X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Sensornets15.git/blobdiff_plain/a8ba58909a1f9c41e391f2cd39227b80becac946..83da7bc3841a8eba8c44f5d54d238668cc0f2cf5:/Example.tex?ds=inline

diff --git a/Example.tex b/Example.tex
index 08ab194..807bdda 100644
--- a/Example.tex
+++ b/Example.tex
@@ -70,12 +70,7 @@ 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
+sensor field is  monitored. 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.  Sensor nodes  are  battery-powered  with no  means  of recharging  or
 replacing, usually due to environmental (hostile or unpractical environments) or
@@ -110,7 +105,68 @@ in Section~\ref{sec:Conclusion and Future Works}.
 
 \section{\uppercase{Literature Review}}
 \label{sec:Literature Review}
-\noindent In this section, we summarize some related works regarding coverage lifetime maximization and scheduling, and distinguish our DiLCO protocol from the works presented in the literature. Some algorithms have been developed in ~\cite{yang2014energy,ChinhVu,vashistha2007energy,deschinkel2012column,shi2009,qu2013distributed,ling2009energy,xin2009area,cheng2014achieving,ling2009energy} to solve the area coverage problem so as to preserve coverage and prolong the network lifetime.
+\noindent In this section, we summarize some related works regarding coverage problem , and distinguish our DiLCO protocol from the works presented in the literature.\\
+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). 
+{\it In DiLCO protocol, the area coverage, ie the coverage
+of every point in the sensing region, is transformed to the coverage of a fraction of points called primary points. }
+
+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 an interest region 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.
+Active node selection is determined based on the problem
+requirements (e.g. area monitoring, connectivity, power
+efficiency). Different methods has been proposed in literature.
+
+{\it DiLCO protocol works in periods, 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.}
+
+Various approaches, including centralised, distributed and localized algorithms, have been proposed to extend the network lifetime. 
+%For instance, in order to hide the occurrence of faults, or the sudden unavailability of
+%sensor nodes, some distributed algorithms have been developed in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02}. 
+
+In distributed algorithms~\cite{yangnovel,ChinhVu,qu2013distributed}, information is disseminated throughout the network and sensors decide cooperatively by communicating with their neighbours which of them will remain in sleep mode for a certain period of time. 
+The 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. 
+
+A large variety of coverage scheduling algorithms have been proposed in the literature. Many of the existing algorithms, dealing with the maximisation 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. Other approaches are based on mathematical programming formulations 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, of the 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 been also used~\cite{castano2013column,rossi2012exact,deschinkel2012column}.
+
+Diongue and Thiare~\cite{diongue2013alarm} proposed an energy aware sleep scheduling algorithm for lifetime maximization in wireless sensor networks (ALARM).  The proposed approach permits to schedule redundant nodes according to the weibull distribution. This work did not analyze the ALARM scheme under the coverage problem. 
+
+Shi et al.~\cite{shi2009} modeled the Area Coverage Problem (ACP), which will be changed into a set coverage
+problem. By using this model, they are proposed  an  Energy-Efficient central-Scheduling greedy algorithm, which can reduces energy consumption and increases network lifetime, by selecting a appropriate subset of sensor nodes to support the networks periodically.
+
+In ~\cite{chenait2013distributed}, the authors presented a coverage-guaranteed distributed sleep/wake scheduling scheme so as to prolong network lifetime while guaranteeing network coverage. This scheme mitigates scheduling process to be more stable by avoiding useless transitions between states without affecting the coverage level required by the application.
+
+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
+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. 
+
+{\it In DiLCO protocol, the area coverage is divided into several smaller subregions, and in each of which, a node called the leader is on charge for selecting the active sensors for the current period.} 
+
+Yang et al.~\cite{yang2014energy} investigated full area coverage problem
+under the probabilistic sensing model in the sensor networks. They have studied the relationship between the
+coverage of two adjacent points mathematically and then convert the problem of full area coverage into point coverage problem. They proposed $\varepsilon$-full area coverage optimization (FCO) algorithm to select a subset
+of sensors to provide probabilistic area coverage dynamically so as to extend the network lifetime.
+
+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 proposed by \cite{qu2013distributed} considers the coverage problem in WSNs where each sensor has variable sensing radius. The final objective is to maximize the network coverage lifetime in WSNs.
+
+{\it In DiLCO protocol, each leader, in each subregion, solves an integer program with a double objective consisting in minimizing the overcoverage and limiting the undercoverage. This program is inspired from the work of \cite{pedraza2006} where the objective is to maximize the number of cover sets.} 
+ 
+
+\iffalse
+
+Some algorithms have been developed in ~\cite{yang2014energy,ChinhVu,vashistha2007energy,deschinkel2012column,shi2009,qu2013distributed,ling2009energy,xin2009area,cheng2014achieving,ling2009energy} to solve the area coverage problem so as to preserve coverage and prolong the network lifetime.
 
 
 Yang et al.~\cite{yang2014energy} investigated full area coverage problem
@@ -137,7 +193,7 @@ The work in~\cite{cheng2014achieving} presented a unified sensing architecture f
 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. 
-
+\fi
 
 \iffalse
 
@@ -155,7 +211,7 @@ coverage. They are proposed a low-complexity heuristic algorithm to obtain full
 achieve increased sensing lifetime of the network. 
 
 
-\fi
+
   
 
 
@@ -163,10 +219,10 @@ In \cite{xu2001geography}, Xu et al. proposed a Geographical Adaptive Fidelity (
 
 The main contributions of our DiLCO Protocol can be summarized as follows:
 (1) The distributed optimization over the subregions in the area of interest, 
-(2) The distributed dynamic leader election at each round by each sensor node in the subregion, 
+(2) The distributed dynamic leader election at each period by each sensor node in the subregion, 
 (3) The primary point coverage model to represent each sensor node in the network, 
 (4) The activity scheduling based optimization on the subregion, which are based on  the primary point coverage model to activate as less number as possible of sensor nodes  to take the mission of the coverage in each subregion, and (5) The improved energy consumption model.
-
+\fi
 \iffalse
 The work presented in~\cite{luo2014parameterized,tian2014distributed} tries to solve the target coverage problem so as to extend the network lifetime since it is easy to verify the coverage status of discreet target.
 %Je ne comprends pas la phrase ci-dessus
@@ -178,9 +234,9 @@ Our Work in~\cite{idrees2014coverage} proposes a coverage optimization protocol
 The work presented in ~\cite{Zhang} focuses on a distributed clustering method, which aims to extend the network lifetime, while the coverage is ensured.
 
 The work proposed by \cite{qu2013distributed} considers the coverage problem in WSNs where each sensor has variable sensing radius. The final objective is to maximize the network coverage lifetime in WSNs.
-\fi
 
-\iffalse
+
+
 Casta{\~n}o et al.~\cite{castano2013column} proposed a multilevel approach based on column generation (CG) to  extend the network lifetime with connectivity and coverage constraints. They are included  two heuristic methods  within the CG framework so as to accelerate the solution process. 
 In \cite{diongue2013alarm}, diongue is proposed an energy Aware sLeep scheduling AlgoRithm for lifetime maximization in WSNs (ALARM) algorithm for coverage lifetime maximization in wireless sensor networks. ALARM is sensor node scheduling approach for lifetime maximization in WSNs in which it schedule redundant nodes according to the weibull distribution  taking into consideration frequent nodes failure.
 Yu et al.~\cite{yu2013cwsc} presented a connected k-coverage working sets construction
@@ -669,7 +725,17 @@ the efficiency of our approach:
 
 %\begin{enumerate}[i)]
 \begin{itemize}
+\item {{\bf Network Lifetime}:} we define the network lifetime as the time until
+  the  coverage  ratio  drops  below  a  predefined  threshold.   We  denote  by
+  $Lifetime_{95}$ (respectively $Lifetime_{50}$) the amount of time during which
+  the  network can  satisfy an  area coverage  greater than  $95\%$ (respectively
+  $50\%$). We assume that the sensor  network can fulfill its task until all its
+  nodes have  been drained of their  energy or it  becomes disconnected. Network
+  connectivity  is crucial because  an active  sensor node  without connectivity
+  towards a base  station cannot transmit any information  regarding an observed
+  event in the area that it monitors.
   
+    
 \item {{\bf Coverage Ratio (CR)}:} it measures how well the WSN is able to 
   observe the area of interest. In our case, we discretized the sensor field
   as a regular grid, which yields the following equation to compute the
@@ -721,15 +787,6 @@ 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 consumed
 by the whole network in the sensing phase (active and sleeping nodes).
 
-\item {{\bf Network Lifetime}:} we define the network lifetime as the time until
-  the  coverage  ratio  drops  below  a  predefined  threshold.   We  denote  by
-  $Lifetime_{95}$ (respectively $Lifetime_{50}$) the amount of time during which
-  the  network can  satisfy an  area coverage  greater than  $95\%$ (respectively
-  $50\%$). We assume that the sensor  network can fulfill its task until all its
-  nodes have  been drained of their  energy or it  becomes disconnected. Network
-  connectivity  is crucial because  an active  sensor node  without connectivity
-  towards a base  station cannot transmit any information  regarding an observed
-  event in the area that it monitors.
 
 \iffalse 
 \item {{\bf Execution Time}:} a  sensor  node has  limited  energy  resources  and computing  power,
@@ -774,7 +831,7 @@ the number of  active nodes: the optimization process  of our protocol activates
 less nodes  than DESK  or GAF, resulting  in a  slight decrease of  the coverage
 ratio. In case of DiLCO-2  (respectively DiLCO-4), the coverage ratio exhibits a
 fast decrease with  the number of periods and reaches zero  value in period {\bf
-  X} (respectively {\bf Y}), whereas the  other versions of DiLCO, DESK, and GAF
+  18} (respectively {\bf 46}), whereas the  other versions of DiLCO, DESK, and GAF
 ensure a coverage  ratio above 50\% for subsequent periods.  We believe that the
 results obtained with  these two methods can be explained  by a high consumption
 of energy and we will check this assumption in the next subsection.
