Modifictions in Abstract + beginning of Introduction

[Sensornets15.git] / Example.tex

diff --git a/Example.tex b/Example.tex
index 4907bf5cf8633c1fadb9c9682a0f01537821121c..abb42a49883b295630f4fb006bc3ebb526bf9d43 100644 (file)
--- a/Example.tex
+++ b/Example.tex
@@ -36,43 +36,78 @@
 \keywords{Wireless   Sensor   Networks,   Area   Coverage,   Network   lifetime,
 Optimization, Scheduling.}
 
 \keywords{Wireless   Sensor   Networks,   Area   Coverage,   Network   lifetime,
 Optimization, Scheduling.}
 

-\abstract{One  of the fundamental  challenges in  Wireless Sensor  Networks (WSNs)  is the
-coverage preservation and the extension of the network lifetime continuously and
-effectively when  monitoring a  certain area (or  region) of interest.   In this
-paper, a Distributed Lifetime Coverage Optimization protocol (DiLCO) to maintain
-the  coverage  and  to improve  the  lifetime  in  wireless sensor  networks  is
-proposed.   The  area of  interest  is first  divided  into  subregions using  a
-divide-and-conquer  method and  then the  DiLCO protocol  is distributed  on the
-sensor nodes  in each  subregion. The DiLCO  combines two  efficient techniques:
-leader election  for each subregion, followed by  an optimization-based planning
-of activity  scheduling decisions for  each subregion. The proposed  DiLCO works
-into periods during which a small  number of nodes, remaining active for sensing,
-is selected to ensure coverage so as to maximize the lifetime of wireless sensor
-network.   Each  period  consists   of  four  phases:  (i)~Information  Exchange,
-(ii)~Leader Election, (iii)~Decision, and (iv)~Sensing.  The decision process is
-carried out  by a leader node,  which solves an integer  program.  Compared with
-some existing protocols, simulation results  show that the proposed protocol can
-prolong the network lifetime and improve the coverage performance effectively.}
+\abstract{ One of the main research challenges faced in Wireless Sensor Networks
+  (WSNs) is to preserve continuously and effectively the coverage of an area (or
+  region) of interest  to be monitored, while simultaneously  preventing as much
+  as possible a network failure due to battery-depleted nodes.  In this paper we
+  propose a protocol, called Distributed Lifetime Coverage Optimization protocol
+  (DiLCO), which maintains the coverage  and improves the lifetime of a wireless
+  sensor  network. As  a  first step  we  partition the  area  of interest  into
+  subregions using a classical  divide-and-conquer method. Our DiLCO protocol is
+  then distributed  on the sensor nodes in  each subregion in a  second step. To
+  fulfill  our   objective,  the   proposed  protocol  combines   two  effective
+  techniques:   a  leader   election   in  each   subregion,   followed  by   an
+  optimization-based node activity 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 result in  an activity scheduling vector,  is carried
+  out by a leader node through  the solving of an integer program. In comparison
+  with  some other  protocols, the  simulations  done using  the discrete  event
+  simulator OMNeT++ show that our approach  is able to increase the WSN lifetime
+  and provides improved coverage performance. }

 
 \onecolumn \maketitle \normalsize \vfill
 
 \section{\uppercase{Introduction}}
 \label{sec:introduction}
 \noindent 
 
 \onecolumn \maketitle \normalsize \vfill
 
 \section{\uppercase{Introduction}}
 \label{sec:introduction}
 \noindent 

-Energy efficiency is very important issue in WSNs since sensors are powered by  batteries. Therefore, reducing energy consumption and extending network lifetime are the main challenges in the design of WSNs. One of  the major scientific research 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. The most discussed coverage problems in literature can be classified
-into three types \cite{li2013survey}: area coverage (where every
-point inside an area is to be monitored), target coverage (where the main objective is to cover only a finite number of discrete
-points  called targets), and barrier coverage (the problem of preventing an intruder from entering a region of interest is referred to as the barrier coverage).
- It is required to monitor the area of interest efficiently~\cite{Nayak04}, but in the same time  the power consumption should be minimized. Sensor nodes runs on batteries with limited capacities~\cite{Sudip03}  and it is impossible, difficult or expensive to recharge and/or replace batteries in  remote, hostile,  or  unpractical environments. 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.
-
-In this paper we concentrate on the area coverage problem with the objective of
-maximizing the  network lifetime by using DiLCO protocol to maintain the coverage and to improve  the  lifetime  in  WSNs. The  area of interest is divided into subregions using divide-and-conquer method and an activity scheduling for sensor nodes is planned by the elected leader in each subregion. In fact,  the nodes in a subregion can be seen as a cluster  where each node sends  sensing data to  the cluster head or  the sink node.  Furthermore, the  activities in a subregion/cluster can  continue even if another  cluster stops due  to too  many node  failures.  Our  DiLCO protocol considers periods, where  a period starts with a discovery phase to exchange information between sensors of the subregion, in order to choose in a suitable manner a sensor  node (the leader) to carry out the coverage strategy.  Our DiLCO protocol involves solving an integer program, which provides the activation of the sensors for the sensing phase of the current period.
-
-The remainder of the paper is organized as follows. The next section reviews  the related  work  in  the field.  Section~\ref{sec:The DiLCO Protocol Description} is devoted to the DiLCO 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} shows the simulation results.  Finally, we give concluding remarks and some suggestions for
-future works in Section~\ref{sec:Conclusion and Future Works}.
+Energy efficiency  is a crucial issue  in wireless sensor  networks since sensor
+nodes drain their  energy from batteries. In fact,  strong constraints on energy
+consumption,  in order  to maximize  the network  lifetime, represent  the major
+difficulty when designing WSNs. As a consequence, one of the scientific research
+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. The  most discussed coverage problems  in literature
+can  be classified into  three types  \cite{li2013survey}: area  coverage (where
+every point inside an area is  to be monitored), target coverage (where the main
+objective is to  cover only a finite number of  discrete points called targets),
+and  barrier coverage (to  prevent intruders  from entering  into the  region of
+interest). On the one  hand we want to monitor the area  of interest in the most
+efficient way~\cite{Nayak04}. On the other hand we want to use as less energy as
+possible. % TO BE CONTINUED
+Sensor nodes runs on batteries with limited capacities~\cite{Sudip03}
+and  it  is  impossible,  difficult  or expensive  to  recharge  and/or  replace
+batteries  in remote,  hostile, or  unpractical environments.  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.
+
+In this paper we concentrate on  the area coverage problem with the objective of
+maximizing the network lifetime by using DiLCO protocol to maintain the coverage
+and  to improve  the lifetime  in WSNs.  The area  of interest  is  divided into
+subregions using divide-and-conquer method and an activity scheduling for sensor
+nodes is planned by the elected leader  in each subregion. In fact, the nodes in
+a subregion can be  seen as a cluster where each node  sends sensing data to the
+cluster   head  or   the  sink   node.    Furthermore,  the   activities  in   a
+subregion/cluster can  continue even  if another cluster  stops due to  too many
+node failures.  Our DiLCO protocol considers periods, where a period starts with
+a discovery phase  to exchange information between sensors  of the subregion, in
+order to choose in a suitable manner a sensor node (the leader) to carry out the
+coverage  strategy.  Our  DiLCO protocol  involves solving  an  integer program,
+which  provides the  activation of  the  sensors for  the sensing  phase of  the
+current period.
+
+The remainder  of the  paper continues with  Section~\ref{sec:Literature Review}
+where a  review of some related  works is presented. The  next section describes
+the  DiLCO  protocol,  followed   in  Section~\ref{cp}  by  the  coverage  model
+formulation    which    is    used     to    schedule    the    activation    of
+sensors. Section~\ref{sec:Simulation Results  and Analysis} shows the simulation
+results. The paper ends with conclusions and some suggestions for futher work in
+Section~\ref{sec:Conclusion and Future Works}.

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


@@ -99,7 +134,7 @@ problem. By using this model, they are proposed  an  Energy-Efficient central-Sc
 
 The work in~\cite{cheng2014achieving} presented a unified sensing architecture for duty cycled sensor networks, called uSense, which comprises three ideas: Asymmetric Architecture, Generic Switching and Global Scheduling. The objective is to  provide a flexible and efficient coverage in sensor networks.
 
 
 The work in~\cite{cheng2014achieving} presented a unified sensing architecture for duty cycled sensor networks, called uSense, which comprises three ideas: Asymmetric Architecture, Generic Switching and Global Scheduling. The objective is to  provide a flexible and efficient coverage in sensor networks.
 

- In~\cite{ling2009energy}, The lifetime of
+ In~\cite{ling2009energy}, the lifetime of

 a sensor node is divided into epochs. At each epoch, the
 base station deduces the current sensing coverage requirement
 from application or user request. It then applies the heuristic algorithm in order to produce the set of active nodes which take the mission of sensing during the current epoch.  After that, the produced schedule is sent to the sensor nodes in the network. 
 a sensor node is divided into epochs. At each epoch, the
 base station deduces the current sensing coverage requirement
 from application or user request. It then applies the heuristic algorithm in order to produce the set of active nodes which take the mission of sensing during the current epoch.  After that, the produced schedule is sent to the sensor nodes in the network. 


@@ -289,7 +324,7 @@ There are five status for each sensor node in the network :
 %\end{enumerate}
 %Below, we describe each phase in more details.
 Algorithm 1 gives a brief description of the protocol applied by each sensor node (denoted by $s_j$ for a sensor node indexed by $j$).
 %\end{enumerate}
 %Below, we describe each phase in more details.
 Algorithm 1 gives a brief description of the protocol applied by each sensor node (denoted by $s_j$ for a sensor node indexed by $j$).

-Initially, the sensor node checks its remaining energy in order to participate in the current period. Each sensor node determines its position and its subregion based Embedded GPS  or Location Discovery Algorithm. After that, all the sensors collect position coordinates, remaining energy $RE_j$, sensor node id, and the number of its one-hop live neighbors during the information exchange. 
+Initially, the sensor node checks its remaining energy in order to participate in the current period. After that, all the sensors collect position coordinates, remaining energy $RE_j$, sensor node id, and the number of its one-hop live neighbors during the information exchange. 

 Then all the sensor nodes in the same subregion will select the leader based on the received informations. The selection criteria for the leader in order  of priority  are: larger number  of neighbours,  larger remaining  energy, and  then in  case of equality, larger index. After that, if the sensor node is leader, it will execute the integer program algorithm (see section~\ref{cp}) which provides a set of sensors planned to be active in the sensing round. As leader, it will send an Active-Sleep packet to each sensor in the same subregion to indicate it if it has to be active or not. On the contrary, if the sensor is not the leader, it will wait for the Active-Sleep packet to know its state for the sensing round.
 
 
 Then all the sensor nodes in the same subregion will select the leader based on the received informations. The selection criteria for the leader in order  of priority  are: larger number  of neighbours,  larger remaining  energy, and  then in  case of equality, larger index. After that, if the sensor node is leader, it will execute the integer program algorithm (see section~\ref{cp}) which provides a set of sensors planned to be active in the sensing round. As leader, it will send an Active-Sleep packet to each sensor in the same subregion to indicate it if it has to be active or not. On the contrary, if the sensor is not the leader, it will wait for the Active-Sleep packet to know its state for the sensing round.
 
 


@@ -348,7 +383,7 @@ we first show the pseudo-code of DiLCO protocol, which is executed by each senso
   \BlankLine
   %\emph{Initialize the sensor node and determine it's position and subregion} \; 
   
   \BlankLine
   %\emph{Initialize the sensor node and determine it's position and subregion} \; 
   

-  \If{ $RE_j \geq E_{R}$ }{
+  \If{ $RE_j \geq E_{th}$ }{

       \emph{$s_j.status$ = COMMUNICATION}\;
       \emph{Send $INFO()$ packet to other nodes in the subregion}\;
       \emph{Wait $INFO()$ packet from other nodes in the subregion}\; 
       \emph{$s_j.status$ = COMMUNICATION}\;
       \emph{Send $INFO()$ packet to other nodes in the subregion}\;
       \emph{Wait $INFO()$ packet from other nodes in the subregion}\; 


@@ -629,7 +664,7 @@ transmit information on an event in the area that it monitors.
  Energy Consumption (EC) can be seen as the total energy consumed by the sensors during the $Lifetime95$ or $Lifetime50$ divided by the number of periods. The EC can be computed as follow: \\
  \begin{equation*}
 \scriptsize
  Energy Consumption (EC) can be seen as the total energy consumed by the sensors during the $Lifetime95$ or $Lifetime50$ divided by the number of periods. The EC can be computed as follow: \\
  \begin{equation*}
 \scriptsize

-\mbox{EC} = \frac{\sum\limits_{m=1}^{M_L} \left( E^{\mbox{com}}_m+E^{\mbox{list}}_m+E^{\mbox{comp}}_m  + E^{a}+E^{s} \right)}{M_L},
+\mbox{EC} = \frac{\sum\limits_{m=1}^{M} \left( E^{\mbox{com}}_m+E^{\mbox{list}}_m+E^{\mbox{comp}}_m  + E^{a}+E^{s} \right)}{M_L},

 \end{equation*}
 
 %\begin{equation*}
 \end{equation*}
 
 %\begin{equation*}


@@ -637,7 +672,7 @@ transmit information on an event in the area that it monitors.
 %\mbox{EC} =  \frac{\mbox{$\sum\limits_{d=1}^D E^c_d$}}{\mbox{$D$}} + \frac{\mbox{$\sum\limits_{d=1}^D %E^l_d$}}{\mbox{$D$}} + \frac{\mbox{$\sum\limits_{d=1}^D E^a_d$}}{\mbox{$D$}} + %\frac{\mbox{$\sum\limits_{d=1}^D E^s_d$}}{\mbox{$D$}}.
 %\end{equation*}
 
 %\mbox{EC} =  \frac{\mbox{$\sum\limits_{d=1}^D E^c_d$}}{\mbox{$D$}} + \frac{\mbox{$\sum\limits_{d=1}^D %E^l_d$}}{\mbox{$D$}} + \frac{\mbox{$\sum\limits_{d=1}^D E^a_d$}}{\mbox{$D$}} + %\frac{\mbox{$\sum\limits_{d=1}^D E^s_d$}}{\mbox{$D$}}.
 %\end{equation*}
 

-where $M_L$ corresponds to the number of  periods.  The total  energy consumed by  the sensors
+where $M$ corresponds to the number of  periods.  The total  energy consumed by  the sensors

 (EC) comes through taking into consideration four main energy factors. The first
 one ,  denoted $E^{\scriptsize \mbox{com}}_m$, represent  the energy consumption
 spent  by  all  the  nodes   for  wireless  communications  during  period  $m$.
 (EC) comes through taking into consideration four main energy factors. The first
 one ,  denoted $E^{\scriptsize \mbox{com}}_m$, represent  the energy consumption
 spent  by  all  the  nodes   for  wireless  communications  during  period  $m$.


@@ -645,7 +680,7 @@ $E^{\scriptsize  \mbox{list}}_m$, the  next  factor, corresponds  to the  energy
 consumed by the sensors in LISTENING  status before receiving the decision to go
 active or  sleep in  period $m$. $E^{\scriptsize  \mbox{comp}}_m$ refers  to the
 energy needed  by all  the leader nodes  to solve  the integer program  during a
 consumed by the sensors in LISTENING  status before receiving the decision to go
 active or  sleep in  period $m$. $E^{\scriptsize  \mbox{comp}}_m$ refers  to the
 energy needed  by all  the leader nodes  to solve  the integer program  during a

-period. Finally, $E^a_{m}$ and $E^s_{m}$  indicate the energy consummed by the whole network in the sensing round.
+period. Finally, $E^a_{m}$ and $E^s_{m}$  indicate the energy consumed by the whole network in the sensing round.

 
 \iffalse 
 \item {{\bf Execution Time}:} a  sensor  node has  limited  energy  resources  and computing  power,
 
 \iffalse 
 \item {{\bf Execution Time}:} a  sensor  node has  limited  energy  resources  and computing  power,


@@ -673,7 +708,7 @@ Our method is compared with other two approaches. The first approach, called DES
 
 
 \subsubsection{Coverage Ratio} 
 
 
 \subsubsection{Coverage Ratio} 

-In this experiment, Figure~\ref{fig3} shows the average coverage ratio for 150 deployed nodes.  
+Figure~\ref{fig3} shows the average coverage ratio for 150 deployed nodes.  

 \parskip 0pt    
 \begin{figure}[h!]
 \centering
 \parskip 0pt    
 \begin{figure}[h!]
 \centering