
 
 
 
