<html><head><meta name="color-scheme" content="light dark"></head><body><pre style="word-wrap: break-word; white-space: pre-wrap;">X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/blobdiff_plain/6b1de891e2849975795b04843f3bfe3d5e44b667..97c825d7b15b2a2dc1350641ca38613d63d30efa:/bare_conf.tex

diff --git a/bare_conf.tex b/bare_conf.tex
index 0804bff..7518ab7 100755
--- a/bare_conf.tex
+++ b/bare_conf.tex
@@ -1,6 +1,3 @@
-
-
- 
 \documentclass[conference]{IEEEtran}
 
 \ifCLASSINFOpdf
@@ -87,13 +84,13 @@ 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 it to
-sense its environment, to compute, to store information and to deliver
+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.
 One of the main design issues in Wireless Sensor Networks (WSNs) is to
 prolong the  network lifetime,  while achieving acceptable  quality of
 service for applications.  Indeed, sensor nodes have limited resources
-in terms of memory, energy and computational power.
+in terms of memory, energy, and computational power.
 
 Since sensor nodes have limited battery life and without being able to
 replace batteries,  especially in remote and  hostile environments, it
@@ -130,7 +127,7 @@ 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  on
-OMNET++  \cite{varga}. They  fully demonstrate  the usefulness  of the
+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}.
 
@@ -191,7 +188,7 @@ transmit information on an event in the area that it monitors.
 
 {\bf Activity scheduling}
 
-Activitiy scheduling is to  schedule the activation and deactivation of
+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
@@ -355,7 +352,7 @@ sections.
   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
@@ -365,10 +362,10 @@ sections.
   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. Our
+  energy in a centralized approach  compared to a distributed one. Our
   work does not  consider only one leader to  compute and to broadcast
-  the  scheduling decision  to all  the sensors.  When the  network size
-  increases,  the  network  is  divided  into many  subregions  and  the
+  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}
 
@@ -630,7 +627,7 @@ X_{j} \in \{0,1\}, &amp;\forall j \in J
 The first group  of constraints indicates that some  primary point $p$
 should be covered by at least one  sensor and, if it is not always the
 case,  overcoverage  and  undercoverage  variables  help  balancing  the
-restriction  equation by taking  positive values.  There are  two main         %%RAPH restriction equations????
+restriction  equations by taking  positive values.  There are  two main         
 objectives.  First we limit the overcoverage of primary points in order to
 activate a minimum number of sensors.  Second we prevent the absence of monitoring on
  some parts of the subregion by  minimizing the undercoverage.   The
@@ -692,7 +689,7 @@ active  node will  consume  12~joules during the sensing  phase, while  a
 sleeping  node will  use  0.002  joules.  Each  sensor  node will  not
 participate in the next round if its remaining energy is less than 12
 joules.  In  all  experiments  the  parameters  are  set  as  follows:
-$R_s=5m$, $w_{\Theta}=1$, and $w_{U}=|P^2|$.
+$R_s=5~m$, $w_{\Theta}=1$, and $w_{U}=|P^2|$.
 
 We  evaluate the  efficiency of  our approach by using  some performance
 metrics such as: coverage ratio,  number of active nodes ratio, energy
@@ -835,7 +832,7 @@ which  is obtained  for 10~simulation  runs,  is then  divided by  the
 average number of rounds to define a metric allowing a fair comparison
 between networks having different densities.
 
-Figure~\ref{fig7} illustrates the Energy Consumption for the different
+Figure~\ref{fig7} illustrates the energy consumption for the different
 network  sizes and  the three  approaches. The  results show  that the
 strategy  with  two  leaders  is  the  most  competitive  from  the energy
 consumption point  of view.  A  centralized method, like  the strategy
@@ -860,7 +857,7 @@ communications have a small impact on the network lifetime.
 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.    %%RAPH: plusieurs phase de pre-sensing??
+used   for  the  sensing   phase,  not   for  the   pre-sensing  ones.   
 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
@@ -944,7 +941,7 @@ subdividing the sensing field  into many subregions, which are managed
 independently and simultaneously, is the most relevant way to maximize
 the lifetime of a network.
 
-\section{Conclusion and future forks}
+\section{Conclusion and future works}
 \label{sec:conclusion}
 
 In this paper, we have  addressed the problem of the coverage and the lifetime
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