X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/LiCO.git/blobdiff_plain/2ef46922d0ae13a726ea9173494e7b0964953183..d2af1567d110987ebd8f80152c8a37de9dbd5d26:/LiCO_Journal.tex?ds=sidebyside

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--- a/LiCO_Journal.tex
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@@ -80,7 +80,7 @@ Wireless Sensor Networks, Area Coverage, Network lifetime, Optimization, Schedul
 
 \IEEEpeerreviewmaketitle
 
-\section{\uppercase{Introduction}}
+\section{Introduction}
 \label{sec:introduction}
 
 \noindent The continuous progress in Micro Electro-Mechanical Systems (MEMS) and
@@ -201,42 +201,46 @@ each period.   {\it Motivated by  these works,  LiCO protocol works  in periods,
   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
-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
+Various centralized  and distributed approaches, or  even a mixing of  these two
+concepts, have  been proposed  to extend the  network lifetime.   In distributed
+algorithms~\cite{yangnovel,ChinhVu,qu2013distributed}  each  sensors decides  of
+its own  activity scheduling after  an information exchange with  its neighbors.
+The main interest of  a such approach is to avoid  long range communications and
+thus to reduce the energy  dedicated to the comunications.  Unfortunately, since
+each node has  only information on its immediate neighbors  (usually the one-hop
+ones)  it may  take a  bad  decision leading  to a  global suboptimal  solution.
+Converseley,                                                         centralized
 algorithms~\cite{cardei2005improving,zorbas2010solving,pujari2011high}    always
-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  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
-  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
-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
-\cite{berman04,zorbas2010solving}.  Other  approaches are based  on mathematical
+provide nearly  or close to  optimal solution since  the algorithm has  a global
+view of the whole network. The disadvantage of a centralized method is obviously
+its high cost  in communications needed to  transmit to a single  node, the base
+station which will  globally schedule nodes activities, data from  all the other
+sensor nodes  in the area.   The price in comunications  can be very  huge since
+long range communications will be needed. In faxt the larger the WNS, the higher
+the  communication and  thus energy  cost.   {\it In  order to  be suitable  for
+  large-scale networks,  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. Thus our
+  protocol  is  scalable  and  a  globally distributed  method,  whereas  it  is
+  centralized in each subregion.} 
+
+Various  coverage scheduling  algorithms have  been developed  this last  years.
+Many of  them, 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 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
+and dedicated techniques (solving with a branch-and-bound algorithm 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
 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.
-}
-
+  protocol, each  leader, in charge  of a  subregion, solves an  integer program
+  which has a twofold objective: minimize the overcoverage and the undercoverage
+  of the perimeter of each sensor.}
 
 %\noindent Recently, the coverage problem has been received a high attention, which concentrates on how the physical space could be well monitored  after the deployment. Coverage is one of the Quality of Service (QoS) parameters in WSNs, which is highly concerned with power depletion~\cite{zhu2012survey}. Most of the works about the coverage protocols have been suggested in the literature focused on three types of the coverage in WSNs~\cite{mulligan2010coverage}: the first, area coverage means that each point in the area of interest within the sensing range of at least one sensor node; the second, target coverage in which a fixed set of targets need to be monitored; the third, barrier coverage refers to detect the intruders crossing a boundary of WSN. The work in this paper emphasized on the area coverage, so,  some area coverage protocols have been reviewed in this section, and the shortcomings of reviewed approaches are being summarized.
 
@@ -276,10 +280,17 @@ used~\cite{castano2013column,rossi2012exact,deschinkel2012column}. {\it  In LiCO
 
 %\uppercase{\textbf{Our Protocol}}. In this paper, a Lifetime Coverage Optimization Protocol, called (LiCO) in WSNs is suggested. The sensing field is divided into smaller subregions by means of divide-and-conquer method, and a LiCO protocol is distributed in each sensor in the subregion. The network lifetime in each subregion is divided into periods, each period includes 4 stages: Information Exchange, Leader election, decision based activity scheduling optimization, and sensing. The leaders are elected in an independent, asynchronous, and distributed way in all the subregions of the WSN. After that, energy-efficient activity scheduling mechanism based new optimization model is performed by each leader in the subregions. This optimization model is based on the perimeter coverage model in order to producing the optimal cover set of active sensors, which are taken the responsibility of sensing during the current period. LiCO protocol merges between two energy efficient mechanisms, which are used the main advantages of the centralized and distributed approaches and avoids the most of their disadvantages.
 
-
 \section{ The LiCO Protocol Description}
 \label{sec:The LiCO Protocol Description}
-\noindent In this section, we describe our Lifetime Coverage Optimization Protocol which is called LiCO in more detail.
+
+\noindent  In  this  section,  we  describe in  details  our  Lifetime  Coverage
+Optimization protocol.  First we present the  assumptions we made and the models
+we considered (in particular the perimter  coverage one), second we describe the
+background idea of our protocol, and third  we give the outline of the algorithm
+executed by each node.
+
+% MICHEL TO BE CONTINUED FROM HERE
+
 % It is based on two efficient-energy mechanisms: the first, is partitioning the sensing field into smaller subregions, and one leader is elected for each subregion;  the second, a sensor activity scheduling based new optimization model so as to produce the optimal cover set of active sensors for the sensing stage during the period.  Obviously, these two mechanisms can be contribute in extend the network lifetime coverage efficiently. 
 %Before proceeding in the presentation of the main ideas of the protocol, we will briefly describe the perimeter coverage model and give some necessary assumptions and definitions.
 
