Michel : Related literature en cours

diff --git a/LiCO_Journal.tex b/LiCO_Journal.tex
index fa18779cbef42a930e6a1710b2d656493a96d423..149aab7fd17eba7562d4e1ca21a0434dfb6d4965 100755 (executable)
--- a/LiCO_Journal.tex
+++ b/LiCO_Journal.tex
@@ -64,13 +64,13 @@ region of interest is first subdivided  into subregions and our protocol is then
 distributed among sensor nodes in each  subregion. A sensor node which runs LiCO
 protocol  repeats   periodically  four  stages:  information   exchange,  leader
 election, optimization decision, and sensing.  More precisely, the scheduling of
 distributed among sensor nodes in each  subregion. A sensor node which runs LiCO
 protocol  repeats   periodically  four  stages:  information   exchange,  leader
 election, optimization decision, and sensing.  More precisely, the scheduling of

-nodes activities (sleep/wakeup  duty cycles) is achieved in each  subregion by a
+nodes activities (sleep/wake up  duty cycles) is achieved in each  subregion by a

 leader selected after  cooperation between nodes within the  same subregion. The
 novelty of  approach lies essentially in  the formulation of a  new mathematical
 optimization  model  based  on  perimeter coverage  level  to  schedule  sensors
 activities.  Extensive simulation experiments have been performed using OMNeT++,
 the  discrete event  simulator, to  demonstrate that  LiCO is  capable to  offer
 leader selected after  cooperation between nodes within the  same subregion. The
 novelty of  approach lies essentially in  the formulation of a  new mathematical
 optimization  model  based  on  perimeter coverage  level  to  schedule  sensors
 activities.  Extensive simulation experiments have been performed using OMNeT++,
 the  discrete event  simulator, to  demonstrate that  LiCO is  capable to  offer

-longer lifetime coverage for WSNs in comparison with some other protols.
+longer lifetime coverage for WSNs in comparison with some other protocols.

 \end{abstract} 
 
 % Note that keywords are not normally used for peerreview papers.
 \end{abstract} 
 
 % Note that keywords are not normally used for peerreview papers.


@@ -95,8 +95,8 @@ range of application  in areas such as business,  environment, health, industry,
 military, and  son~\cite{yick2008wireless}.  Typically,  a sensor  node contains
 three main components~\cite{anastasi2009energy}: a  sensing unit able to measure
 physical,  chemical, or  biological  phenomena observed  in  the environment;  a
 military, and  son~\cite{yick2008wireless}.  Typically,  a sensor  node contains
 three main components~\cite{anastasi2009energy}: a  sensing unit able to measure
 physical,  chemical, or  biological  phenomena observed  in  the environment;  a

-processing  unit  which  will  process  and store  the  measurements  which  are
-collected; a radio communication unit for data transmission and receiving.
+processing unit which will process and store the collected measurements; a radio
+communication unit for data transmission and receiving.

 
 The energy needed  by an active sensor node to  perform sensing, processing, and
 communication is supplied by a power supply which is a battery. This battery has
 
 The energy needed  by an active sensor node to  perform sensing, processing, and
 communication is supplied by a power supply which is a battery. This battery has


@@ -117,120 +117,124 @@ lifetime of the WSNs~\cite{rault2014energy}.
 
 %\uppercase{\textbf{Our contributions.}}
 
 
 %\uppercase{\textbf{Our contributions.}}
 

-% MICHEL - TO CONTINUED FROM HERE

 This paper makes the following contributions.
 \begin{enumerate}
 This paper makes the following contributions.
 \begin{enumerate}

-\item  We  devise a  framework  to  schedules  nodes to  be  activated
-  alternatively, such that  the network lifetime may  be prolonged ans
-  certain coverage  requirement can still  be met.  This  framework is
-  based on the  division of the area of interest  into several smaller
-  subregions;  on  the division  of  timeline  into periods  of  equal
-  length.  One leader is elected for each subregion in an independent,
-  distributed,  and  simultaneous way  by  the  cooperation among  the
-  sensor nodes within  each subregion, and this is  similar to cluster
-  architecture
-\item We  propose a  new mathematical  optimization model.  Instead of
-  trying to cover a set of  specified points/targets as in most of the
-  methods proposed in the literature,  we formulate an integer program
-  based  on perimeter  coverage  of each  sensor.  The model  involves
-  integer variables to capture the deviations between the actual level
-  of coverage  and the  required level.  And a  weighted sum  of these
-  deviations is minimized.
-\item We conducted extensive simulation experiments using the discrete
-  event  simulator  OMNeT++,  to  demonstrate the  efficiency  of  our
-  protocol, compared to  two approaches found in  the literature, DESK
-  \cite{ChinhVu} and  GAF \cite{xu2001geography}, and compared  to our
-  previous work using another optimization model for sensor scheduling
-  \cite{Idrees2}.
+\item We devise a framework to schedule nodes to be activated alternatively such
+  that the network lifetime is prolonged  while ensuring that a certain level of
+  coverage is preserved.  A  key idea in our framework is  to exploit spatial an
+  temporal subdivision.   On the one hand  the area of interest  if divided into
+  several smaller subregions and on the other hand the time line is divided into
+  periods of equal length. In each subregion the sensor nodes will cooperatively
+  choose a  leader which will  schedule nodes  activities, and this  grouping of
+  sensors is similar to typical cluster architecture.
+\item We  propose a new mathematical  optimization model.  Instead of  trying to
+  cover a set of specified points/targets as  in most of the methods proposed in
+  the literature, we formulate an integer program based on perimeter coverage of
+  each sensor.  The  model involves integer variables to  capture the deviations
+  between  the actual  level of  coverage and  the required  level.  So  that an
+  optimal scheduling  will be  obtained by  minimizing a  weighted sum  of these
+  deviations.
+\item We  conducted extensive simulation  experiments, using the  discrete event
+  simulator OMNeT++, to demonstrate the  efficiency of our protocol. We compared
+  our   LiCO   protocol   to   two   approaches   found   in   the   literature:
+  DESK~\cite{ChinhVu} and  GAF~\cite{xu2001geography}, and also to  our previous
+  work published in~\cite{Idrees2} which is  based on another optimization model
+  for sensor scheduling.

 \end{enumerate}
 
 \end{enumerate}
 

-

 %Two combined integrated energy-efficient techniques have been used by LiCO protocol in order to maximize the lifetime coverage in WSN: the first, by dividing the area of interest into several smaller subregions based on divide-and-conquer method and then one leader elected for each subregion in an independent, distributed, and simultaneous way by the cooperation among the sensor nodes within each subregion, and this similar to cluster architecture;
 % the second, activity scheduling based new optimization model has been used to provide the optimal cover set that will take the mission of sensing during current period. This optimization algorithm is based on a perimeter-coverage model so as to optimize the shared perimeter among the sensors in each subregion, and this represents as a energu-efficient control topology mechanism in WSN.
 
 %Two combined integrated energy-efficient techniques have been used by LiCO protocol in order to maximize the lifetime coverage in WSN: the first, by dividing the area of interest into several smaller subregions based on divide-and-conquer method and then one leader elected for each subregion in an independent, distributed, and simultaneous way by the cooperation among the sensor nodes within each subregion, and this similar to cluster architecture;
 % the second, activity scheduling based new optimization model has been used to provide the optimal cover set that will take the mission of sensing during current period. This optimization algorithm is based on a perimeter-coverage model so as to optimize the shared perimeter among the sensors in each subregion, and this represents as a energu-efficient control topology mechanism in WSN.
 

-
-The remainder of  the paper is organized as follows.  The next section
-reviews  the related  work  in the  field.  Section~\ref{sec:The  LiCO
-  Protocol   Description}   is   devoted    to   the   LiCO   protocol
-Description.  Section~\ref{cp} gives  the  coverage model  formulation
-which   is    used   to   schedule   the    activation   of   sensors.
-Section~\ref{sec:Simulation Results and Analysis} presents simulations
-results. Finally, we give concluding  remarks and some suggestions for
-future works in Section~\ref{sec:Conclusion and Future Works}.
+The rest  of the paper is  organized as follows.  In the next section  we review
+some related work in the  field. Section~\ref{sec:The LiCO Protocol Description}
+is devoted to the LiCO protocol  description and Section~\ref{cp} focuses on the
+coverage model  formulation which is used  to schedule the activation  of sensor
+nodes.  Section~\ref{sec:Simulation  Results and Analysis}  presents simulations
+results and discusses the comparison  with other approaches. Finally, concluding
+remarks   are  drawn   and  some   suggestions   given  for   future  works   in
+Section~\ref{sec:Conclusion and Future Works}.

 
 % that show that our protocol outperforms others protocols.
 
 % that show that our protocol outperforms others protocols.

-\section{\uppercase{Related Literature}}
+\section{Related Literature}

 \label{sec:Literature Review}
 
 \label{sec:Literature Review}
 

-

 \noindent  In  this section,  we  summarize  some  related works  regarding  the
 \noindent  In  this section,  we  summarize  some  related works  regarding  the

-coverage problem and distinguish our  LiCO protocol from the works presented in
+coverage problem and  distinguish our LiCO protocol from the  works presented in

 the literature.
 
 the literature.
 

-The most discussed coverage problems  in literature can be classified into three
-types \cite{li2013survey}: area coverage \cite{Misra} where every point inside
-an area is to be  monitored, target coverage \cite{yang2014novel} where the main
-objective is  to cover only a  finite number of discrete  points called targets,
-and barrier coverage \cite{HeShibo}\cite{kim2013maximum} to prevent intruders
-from entering into the region  of interest. In \cite{Deng2012} authors transform
-the area coverage problem to the target coverage problem taking into account the
-intersection points among disks of sensors nodes or between disk of sensor nodes
-and boundaries. In \cite{Huang:2003:CPW:941350.941367} authors prove that if the perimeters of sensors are sufficiently covered, the whole area is sufficiently covered and they provide an algorithm in $O(nd~log~d)$ time to compute the perimeter-coverage of each sensor ($d$ the maximum number of sensors that are neighboring to a sensor, $n$ the total number of sensors in the network). {\it In LiCO protocol, instead of determining the level of coverage of a set of discrete points, our optimization model is based on checking the perimeter-coverage of each sensor to activate a minimal number of sensors.}
-
-The major  approach to extend network  lifetime while preserving  coverage is to
-divide/organize the  sensors into a suitable  number of set  covers (disjoint or
-non-disjoint), where  each set  completely covers a  region of interest,  and to
-activate these set  covers successively. The network activity  can be planned in
-advance and scheduled  for the entire network lifetime  or organized in periods,
+The most  discussed coverage problems in  literature can be classified  in three
+categories~\cite{li2013survey}   according   to  their   respective   monitoring
+objective.  Hence,  area coverage \cite{Misra}  means that every point  inside a
+fixed area  must be monitored, while  target coverage~\cite{yang2014novel} refer
+to  the objective  of coverage  for a  finite number  of discrete  points called
+targets,  and  barrier coverage~\cite{HeShibo}\cite{kim2013maximum}  focuses  on
+preventing  intruders   from  entering   into  the   region  of   interest.   In
+\cite{Deng2012}  authors  transform the  area  coverage  problem to  the  target
+coverage one taking into account the  intersection points among disks of sensors
+nodes    or   between    disk   of    sensor   nodes    and   boundaries.     In
+\cite{Huang:2003:CPW:941350.941367}  authors prove  that  if  the perimeters  of
+sensors are sufficiently  covered it will be  the case for the  whole area. They
+provide an algorithm in $O(nd~log~d)$  time to compute the perimeter-coverage of
+each  sensor,  where  $d$  denotes  the  maximum  number  of  sensors  that  are
+neighboring  to  a  sensor and  $n$  is  the  total  number of  sensors  in  the
+network. {\it In LiCO protocol, instead  of determining the level of coverage of
+  a set  of discrete  points, our  optimization model is  based on  checking the
+  perimeter-coverage of each sensor to activate a minimal number of sensors.}
+
+The major  approach to extend network  lifetime while preserving coverage  is to
+divide/organize the  sensors into a suitable  number of set covers  (disjoint or
+non-disjoint), where  each set completely  covers a  region of interest,  and to
+activate these set  covers successively. The network activity can  be planned in
+advance and scheduled  for the entire network lifetime or  organized in periods,

 and the set  of active sensor nodes  is decided at the beginning  of each period
 \cite{ling2009energy}.  Active node selection is determined based on the problem
 and the set  of active sensor nodes  is decided at the beginning  of each period
 \cite{ling2009energy}.  Active node selection is determined based on the problem

-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
+requirements (e.g.   area monitoring,  connectivity, or power  efficiency).  For
+instance, Jaggi {\em et al.}~\cite{jaggi2006}  address the problem of maximizing
+the lifetime  by dividing sensors  into the  maximum number of  disjoint subsets
+such  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
 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
-periodic fashion where a cover set  is computed at the beginning of each period.
-{\it  Motivated by  these works,  LiCO protocol  works in  periods,  where each
-  period contains  a preliminary phase  for information exchange  and decisions,
-  followed by a  sensing phase where one  cover set is in charge  of the sensing
-  task.}
-
+\cite{chin2007},  Padmatvathy  {\em   et  al.}~\cite{pc10},  propose  algorithms
+working in a periodic fashion where a  cover set is computed at the beginning of
+each period.   {\it Motivated by  these works,  LiCO protocol works  in periods,
+  where each  period contains a  preliminary phase for information  exchange and
+  decisions, followed by a sensing phase where one cover set is in charge of the
+  sensing task.}
+
+% MICHEL TO BE CONTINUED FROM HERE

 Various approaches, including centralized,  or distributed algorithms, have been
 Various approaches, including centralized,  or distributed algorithms, have been

-proposed     to    extend    the     network    lifetime.      In    distributed
+proposed    to     extend    the     network    lifetime.      In    distributed

 algorithms~\cite{yangnovel,ChinhVu,qu2013distributed},       information      is
 disseminated  throughout  the  network   and  sensors  decide  cooperatively  by
 communicating with their neighbors which of them will remain in sleep mode for a
 certain         period         of         time.          The         centralized
 algorithms~\cite{yangnovel,ChinhVu,qu2013distributed},       information      is
 disseminated  throughout  the  network   and  sensors  decide  cooperatively  by
 communicating with their neighbors which of them will remain in sleep mode for a
 certain         period         of         time.          The         centralized

-algorithms~\cite{cardei2005improving,zorbas2010solving,pujari2011high}     always
+algorithms~\cite{cardei2005improving,zorbas2010solving,pujari2011high}    always

 provide nearly or close to optimal  solution since the algorithm has global view
 provide nearly or close to optimal  solution since the algorithm has global view

-of the whole  network. But such a method has the  disadvantage of requiring high
-communication costs,  since the  node (located at  the base station)  making the
-decision needs information from all the  sensor nodes in the area and the amount
+of the whole network.  But such a method has the  disadvantage of requiring high
+communication costs,  since the node  (located at  the base station)  making the
+decision needs information from all the sensor  nodes in the area and the amount

 of  information can  be huge.   {\it  In order  to be  suitable for  large-scale
 of  information can  be huge.   {\it  In order  to be  suitable for  large-scale

-  network,  in the LiCO  protocol, the  area of interest is divided  into several
+  network, in  the LiCO protocol, the  area of interest is  divided into several

   smaller subregions, and in each one, a node called the leader is in charge for
   selecting the active sensors for the current period.}
 
   smaller subregions, and in each one, a node called the leader is in charge for
   selecting the active sensors for the current period.}
 

-A large  variety of coverage scheduling  algorithms has been  developed. Many of
-the existing  algorithms, dealing with the  maximization of the  number of cover
-sets, are heuristics.  These heuristics  involve the construction of a cover set
+A large  variety of coverage scheduling  algorithms has been developed.  Many of
+the existing  algorithms, dealing with the  maximization of the number  of cover
+sets, are heuristics.  These heuristics involve  the construction of a cover set

 by including in priority the sensor  nodes which cover critical targets, that is
 by including in priority the sensor  nodes which cover critical targets, that is

-to  say   targets  that   are  covered  by   the  smallest  number   of  sensors
+to  say   targets  that  are   covered  by   the  smallest  number   of  sensors

 \cite{berman04,zorbas2010solving}.  Other  approaches are based  on mathematical
 programming formulations~\cite{cardei2005energy,5714480,pujari2011high,Yang2014}
 and dedicated  techniques (solving with a  branch-and-bound algorithms available
 in optimization solver).   The problem is formulated as  an optimization problem
 (maximization of the lifetime or number of cover sets) under target coverage and
 \cite{berman04,zorbas2010solving}.  Other  approaches are based  on mathematical
 programming formulations~\cite{cardei2005energy,5714480,pujari2011high,Yang2014}
 and dedicated  techniques (solving with a  branch-and-bound algorithms available
 in optimization solver).   The problem is formulated as  an optimization problem
 (maximization of the lifetime or number of cover sets) under target coverage and

-energy  constraints.   Column   generation  techniques,  well-known  and  widely
+energy  constraints.   Column  generation   techniques,  well-known  and  widely

 practiced techniques for  solving linear programs with too  many variables, have
 also                                                                        been
 practiced techniques for  solving linear programs with too  many variables, have
 also                                                                        been

-used~\cite{castano2013column,rossi2012exact,deschinkel2012column}. {\it In LiCO
-  protocol, each  leader, in  each subregion, solves  an integer program  with 
-the double objective  consisting in minimizing  the overcoverage and the
-  undercoverage of the perimeter of each sensor.  
-
+used~\cite{castano2013column,rossi2012exact,deschinkel2012column}. {\it  In LiCO
+  protocol, each leader,  in each subregion, solves an integer  program with the
+  double   objective  consisting   in  minimizing   the  overcoverage   and  the
+  undercoverage of the perimeter of each sensor.

 }
 
 
 }