X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Sensornets15.git/blobdiff_plain/30623c88a2804ea8abf07dd5b15eeacdcd39a33d..8abf40a6144c2ae97b77ec718fef360e652d01b9:/Example.tex?ds=sidebyside

diff --git a/Example.tex b/Example.tex
index cef7ad1..38cc14f 100644
--- a/Example.tex
+++ b/Example.tex
@@ -91,7 +91,7 @@ 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 \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, movement, light, soil makeup, noise levels, presence or absence of certain kinds of objects, and mechanical stress levels on attached objects. Consequently, a wide range of WSN applications such as~\cite{ref22}: health-care, environment, agriculture, public safety, military, transportation systems, and industry applications.}
+the sensing phase to prolong the network lifetime. \textcolor{blue}{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, movement, light, soil makeup, noise levels, presence or absence of certain 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.}
 
 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
@@ -116,8 +116,7 @@ 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 approaches (a distributed one,  DESK ~\cite{ChinhVu}, and a centralized
-one called GAF  ~\cite{xu2001geography}) in order to  compare their performances
+existing \textcolor{blue}{and distributed approaches}(DESK ~\cite{ChinhVu}, and GAF  ~\cite{xu2001geography}) in order to  compare their performances
 with our approach.  We also focus on performance analysis based on the number of
 subregions. 
 % MODIF - END
@@ -244,7 +243,8 @@ 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.
+executed   simultaneously  in   each   subregion. \textcolor{blue}{Sensor nodes  are assumed to
+be deployed  almost uniformly over the  region and the subdivision of the area of interest is regular.}
 
 \begin{figure}[ht!]
 \centering
@@ -257,7 +257,9 @@ As  shown  in Figure~\ref{fig2},  the  proposed  DiLCO  protocol is  a  periodic
 protocol where  each period is  decomposed into 4~phases:  Information Exchange,
 Leader Election,  Decision, and Sensing. For  each period there  will be exactly
 one  cover  set  in charge  of  the  sensing  task.   A periodic  scheduling  is
-interesting  because it  enhances the  robustness  of the  network against  node failures. \textcolor{blue}{Many WSN applications have communication requirements that are periodic and known previously such as collecting temperature statistics at regular intervals. This periodic nature can be used to provide a regular schedule to sensor nodes and thus avoid a sensor failure. If the period time increases, the reliability and energy consumption are decreased and vice versa}. First,  a node  that has not  enough energy  to complete a  period, or
+interesting  because it  enhances the  robustness  of the  network against  node failures.
+% \textcolor{blue}{Many WSN applications have communication requirements that are periodic and known previously such as collecting temperature statistics at regular intervals. This periodic nature can be used to provide a regular schedule to sensor nodes and thus avoid a sensor failure. If the period time increases, the reliability and energy consumption are decreased and vice versa}. 
+First,  a node  that has not  enough energy  to complete a  period, or
 which fails before  the decision is taken, will be  excluded from the scheduling
 process. Second,  if a node  fails later, whereas  it was supposed to  sense the
 region of  interest, it will only affect  the quality of the  coverage until the
@@ -291,10 +293,10 @@ 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 to stay active during the next sensing phase. If yes, it exchanges
+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 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. Once  the  first phase  is
+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 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 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.
