X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/blobdiff_plain/515875e0557d919386e4fa218f79945999bb1d2c..6faf6604efe05771971cadb7bb476b0bb8bc0b86:/bare_conf.tex

diff --git a/bare_conf.tex b/bare_conf.tex
index b9ed86e..3a76a98 100755
--- a/bare_conf.tex
+++ b/bare_conf.tex
@@ -55,12 +55,12 @@ Email: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon
 
 \begin{abstract}
 One of  the fundamental challenges in Wireless  Sensor Networks (WSNs)
-is  coverage  preservation  and  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 scheduling  of sensor node
+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
@@ -79,12 +79,12 @@ network lifetime and improve the coverage performance.
 
 \noindent Recent years have witnessed significant advances in wireless
 communications and embedded micro-sensing MEMS technologies which have
-made  emerge wireless  sensor networks  as one  of the  most promising
+led to the  emergence of wireless  sensor networks  as one  of the  most promising
 technologies~\cite{asc02}.   In fact, they  present huge  potential in
 several  domains ranging  from  health care  applications to  military
 applications.  A sensor network is  composed of a large number of tiny
 sensing  devices deployed  in a  region of  interest. Each  device has
-processing  and wireless communication  capabilities, which  enable to
+processing  and wireless communication  capabilities, which  enable it to
 sense its environment, to compute, to store information and to deliver
 report messages to a base station.
 %These sensor nodes run on batteries with limited capacities. To achieve a long life of the network, it is important to conserve battery power. Therefore, lifetime optimisation is one of the most critical issues in wireless sensor networks.
@@ -99,26 +99,24 @@ is desirable that  a WSN should be deployed  with high density because
 spatial redundancy can  then be exploited to increase  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. To
-extend the lifetime  of the network, the main idea  is to take benefit
-from  the overlapping  sensing regions  of some  sensor nodes  to save
+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.
 Obviously, the deactivation of nodes  is only relevant if the coverage
-of the monitored area  is not affected.  Consequently, future software
+of the monitored area  is not affected.  Consequently, future softwares
 may  need to  adapt  appropriately to  achieve  acceptable quality  of
-service  for  applications.  In  this  paper  we  concentrate on  area
+service  for  applications.  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. 
-% DEBUT AJOUT
-{\bf In fact, the nodes in a  subregion can be seen as a cluster where
+ 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 much node failures.}
-% FIN AJOUT
+  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  suitable manner  a sensor  node to
+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.
@@ -141,8 +139,8 @@ suggestions for future works in Section~\ref{sec:conclusion}.
 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 suggest
-to recall main definitions and assumptions related to our work.
+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
@@ -158,14 +156,14 @@ to recall main definitions and assumptions related to our work.
 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
+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 in the same time improve the lifetime of the wireless
+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
@@ -177,10 +175,10 @@ simultaneously).
 {\bf Lifetime}
 
 Various   definitions   exist   for   the   lifetime   of   a   sensor
-network~\cite{die09}.  Main definitions proposed in the literature are
-related to the  remaining energy of the nodes or  to the percentage of
-coverage. The lifetime of the  network is mainly defined as the amount
-of  time that  the network  can  satisfy its  coverage objective  (the
+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
@@ -193,11 +191,11 @@ transmit information on an event in the area that it monitors.
 
 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, such that the
+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  the distributed algorithms, each node  in the network
+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
@@ -208,40 +206,39 @@ the time intervals to be activated.
 Some      distributed     algorithms      have      been     developed
 in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02}  to perform the
 scheduling.   Distributed algorithms typically  operate in  rounds for
-predetermined  duration. At  the  beginning of  each  round, a  sensor
-exchange information with its neighbors and makes a decision to either
+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 based  on simple greedy criteria like  the largest uncovered
+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 node ID
-and node location  to its neighbors at the beginning  of each round. A
+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
-that nodes  make conflicting decisions simultaneously and  that a part
-of       the       area        is       no       longer       covered.
+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
-prioritize the  covers and  a sensing phase  during which  each sensor
+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
 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 requirements  is being  maintained.  This algorithm  works in
+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 others approaches do  not consider synchronized and predetermined
+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  time wake-up  is randomized
+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
 
@@ -264,7 +261,7 @@ 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.
 
-First algorithms  proposed in the  literature consider that  the cover
+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
@@ -281,7 +278,7 @@ 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  include an area, summed over  all areas, is maximized.
+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.
@@ -672,11 +669,9 @@ simulator  OMNeT++  \cite{varga}. We  performed  simulations for  five
 different densities varying from 50 to 250~nodes. Experimental results
 were  obtained from  randomly generated  networks in  which  nodes are
 deployed over a  $(50 \times 25)~m^2 $ sensing  field. 
-% DEBUT MODIFICATION
-{\bf More precisely, the deployment is controlled at a coarse scale in
+More precisely, the deployment is controlled at a coarse scale in
   order to ensure that the  deployed nodes can fully cover the sensing
-  field with the given sensing range.}
-% FIN MODIFICATION
+  field with the given sensing range.
 10~simulation  runs  are performed  with
 different  network  topologies for  each  node  density.  The  results
 presented hereafter  are the  average of these  10 runs.  A simulation
@@ -863,11 +858,8 @@ A  sensor  node has  limited  energy  resources  and computing  power,
 therefore it is important that the proposed algorithm has the shortest
 possible execution  time. The energy of  a sensor node  must be mainly
 used   for  the  sensing   phase,  not   for  the   pre-sensing  ones.
-Table~\ref{table1} gives the average  execution times {\bf in seconds}
-on a laptop of the decision phase
-% DEBUT AJOUT
-{\bf (resolution of the optimization problem)}
-% FIN AJOUT
+Table~\ref{table1} gives the average  execution times  in seconds
+on a laptop of the decision phase (solving of the optimization problem)
 during one  round.  They  are given for  the different  approaches and
 various numbers of sensors.  The lack of any optimization explains why
 the heuristic has very  low execution times.  Conversely, the Strategy
@@ -890,7 +882,7 @@ distributed method is clearly required.
 % centered columns (4 columns)
       \hline
 %inserts double horizontal lines
-Sensors Number & Strategy~1 & Strategy~2  & Simple Heuristic \\ [0.5ex]
+Sensors Number & Strategy~2 & Strategy~1  & Simple Heuristic \\ [0.5ex]
  & (with Two Leaders) & (with One Leader) & \\ [0.5ex]
 %Case & Strategy (with Two Leaders) & Strategy (with One Leader) & Simple Heuristic \\ [0.5ex]
 % inserts table
@@ -922,11 +914,9 @@ nodes  have  been drained  of  their  energy  or each  sensor  network
 monitoring  an area becomes  disconnected.  In  figure~\ref{fig8}, the
 network  lifetime for different  network sizes  and for  both Strategy
 with Two  Leaders and the Simple  Heuristic is illustrated. 
-% DEBUT MODIFICATION
-{\bf  We do  not consider  anymore the  centralized Strategy  with One
+  We do  not consider  anymore the  centralized Strategy  with One
   Leader, because, as shown above, this strategy results  in execution
-  times that quickly become unsuitable for a sensor network.}
-% FIN MODIFICATION
+  times that quickly become unsuitable for a sensor network.
 
 \begin{figure}[h!]
 %\centering
@@ -982,12 +972,9 @@ problems, one per subregion, that can be solved more easily.
 In  future, we plan  to study  and propose  a coverage  protocol which
 computes  all  active  sensor  schedules  in  a  single  round,  using
 optimization  methods  such  as  swarms optimization  or  evolutionary
-algorithms.
-% DEBUT AJOUT
-{\bf  This single  round  will still  consists  of 4  phases, but  the
+algorithms.  This single  round  will still  consists  of 4  phases, but  the
   decision phase will compute the schedules for several sensing phases
-  which aggregated together define a kind of meta-sensing phase.}
-% FIN AJOUT
+  which aggregated together define a kind of meta-sensing phase.
 The computation of all cover sets in one round is far more
 difficult, but will reduce the communication overhead.
 
