X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Sensornets15.git/blobdiff_plain/5442cc8a9128527983d0c1f0f7259531eca9330b..03f7b8cc61ab12c78997141c2461438382a08925:/Example.tex?ds=inline

diff --git a/Example.tex b/Example.tex
index b2b473f..042bfca 100644
--- a/Example.tex
+++ b/Example.tex
@@ -56,19 +56,8 @@ email: ali.idness@edu.univ-fcomte.fr,\\ $\lbrace$karine.deschinkel, michel.salom
   scheduling  performed by  each elected  leader.  This  two-step process  takes
   place periodically, in  order to choose a small set  of nodes remaining active
   for sensing during a time slot.  Each set is built to ensure coverage at a low
-  energy cost,  allowing to optimize  the network lifetime.  
-%More  precisely, a
-  %period  consists  of  four   phases:  (i)~Information  Exchange,  (ii)~Leader
-  %Election,  (iii)~Decision, and  (iv)~Sensing.   The  decision process,  which
-%  results in  an activity  scheduling vector,  is carried out  by a  leader node
-%  through the solving of an integer program.
-% MODIF - BEGIN
-  Simulations are conducted using the discret event simulator
-  OMNET++.  We  refer to the characterictics  of a Medusa II  sensor for
-  the energy consumption  and the computation time.   In comparison with
-  two other existing  methods, our approach is able to  increase the WSN
-  lifetime and provides improved coverage performance. }
-% MODIF - END
+  energy cost,  allowing to optimize  the network lifetime. Simulations are conducted using the discrete event simulator OMNET++. We refer to the characterictics  of a Medusa II  sensor for the energy consumption  and the computation time.   In comparison with two other existing  methods, our approach is able to  increase the WSN lifetime and provides improved coverage performances. }
+
 
 %\onecolumn
 
@@ -86,15 +75,15 @@ challenges in  WSNs, which has  been addressed by  a large amount  of literature
 during the  last few  years, is  the design of  energy efficient  approaches for
 coverage and connectivity~\cite{conti2014mobile}.  Coverage  reflects how well a
 sensor  field is  monitored. On  the one  hand we  want to  monitor the  area of
-interest in the most  efficient way~\cite{Nayak04}, \textcolor{blue}{which means
-  that we want to maintain the best coverage as long as possible}.  On the other
+interest in the most  efficient way~\cite{Nayak04}, which means
+  that we want to maintain the best coverage as long as possible.  On the other
 hand  we  want  to  use  as   little  energy  as  possible.   Sensor  nodes  are
 battery-powered  with  no means  of  recharging  or  replacing, usually  due  to
 environmental (hostile or unpractical environments) or cost reasons.  Therefore,
 it is desired  that the WSNs are  deployed with high densities so  as to exploit
 the overlapping sensing  regions of some sensor nodes to  save energy by turning
 off  some   of  them   during  the   sensing  phase   to  prolong   the  network
-lifetime.  \textcolor{blue}{A WSN  can  use  various types  of  sensors such  as
+lifetime.  A WSN  can  use  various types  of  sensors such  as
   \cite{ref17,ref19}:  thermal, seismic,  magnetic, visual,  infrared, acoustic,
   and  radar.  These  sensors  are   capable  of  observing  different  physical
   conditions  such  as:  temperature,   humidity,  pressure,  speed,  direction,
@@ -102,7 +91,7 @@ lifetime.  \textcolor{blue}{A WSN  can  use  various types  of  sensors such  as
   kinds  of  objects,   and  mechanical  stress  levels   on  attached  objects.
   Consequently, there is a wide  range of WSN applications such as~\cite{ref22}:
   health-care, environment, agriculture, public safety, military, transportation
-  systems, and industry applications.}
+  systems, and industry applications.
 
 In this  paper we design  a protocol that focuses  on the area  coverage problem
 with  the objective  of maximizing  the network  lifetime. Our  proposition, the
@@ -127,13 +116,13 @@ framework of the  DiLCO approach and the coverage problem  formulation.  In this
 paper  we   made  more  realistic   simulations  by  taking  into   account  the
 characteristics of  a Medusa II sensor  ~\cite{raghunathan2002energy} to measure
 the energy consumption and the computation  time.  We have implemented two other
-existing \textcolor{blue}{and distributed approaches} (DESK ~\cite{ChinhVu}, and
+existing and distributed approaches (DESK ~\cite{ChinhVu}, and
 GAF ~\cite{xu2001geography})  in order  to compare  their performances  with our
-approach. \textcolor{blue}{We focus  on DESK and GAF protocols  for two reasons.
+approach. We focused  on DESK and GAF protocols  for two reasons.
   First our protocol is inspired by both  of them: DiLCO uses a regular division
   of the  area of interest  as in GAF  and a temporal  division in rounds  as in
   DESK.  Second, DESK  and GAF are well-known protocols, easy  to implement, and
-  often  used  as references  for  comparison}.  We  also focus  on  performance
+  often  used  as references  for  comparison.  We  also focus  on  performance
 analysis based on the number of subregions.
 % MODIF - END
 
@@ -174,7 +163,7 @@ and the set  of active sensor nodes  is decided at the beginning  of each period
 requirements  (e.g.  area   monitoring,  connectivity,  power  efficiency).  For
 instance,  Jaggi  et al.  \cite{jaggi2006}  address  the  problem of  maximizing
 network lifetime by dividing sensors into the maximum number of disjoint subsets
-such  that each  subset  can ensure  both  coverage and  connectivity. A  greedy
+so that each  subset  can ensure  both  coverage and  connectivity. A  greedy
 algorithm  is applied  once to  solve  this problem  and the  computed sets  are
 activated  in   succession  to  achieve   the  desired  network   lifetime.   Vu
 \cite{chin2007}, Padmatvathy et al. \cite{pc10}, propose algorithms working in a
@@ -201,13 +190,13 @@ of  information can  be huge.   {\it  In order  to be  suitable for  large-scale
   selecting the active sensors for the current period.}
 
 % MODIF - BEGIN
-\textcolor{blue}{ Our approach to select the leader node in a subregion is quite
+ Our approach to select the leader node in a subregion is quite
   different   from    cluster   head    selection   methods   used    in   LEACH
   \cite{DBLP:conf/hicss/HeinzelmanCB00}         or          its         variants
   \cite{ijcses11}. Contrary  to LEACH, the division  of the area of  interest is
   supposed to be performed before the  leader election. Moreover, we assume that
   the sensors are deployed almost uniformly  and with high density over the area
-  of interest, such  that the division is  fixed and regular.  As  in LEACH, our
+  of interest, so  that the division is  fixed and regular.  As  in LEACH, our
   protocol works in round fashion.  In each round, during the pre-sensing phase,
   nodes make autonomous  decisions. In LEACH, each sensor elects  itself to be a
   cluster head,  and each non-cluster  head will  determine its cluster  for the
@@ -216,7 +205,7 @@ of  information can  be huge.   {\it  In order  to be  suitable for  large-scale
   account  to promote  the nodes  that have  the most  energy to  become leader.
   Contrary to  the LEACH protocol  where all sensors  will be active  during the
   sensing-phase, our protocol  allows to deactivate a subset  of sensors through
-  an optimization process which reduces significantly the energy consumption.}
+  an optimization process which significantly reduces  the energy consumption.
 % MODIF - END
 
 A large  variety of coverage scheduling  algorithms has been  developed. Many of
@@ -277,9 +266,9 @@ less accurate according to the number of primary points.
 \label{main_idea}
 \noindent We start  by applying a divide-and-conquer algorithm  to partition the
 area of interest  into smaller areas called subregions and  then our protocol is
-executed  simultaneously in  each subregion.  \textcolor{blue}{Sensor nodes  are
+executed  simultaneously in  each subregion. Sensor nodes  are
   assumed to be deployed almost uniformly over the region and the subdivision of
-  the area of interest is regular.}
+  the area of interest is regular.
 
 \begin{figure}[ht!]
 \centering
@@ -328,20 +317,20 @@ and each sensor node will have five possible status in the network:
 An outline of the protocol  implementation is given by Algorithm~\ref{alg:DiLCO}
 which describes  the execution of  a period  by a node  (denoted by $s_j$  for a
 sensor node  indexed by  $j$). At  the beginning  a node  checks whether  it has
-enough  energy  \textcolor{blue}{(its energy  should  be  greater than  a  fixed
-  treshold $E_{th}$)} to  stay active during the next sensing  phase. If yes, it
+enough  energy (its energy  should  be  greater than  a  fixed
+  treshold $E_{th}$) to  stay active during the next sensing  phase. If yes, it
 exchanges information with all the other  nodes belonging to the same subregion:
 it collects from each node  its position coordinates, remaining energy ($RE_j$),
-ID,  and the  number of  one-hop neighbors  still alive.   \textcolor{blue}{INFO
-  packet contains two parts: header and  data payload. The sensor ID is included
+ID,  and the  number of  one-hop neighbors  still alive.  INFO
+  packet contains two parts: header and payload data. The sensor ID is included
   in  the header,  where the  header  size is  8  bits. The  data part  includes
   position coordinates (64 bits), remaining energy  (32 bits), and the number of
   one-hop live neighbors (8 bits). Therefore the  size of the INFO packet is 112
-  bits.} Once the  first phase is completed,  the nodes of a  subregion choose a
+  bits. Once the  first phase is completed,  the nodes of a  subregion choose a
 leader to  take the  decision based  on the  following criteria  with decreasing
 importance: larger  number of  neighbors, larger remaining  energy, and  then in
 case of equality,  larger index.  After that,  if the sensor node  is leader, it
-will  solve an  integer program  (see Section~\ref{cp}).   \textcolor{blue}{This
+will  solve an  integer program  (see Section~\ref{cp}).  This
   integer program  contains boolean variables  $X_j$ where ($X_j=1$)  means that
   sensor $j$ will be active in the  next sensing phase. Only sensors with enough
   remaining energy are  involved in the integer  program ($J$ is the  set of all
@@ -353,7 +342,7 @@ will  solve an  integer program  (see Section~\ref{cp}).   \textcolor{blue}{This
   send an ActiveSleep packet to each sensor in the same subregion to indicate it
   if it has to be active or not.  Otherwise, if the sensor is not the leader, it
   will wait for the ActiveSleep packet to  know its state for the coming sensing
-  phase.}
+  phase.
 %which provides a set of  sensors planned to be
 %active in the next sensing phase.
 
@@ -447,11 +436,11 @@ X_{j} \in \{0,1\}, &\forall j \in J
 \end{array}
 \right.
 \end{equation}
-The objective function is a weighted sum of overcoverage and undercoverage. The goal is to limit the overcoverage in order to activate a minimal number of sensors while simultaneously preventing undercoverage.  \textcolor{blue}{ By
+The objective function is a weighted sum of overcoverage and undercoverage. The goal is to limit the overcoverage in order to activate a minimal number of sensors while simultaneously preventing undercoverage.  By
     choosing  $w_{U}$ much  larger than $w_{\theta}$,  the coverage  of a
     maximum of  primary points  is ensured.  Then for the  same number  of covered
     primary points,  the solution  with a  minimal number  of active  sensors is
-    preferred. }
+    preferred. 
 %Both  weights $w_\theta$  and $w_U$ must  be carefully  chosen in
 %order to  guarantee that the  maximum number of  points are covered  during each
 %period.
