%% \address{Address\fnref{label3}}
%% \fntext[label3]{}
-\title{Multiperiod Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}
+\title{Multiround Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}
%% use optional labels to link authors explicitly to addresses:
%% \author[label1,label2]{}
%% \address[label1]{}
%% \address[label2]{}
-\author{Ali Kadhum Idrees, Karine Deschinkel, \\
-Michel Salomon, and Rapha\"el Couturier}
+%\author{Ali Kadhum Idrees, Karine Deschinkel, \\
+%Michel Salomon, and Rapha\"el Couturier}
+
%\thanks{are members in the AND team - DISC department - FEMTO-ST Institute, University of Franche-Comt\'e, Belfort, France.
% e-mail: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr.}% <-this % stops a space
%\thanks{}% <-this % stops a space
-\address{FEMTO-ST Institute, University of Franche-Comt\'e, Belfort, France. \\
-e-mail: ali.idness@edu.univ-fcomte.fr, \\
-$\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr.}
+%\address{FEMTO-ST Institute, University of Franche-Comt\'e, Belfort, France. \\
+%e-mail: ali.idness@edu.univ-fcomte.fr, \\
+%$\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr.}
+
+
+\author{Ali Kadhum Idrees$^{a,b}$, Karine Deschinkel$^{a}$, \\
+Michel Salomon$^{a}$ and Rapha\"el Couturier $^{a}$ \\
+ $^{a}${\em{FEMTO-ST Institute, UMR 6174 CNRS, \\
+ University Bourgogne Franche-Comt\'e, Belfort, France}} \\
+ $^{b}${\em{Department of Computer Science, University of Babylon, Babylon, Iraq}}
+}
+
\begin{abstract}
%One of the fundamental challenges in Wireless Sensor Networks (WSNs)
%continuously and effectively when monitoring a certain area (or
%region) of interest.
Coverage and lifetime are two paramount problems in Wireless Sensor Networks
-(WSNs). In this paper, a method called Multiperiod Distributed Lifetime Coverage
+(WSNs). In this paper, a method called Multiround Distributed Lifetime Coverage
Optimization protocol (MuDiLCO) is proposed to maintain the coverage and to
improve the lifetime in wireless sensor networks. The area of interest is first
divided into subregions and then the MuDiLCO protocol is distributed on the
-sensor nodes in each subregion. The proposed MuDiLCO protocol works into periods
+sensor nodes in each subregion. The proposed MuDiLCO protocol works in periods
during which sets of sensor nodes are scheduled to remain active for a number of
rounds during the sensing phase, to ensure coverage so as to maximize the
lifetime of WSN. The decision process is carried out by a leader node, which
\end{abstract}
\begin{keyword}
-Wireless Sensor Networks, Area Coverage, Network lifetime,
+Wireless Sensor Networks, Area Coverage, Network Lifetime,
Optimization, Scheduling, Distributed Computation.
\end{keyword}
\indent The fast developments of low-cost sensor devices and wireless
communications have allowed the emergence of WSNs. A WSN includes a large number
-of small, limited-power sensors that can sense, process and transmit data over a
-wireless communication. They communicate with each other by using multi-hop
+of small, limited-power sensors that can sense, process, and transmit data over
+a wireless communication. They communicate with each other by using multi-hop
wireless communications and cooperate together to monitor the area of interest,
so that each measured data can be reported to a monitoring center called sink
-for further analysis~\cite{Sudip03}. There are several fields of application
+for further analysis~\cite{Sudip03}. There are several fields of application
covering a wide spectrum for a WSN, including health, home, environmental,
military, and industrial applications~\cite{Akyildiz02}.
On the one hand sensor nodes run on batteries with limited capacities, and it is
often costly or simply impossible to replace and/or recharge batteries,
especially in remote and hostile environments. Obviously, to achieve a long life
-of the network it is important to conserve battery power. Therefore, lifetime
+of the network it is important to conserve battery power. Therefore, lifetime
optimization is one of the most critical issues in wireless sensor networks. On
-the other hand we must guarantee coverage over the area of interest. To fulfill
+the other hand we must guarantee coverage over the area of interest. To fulfill
these two objectives, the main idea is to take advantage of overlapping sensing
regions to turn-off redundant sensor nodes and thus save energy. In this paper,
we concentrate on the area coverage problem, with the objective of maximizing
-the network lifetime by using an optimized multirounds scheduling.
+the network lifetime by using an optimized multiround scheduling.
% One of the major scientific research challenges in WSNs, which are addressed by a large number of literature during the last few years is to design energy efficient approaches for coverage and connectivity in WSNs~\cite{conti2014mobile}. The coverage problem is one of the
%fundamental challenges in WSNs~\cite{Nayak04} that consists in monitoring efficiently and continuously
The remainder of the paper is organized as follows. The next section
% Section~\ref{rw}
-reviews the related works in the field. Section~\ref{pd} is devoted to the
+reviews the related works in the field. Section~\ref{pd} is devoted to the
description of MuDiLCO protocol. Section~\ref{exp} shows the simulation results
-obtained using the discrete event simulator OMNeT++ \cite{varga}. They fully
+obtained using the discrete event simulator 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}.
+
+%%RC : Related works good for a phd thesis but too long for a paper. Ali you need to learn to .... summarize :-)
\section{Related works} % Trop proche de l'etat de l'art de l'article de Zorbas ?
\label{rw}
\item Sensors scheduling algorithm implementation, i.e. centralized or
distributed/localized algorithms.
\item The objective of sensor coverage, i.e. to maximize the network lifetime or
- to minimize the number of sensors during the sensing period.
+ to minimize the number of sensors during a sensing round.
\item The homogeneous or heterogeneous nature of the nodes, in terms of sensing
or communication capabilities.
\item The node deployment method, which may be random or deterministic.
-\item Additional requirements for energy-efficient coverage and connected
- coverage.
+\item Additional requirements for energy-efficient and connected coverage.
\end{itemize}
The choice of non-disjoint or disjoint cover sets (sensors participate or not in
many cover sets) can be added to the above list.
% The independency in the cover set (i.e. whether the cover sets are disjoint or non-disjoint) \cite{zorbas2010solving} is another design choice that can be added to the above list.
+
+\subsection{Centralized approaches}
+
+The major approach is to divide/organize the sensors into a suitable number of
+cover sets where each set completely covers an interest region and to activate
+these cover sets successively. The centralized algorithms always provide nearly
+or close to optimal solution since the algorithm has global view of the whole
+network. Note that centralized algorithms have the advantage of requiring very
+low processing power from the sensor nodes, which usually have limited
+processing capabilities. The main drawback of this kind of approach is its
+higher cost in communications, since the node that will make the decision needs
+information from all the sensor nodes. Moreover, centralized approaches usually
+suffer from the scalability problem, making them less competitive as the network
+size increases.
+
+The first algorithms proposed in the literature consider that the cover sets are
+disjoint: a sensor node appears in exactly one of the generated cover
+sets~\cite{abrams2004set,cardei2005improving,Slijepcevic01powerefficient}. In
+the case of non-disjoint algorithms \cite{pujari2011high}, sensors may
+participate in more than one cover set. In some cases, this may prolong the
+lifetime of the network in comparison to the disjoint cover set algorithms, but
+designing algorithms for non-disjoint cover sets generally induces a higher
+order of complexity. Moreover, in case of a sensor's failure, non-disjoint
+scheduling policies are less resilient and reliable because a sensor may be
+involved in more than one cover sets.
+%For instance, the proposed work in ~\cite{cardei2005energy, berman04}
+
+In~\cite{yang2014maximum}, the authors have considered a linear programming
+approach to select the minimum number of working sensor nodes, in order to
+preserve a maximum coverage and to extend lifetime of the network. Cheng et
+al.~\cite{cheng2014energy} have defined a heuristic algorithm called Cover Sets
+Balance (CSB), which chooses a set of active nodes using the tuple (data
+coverage range, residual energy). Then, they have introduced a new Correlated
+Node Set Computing (CNSC) algorithm to find the correlated node set for a given
+node. After that, they proposed a High Residual Energy First (HREF) node
+selection algorithm to minimize the number of active nodes so as to prolong the
+network lifetime. Various centralized methods based on column generation
+approaches have also been
+proposed~\cite{castano2013column,rossi2012exact,deschinkel2012column}.
+
+\subsection{Distributed approaches}
+%{\bf Distributed approaches}
+In distributed and localized coverage algorithms, the required computation to
+schedule the activity of sensor nodes will be done by the cooperation among
+neighboring nodes. These algorithms may require more computation power for the
+processing by the cooperating sensor nodes, but they are more scalable for large
+WSNs. Localized and distributed algorithms generally result in non-disjoint set
+covers.
+
+Many distributed algorithms have been developed to perform the scheduling so as
+to preserve coverage, see for example
+\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02, yardibi2010distributed,
+ prasad2007distributed,Misra}. Distributed algorithms typically operate in
+rounds for a predetermined duration. At the beginning of each round, a sensor
+exchanges information with its neighbors and makes a decision to either remain
+turned on or to go to sleep for the round. This decision is basically made on
+simple greedy criteria like the largest uncovered area
+\cite{Berman05efficientenergy} or maximum uncovered targets
+\cite{lu2003coverage}. The Distributed Adaptive Sleep Scheduling Algorithm
+(DASSA) \cite{yardibi2010distributed} does not require location information of
+sensors while maintaining connectivity and satisfying a user defined coverage
+target. In DASSA, nodes use the residual energy levels and feedback from the
+sink for scheduling the activity of their neighbors. This feedback mechanism
+reduces the randomness in scheduling that would otherwise occur due to the
+absence of location information. In \cite{ChinhVu}, the author have designed a
+novel distributed heuristic, called Distributed Energy-efficient Scheduling for
+k-coverage (DESK), which ensures that the energy consumption among the sensors
+is balanced and the lifetime maximized while the coverage requirement is
+maintained. This heuristic works in rounds, requires only one-hop neighbor
+information, and each sensor decides its status (active or sleep) based on the
+perimeter coverage model from~\cite{Huang:2003:CPW:941350.941367}.
+
+%Our Work, which is presented in~\cite{idrees2014coverage} proposed a coverage optimization protocol to improve the lifetime in
+%heterogeneous energy wireless sensor networks.
+%In this work, the coverage protocol distributed in each sensor node in the subregion but the optimization take place over the the whole subregion. We consider only distributing the coverage protocol over two subregions.
+
+The works presented in \cite{Bang, Zhixin, Zhang} focus on coverage-aware,
+distributed energy-efficient, and distributed clustering methods respectively,
+which aim at extending the network lifetime, while the coverage is ensured.
+More recently, Shibo et al. \cite{Shibo} have expressed the coverage problem as
+a minimum weight submodular set cover problem and proposed a Distributed
+Truncated Greedy Algorithm (DTGA) to solve it. They take advantage from both
+temporal and spatial correlations between data sensed by different sensors, and
+leverage prediction, to improve the lifetime. In \cite{xu2001geography}, Xu et
+al. have described an algorithm, called Geographical Adaptive Fidelity (GAF),
+which uses geographic location information to divide the area of interest into
+fixed square grids. Within each grid, it keeps only one node staying awake to
+take the responsibility of sensing and communication.
+
+Some other approaches (outside the scope of our work) do not consider a
+synchronized and predetermined time-slot where the sensors are active or not.
+Indeed, each sensor maintains its own timer and its wake-up time is randomized
+\cite{Ye03} or regulated \cite{cardei2005maximum} over time.
+
+The MuDiLCO protocol (for Multiround Distributed Lifetime Coverage Optimization
+protocol) presented in this paper is an extension of the approach introduced
+in~\cite{idrees2014coverage}. In~\cite{idrees2014coverage}, the protocol is
+deployed over only two subregions. Simulation results have shown that it was
+more interesting to divide the area into several subregions, given the
+computation complexity. Compared to our previous paper, in this one we study the
+possibility of dividing the sensing phase into multiple rounds and we also add
+an improved model of energy consumption to assess the efficiency of our
+approach. In fact, in this paper we make a multiround optimization, while it was
+a single round optimization in our previous work.
+
+\iffalse
\subsection{Centralized Approaches}
%{\bf Centralized approaches}
The first algorithms proposed in the literature consider that the cover sets are
disjoint: a sensor node appears in exactly one of the generated cover sets. For
-instance, Slijepcevic and Potkonjak \cite{Slijepcevic01powerefficient} proposed
-an algorithm, which allocates sensor nodes in mutually independent sets to
-monitor an area divided into several fields. Their algorithm builds a cover set
-by including in priority the sensor nodes which cover critical fields, that is
-to say fields that are covered by the smallest number of sensors. The time
-complexity of their heuristic is $O(n^2)$ where $n$ is the number of
-sensors. Abrams et al.~\cite{abrams2004set} designed three approximation
-algorithms for a variation of the set k-cover problem, where the objective is to
-partition the sensors into covers such that the number of covers that include an
-area, summed over all areas, is maximized. Their work builds upon previous work
+instance, Slijepcevic and Potkonjak \cite{Slijepcevic01powerefficient} have
+proposed an algorithm, which allocates sensor nodes in mutually independent sets
+to monitor an area divided into several fields. Their algorithm builds a cover
+set by including in priority the sensor nodes which cover critical fields, that
+is to say fields that are covered by the smallest number of sensors. The time
+complexity of their heuristic is $O(n^2)$ where $n$ is the number of sensors.
+Abrams et al.~\cite{abrams2004set} have designed three approximation algorithms
+for a variation of the set k-cover problem, where the objective is to partition
+the sensors into covers such that the number of covers that include an area,
+summed over all areas, is maximized. Their work builds upon previous work
in~\cite{Slijepcevic01powerefficient} and the generated cover sets do not
provide complete coverage of the monitoring zone.
-\cite{cardei2005improving} proposed a method to efficiently compute the maximum
-number of disjoint set covers such that each set can monitor all targets. They
-first transform the problem into a maximum flow problem, which is formulated as
-a mixed integer programming (MIP). Then their heuristic uses the output of the
-MIP to compute disjoint set covers. Results show that this heuristic provides a
-number of set covers slightly larger compared to
+In \cite{cardei2005improving}, the authors have proposed a method to efficiently
+compute the maximum number of disjoint set covers such that each set can monitor
+all targets. They first transform the problem into a maximum flow problem, which
+is formulated as a mixed integer programming (MIP). Then their heuristic uses
+the output of the MIP to compute disjoint set covers. Results show that this
+heuristic provides a number of set covers slightly larger compared to
\cite{Slijepcevic01powerefficient}, but with a larger execution time due to the
complexity of the mixed integer programming resolution.
Zorbas et al. \cite{zorbas2010solving} presented a centralized greedy algorithm
-for the efficient production of both node disjoint and non-disjoint cover
-sets. Compared to algorithm's results of Slijepcevic and Potkonjak
+for the efficient production of both node disjoint and non-disjoint cover sets.
+Compared to algorithm's results of Slijepcevic and Potkonjak
\cite{Slijepcevic01powerefficient}, their heuristic produces more disjoint cover
-sets with a slight growth rate in execution time. When producing non-disjoint
+sets with a slight growth rate in execution time. When producing non-disjoint
cover sets, both Static-CCF and Dynamic-CCF algorithms, where CCF means that
they use a cost function called Critical Control Factor, provide cover sets
-offering longer network lifetime than those produced by
-\cite{cardei2005energy}. Also, they require a smaller number of node
-participations in order to achieve these results.
+offering longer network lifetime than those produced by \cite{cardei2005energy}.
+Also, they require a smaller number of participating nodes in order to achieve
+these results.
In the case of non-disjoint algorithms \cite{pujari2011high}, sensors may
participate in more than one cover set. In some cases, this may prolong the
involved in more than one cover sets. For instance, Cardei et
al.~\cite{cardei2005energy} present a linear programming (LP) solution and a
greedy approach to extend the sensor network lifetime by organizing the sensors
-into a maximal number of non-disjoint cover sets. Simulation results show that
+into a maximal number of non-disjoint cover sets. Simulation results show that
by allowing sensors to participate in multiple sets, the network lifetime
increases compared with related work~\cite{cardei2005improving}.
In~\cite{berman04}, the authors have formulated the lifetime problem and
-suggested another (LP) technique to solve this problem. A centralized solution
+suggested another (LP) technique to solve this problem. A centralized solution
based on the Garg-K\"{o}nemann algorithm~\cite{garg98}, provably near the
optimal solution, is also proposed.
-In~\cite{yang2014maximum}, The authors are proposed a linear programming approach for selecting the minimum number of sensor nodes in working station so as to preserve a maximum coverage and extend lifetime of the network. Cheng et al.~\cite{cheng2014energy} are proposed a heuristic algorithm called Cover Sets Balance (CSB) algorithm to choose a set of active nodes using the tuple (data coverage range, residual energy). Then, they are introduced a new Correlated Node Set Computing (CNSC) algorithm to find the correlated node set for a given node. After that, they are proposed a High Residual Energy First (HREF) node selection algorithm to minimize the number of active nodes so as to prolong the network lifetime.
-In~\cite{castano2013column,rossi2012exact,deschinkel2012column}, The authors are proposed a centralized methods based on column generation approach to extend lifetime in wireless sensor networks while coverage preservation.
-
+In~\cite{yang2014maximum}, the authors have proposed a linear programming
+approach for selecting the minimum number of working sensor nodes, in order to
+as to preserve a maximum coverage and extend lifetime of the network. Cheng et
+al.~\cite{cheng2014energy} have defined a heuristic algorithm called Cover Sets
+Balance (CSB), which choose a set of active nodes using the tuple (data coverage
+range, residual energy). Then, they have introduced a new Correlated Node Set
+Computing (CNSC) algorithm to find the correlated node set for a given node.
+After that, they proposed a High Residual Energy First (HREF) node selection
+algorithm to minimize the number of active nodes so as to prolong the network
+lifetime. Various centralized methods based on column generation approaches have
+also been proposed~\cite{castano2013column,rossi2012exact,deschinkel2012column}.
\subsection{Distributed approaches}
%{\bf Distributed approaches}
In distributed and localized coverage algorithms, the required computation to
schedule the activity of sensor nodes will be done by the cooperation among
neighboring nodes. These algorithms may require more computation power for the
-processing by the cooperating sensor nodes, but they are more scalable for
-large WSNs. Localized and distributed algorithms generally result in
-non-disjoint set covers.
-
-Some distributed algorithms have been developed
-in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02, yardibi2010distributed}
-to perform the scheduling so as to preserve coverage. Distributed algorithms
-typically operate in rounds for a predetermined duration. At the beginning of
-each round, a sensor exchanges information with its neighbors and makes a
-decision to either remain turned on or to go to sleep for the round. This
-decision is basically made on simple greedy criteria like the largest uncovered
-area \cite{Berman05efficientenergy} or maximum uncovered targets
-\cite{lu2003coverage}. In \cite{Tian02}, the scheduling scheme is divided into
-rounds, where each round has a self-scheduling phase followed by a sensing
-phase. Each sensor broadcasts a message containing the node~ID and the node
-location to its neighbors at the beginning of each round. A sensor determines
-its status by a rule named off-duty eligible rule, which tells him to turn off
-if its sensing area is covered by its neighbors. A back-off scheme is introduced
-to let each sensor delay the decision process with a random period of time, in
-order to avoid simultaneous conflicting decisions between nodes and lack of
-coverage on any area. \cite{prasad2007distributed} defines a model for
-capturing the dependencies between different cover sets and proposes localized
-heuristic based on this dependency. The algorithm consists of two phases, an
-initial setup phase during which each sensor computes and prioritizes the covers
-and a sensing phase during which each sensor first decides its on/off status,
-and then remains on or off for the rest of the duration.
-
-The authors in \cite{yardibi2010distributed} developed a Distributed Adaptive
-Sleep Scheduling Algorithm (DASSA) for WSNs with partial coverage. DASSA does
-not require location information of sensors while maintaining connectivity and
-satisfying a user defined coverage target. In DASSA, nodes use the residual
-energy levels and feedback from the sink for scheduling the activity of their
-neighbors. This feedback mechanism reduces the randomness in scheduling that
-would otherwise occur due to the absence of location information. In
-\cite{ChinhVu}, the author proposed a novel distributed heuristic, called
-Distributed Energy-efficient Scheduling for k-coverage (DESK), which ensures
-that the energy consumption among the sensors is balanced and the lifetime
-maximized while the coverage requirement is maintained. This heuristic works in
-rounds, requires only one-hop neighbor information, and each sensor decides its
-status (active or sleep) based on the perimeter coverage model proposed in
-\cite{Huang:2003:CPW:941350.941367}.
+processing by the cooperating sensor nodes, but they are more scalable for large
+WSNs. Localized and distributed algorithms generally result in non-disjoint set
+covers.
+
+Many distributed algorithms have been developed to perform the scheduling so as
+to preserve coverage, see for example
+\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02,yardibi2010distributed}.
+Distributed algorithms typically operate in rounds for a predetermined
+duration. At the beginning of each round, a sensor exchanges information with
+its neighbors and makes a decision to either remain turned on or to go to sleep
+for the round. This decision is basically made on simple greedy criteria like
+the largest uncovered area \cite{Berman05efficientenergy} or maximum uncovered
+targets \cite{lu2003coverage}. In \cite{Tian02}, the scheduling scheme is
+divided into rounds, where each round has a self-scheduling phase followed by a
+sensing phase. Each sensor broadcasts a message containing the node~ID and the
+node location to its neighbors at the beginning of each round. A sensor
+determines its status by a rule named off-duty eligible rule, which tells him to
+turn off if its sensing area is covered by its neighbors. A back-off scheme is
+introduced to let each sensor delay the decision process with a random period of
+time, in order to avoid simultaneous conflicting decisions between nodes and
+lack of coverage on any area. In \cite{prasad2007distributed} a model for
+capturing the dependencies between different cover sets is defined and it
+proposes localized heuristic based on this dependency. The algorithm consists of
+two phases, an initial setup phase during which each sensor computes and
+prioritizes the covers and a sensing phase during which each sensor first
+decides its on/off status, and then remains on or off for the rest of the
+duration.
+
+The authors in \cite{yardibi2010distributed} have developed a Distributed
+Adaptive Sleep Scheduling Algorithm (DASSA) for WSNs with partial coverage.
+DASSA does not require location information of sensors while maintaining
+connectivity and satisfying a user defined coverage target. In DASSA, nodes use
+the residual energy levels and feedback from the sink for scheduling the
+activity of their neighbors. This feedback mechanism reduces the randomness in
+scheduling that would otherwise occur due to the absence of location
+information. In \cite{ChinhVu}, the author have proposed a novel distributed
+heuristic, called Distributed Energy-efficient Scheduling for k-coverage (DESK),
+which ensures that the energy consumption among the sensors is balanced and the
+lifetime maximized while the coverage requirement is maintained. This heuristic
+works in rounds, requires only one-hop neighbor information, and each sensor
+decides its status (active or sleep) based on the perimeter coverage model
+proposed in \cite{Huang:2003:CPW:941350.941367}.
%Our Work, which is presented in~\cite{idrees2014coverage} proposed a coverage optimization protocol to improve the lifetime in
%heterogeneous energy wireless sensor networks.
%In this work, the coverage protocol distributed in each sensor node in the subregion but the optimization take place over the the whole subregion. We consider only distributing the coverage protocol over two subregions.
-The works presented in \cite{Bang, Zhixin, Zhang} focuses on coverage-aware,
+The works presented in \cite{Bang, Zhixin, Zhang} focus on coverage-aware,
distributed energy-efficient, and distributed clustering methods respectively,
-which aims to extend the network lifetime, while the coverage is ensured. S.
-Misra et al. \cite{Misra} proposed a localized algorithm for coverage in sensor
-networks. The algorithm conserve the energy while ensuring the network coverage
-by activating the subset of sensors with the minimum overlap area. The proposed
-method preserves the network connectivity by formation of the network backbone.
-More recently, Shibo et al. \cite{Shibo} expressed the coverage problem as a
-minimum weight submodular set cover problem and proposed a Distributed Truncated
-Greedy Algorithm (DTGA) to solve it. They take advantage from both temporal and
-spatial correlations between data sensed by different sensors, and leverage
-prediction, to improve the lifetime. In \cite{xu2001geography}, Xu et
-al. proposed an algorithm, called Geographical Adaptive Fidelity (GAF), which
-uses geographic location information to divide the area of interest into fixed
-square grids. Within each grid, it keeps only one node staying awake to take the
-responsibility of sensing and communication.
+which aim to extend the network lifetime, while the coverage is ensured. S.
+Misra et al. \cite{Misra} have proposed a localized algorithm for coverage in
+sensor networks. The algorithm conserve the energy while ensuring the network
+coverage by activating the subset of sensors with the minimum overlap area. The
+proposed method preserves the network connectivity by formation of the network
+backbone. More recently, Shibo et al. \cite{Shibo} have expressed the coverage
+problem as a minimum weight submodular set cover problem and proposed a
+Distributed Truncated Greedy Algorithm (DTGA) to solve it. They take advantage
+from both temporal and spatial correlations between data sensed by different
+sensors, and leverage prediction, to improve the lifetime. In
+\cite{xu2001geography}, Xu et al. have proposed an algorithm, called
+Geographical Adaptive Fidelity (GAF), which uses geographic location information
+to divide the area of interest into fixed square grids. Within each grid, it
+keeps only one node staying awake to take the responsibility of sensing and
+communication.
Some other approaches (outside the scope of our work) do not consider a
synchronized and predetermined period of time where the sensors are active or
not. Indeed, each sensor maintains its own timer and its wake-up time is
randomized \cite{Ye03} or regulated \cite{cardei2005maximum} over time.
-The MuDiLCO protocol (for Multiperiod Distributed Lifetime Coverage Optimization
+The MuDiLCO protocol (for Multiround Distributed Lifetime Coverage Optimization
protocol) presented in this paper is an extension of the approach introduced
-in~\cite{idrees2014coverage}. In~\cite{idrees2014coverage}, the protocol is
+in~\cite{idrees2014coverage}. In~\cite{idrees2014coverage}, the protocol is
deployed over only two subregions. Simulation results have shown that it was
more interesting to divide the area into several subregions, given the
computation complexity. Compared to our previous paper, in this one we study the
an improved model of energy consumption to assess the efficiency of our
approach.
+
+
+
+\fi
%The main contributions of our MuDiLCO Protocol can be summarized as follows:
%(1) The high coverage ratio, (2) The reduced number of active nodes, (3) The distributed optimization over the subregions in the area of interest, (4) The distributed dynamic leader election at each round based on some priority factors that led to energy consumption balancing among the nodes in the same subregion, (5) The primary point coverage model to represent each sensor node in the network, (6) 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 for a multirounds to take the mission of the coverage in each subregion, (7) The very low energy consumption, (8) The higher network lifetime.
%\section{Preliminaries}
%minimizing overcoverage (points covered by multiple active sensors
%simultaneously).
-%In this section, we introduce a Multiperiod Distributed Lifetime Coverage Optimization protocol, which is called MuDiLCO. It is distributed on each subregion in the area of interest. It is based on two efficient techniques: network
+%In this section, we introduce a Multiround Distributed Lifetime Coverage Optimization protocol, which is called MuDiLCO. It is distributed on each subregion in the area of interest. It is based on two efficient techniques: network
%leader election and sensor activity scheduling for coverage preservation and energy conservation continuously and efficiently to maximize the lifetime in the network.
%The main features of our MuDiLCO protocol:
%i)It divides the area of interest into subregions by using divide-and-conquer concept, ii)It requires only the information of the nodes within the subregion, iii) it divides the network lifetime into periods, which consists in round(s), iv)It based on the autonomous distributed decision by the nodes in the subregion to elect the Leader, v)It apply the activity scheduling based optimization on the subregion, vi) it achieves an energy consumption balancing among the nodes in the subregion by selecting different nodes as a leader during the network lifetime, vii) It uses the optimization to select the best representative non-disjoint sets of sensors in the subregion by optimize the coverage and the lifetime over the area of interest, viii)It uses our proposed primary point coverage model, which represent the sensing range of the sensor as a set of points, which are used by the our optimization algorithm, ix) It uses a simple energy model that takes communication, sensing and computation energy consumptions into account to evaluate the performance of our Protocol.
communication range satisfies $R_c \geq 2R_s$. In fact, Zhang and
Zhou~\cite{Zhang05} proved that if the transmission range fulfills the previous
hypothesis, a complete coverage of a convex area implies connectivity among the
-working nodes in the active mode.
+active nodes.
Instead of working with a continuous coverage area, we make it discrete by
considering for each sensor a set of points called primary points. Consequently,
we assume that the sensing disk defined by a sensor is covered if all of its
-primary points are covered. The choice of number and locations of primary points
-is the subject of another study not presented here.
+primary points are covered. The choice of number and locations of primary points is the subject of another study not presented here.
%By knowing the position (point center: ($p_x,p_y$)) of a wireless
%sensor node and its $R_s$, we calculate the primary points directly
%The sensor node enter in listening mode waiting to receive ActiveSleep packet from the leader after the decision to apply multi-round activity scheduling during the sensing phase. Each sensor node will execute the Algorithm~1 to know who is the leader. After that, if the sensor node is leader, It will execute the integer program algorithm ( see section~\ref{cp}) to optimize the coverage and the lifetime in it's subregion. After the decision, the optimization approach will produce the cover sets of sensor nodes to take the mission of coverage during the sensing phase for $T$ rounds. The leader will send ActiveSleep packet to each sensor node in the subregion to inform him to it's schedule for $T$ rounds during the period of sensing, either Active or sleep until the starting of next period. Based on the decision, the leader as other nodes in subregion, either go to be active or go to be sleep based on it's schedule for $T$ rounds during current sensing phase. the other nodes in the same subregion will stay in listening mode waiting the ActiveSleep packet from the leader. After finishing the time period for sensing, which are includes $T$ rounds, all the sensor nodes in the same subregion will start new period by executing the MuDiLCO protocol and the lifetime in the subregion will continue until all the sensor nodes are died or the network becomes disconnected in the subregion.
\subsection{Background idea}
-
-The area of interest can be divided using the divide-and-conquer
-strategy into smaller areas, called subregions, and then our MuDiLCO
-protocol will be implemented in each subregion in a distributed way.
-
-As can be seen in Figure~\ref{fig2}, our protocol works in periods
-fashion, where each is divided into 4 phases: Information~Exchange,
-Leader~Election, Decision, and Sensing. Each sensing phase may be
-itself divided into $T$ rounds and for each round a set of sensors
-(said a cover set) is responsible for the sensing task.
+%%RC : we need to clarify the difference between round and period. Currently it seems to be the same (for me at least).
+The area of interest can be divided using the divide-and-conquer strategy into
+smaller areas, called subregions, and then our MuDiLCO protocol will be
+implemented in each subregion in a distributed way.
+
+As can be seen in Figure~\ref{fig2}, our protocol works in periods fashion,
+where each is divided into 4 phases: Information~Exchange, Leader~Election,
+Decision, and Sensing. Each sensing phase may be itself divided into $T$ rounds
+and for each round a set of sensors (a cover set) is responsible for the sensing
+task. In this way a multiround optimization process is performed during each
+period after Information~Exchange and Leader~Election phases, in order to
+produce $T$ cover sets that will take the mission of sensing for $T$ rounds.
\begin{figure}[ht!]
-\centering
-\includegraphics[width=95mm]{Modelgeneral.pdf} % 70mm
+\centering \includegraphics[width=100mm]{Modelgeneral.pdf} % 70mm
\caption{The MuDiLCO protocol scheme executed on each node}
\label{fig2}
\end{figure}
% set cover responsible for the sensing task.
%For each round a set of sensors (said a cover set) is responsible for the sensing task.
-This protocol is reliable against an unexpected node failure, because
-it works in periods. On the one hand, if a node failure is detected
-before making the decision, the node will not participate to this
-phase, and, on the other hand, if the node failure occurs after the
-decision, the sensing task of the network will be temporarily
-affected: only during the period of sensing until a new period starts.
-
-The energy consumption and some other constraints can easily be taken
-into account, since the sensors can update and then exchange their
-information (including their residual energy) at the beginning of each
-period. However, the pre-sensing phases (Information Exchange, Leader
-Election, and Decision) are energy consuming for some nodes, even when
-they do not join the network to monitor the area.
+This protocol minimizes the impact of unexpected node failure (not due to batteries
+running out of energy), because it works in periods.
+%This protocol is reliable against an unexpected node failure, because it works in periods.
+%%RC : why? I am not convinced
+ On the one hand, if a node failure is detected before making the
+decision, the node will not participate to this phase, and, on the other hand,
+if the node failure occurs after the decision, the sensing task of the network
+will be temporarily affected: only during the period of sensing until a new
+period starts.
+%%RC so if there are at least one failure per period, the coverage is bad...
+%%MS if we want to be reliable against many node failures we need to have an
+%% overcoverage...
+
+The energy consumption and some other constraints can easily be taken into
+account, since the sensors can update and then exchange their information
+(including their residual energy) at the beginning of each period. However, the
+pre-sensing phases (Information Exchange, Leader Election, and Decision) are
+energy consuming for some nodes, even when they do not join the network to
+monitor the area.
%%%%%%%%%%%%%%%%%parler optimisation%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-We define two types of packets that will be used by the proposed
-protocol:
+We define two types of packets that will be used by the proposed protocol:
\begin{enumerate}[(a)]
-\item INFO packet: a such packet will be sent by each sensor node to
- all the nodes inside a subregion for information exchange.
-\item Active-Sleep packet: sent by the leader to all the nodes inside a
- subregion to inform them to remain Active or to go Sleep during the
- sensing phase.
+\item INFO packet: such a packet will be sent by each sensor node to all the
+ nodes inside a subregion for information exchange.
+\item Active-Sleep packet: sent by the leader to all the nodes inside a
+ subregion to inform them to remain Active or to go Sleep during the sensing
+ phase.
\end{enumerate}
There are five status for each sensor node in the network:
\begin{enumerate}[(a)]
-\item LISTENING: sensor node is waiting for a decision (to be active
- or not);
-\item COMPUTATION: sensor node has been elected as leader and applies
- the optimization process;
-\item ACTIVE: sensor node participate to the monitoring of the area;
+\item LISTENING: sensor node is waiting for a decision (to be active or not);
+\item COMPUTATION: sensor node has been elected as leader and applies the
+ optimization process;
+\item ACTIVE: sensor node is taking part in the monitoring of the area;
\item SLEEP: sensor node is turned off to save energy;
\item COMMUNICATION: sensor node is transmitting or receiving packet.
\end{enumerate}
\subsection{Leader Election phase}
-This step consists in choosing the Wireless Sensor Node Leader (WSNL),
-which will be responsible for executing the coverage algorithm. Each
-subregion in the area of interest will select its own WSNL
-independently for each period. All the sensor nodes cooperate to
-elect a WSNL. The nodes in the same subregion will select the leader
-based on the received informations from all other nodes in the same
-subregion. The selection criteria are, in order of importance: larger
-number of neighbors, larger remaining energy, and then in case of
-equality, larger index. Observations on previous simulations suggest
-to use the number of one-hop neighbors as the primary criterion to
-reduce energy consumption due to the communications.
+This step consists in choosing the Wireless Sensor Node Leader (WSNL), which
+will be responsible for executing the coverage algorithm. Each subregion in the
+area of interest will select its own WSNL independently for each period. All
+the sensor nodes cooperate to elect a WSNL. The nodes in the same subregion
+will select the leader based on the received information from all other nodes
+in the same subregion. The selection criteria are, in order of importance:
+larger number of neighbors, larger remaining energy, and then in case of
+equality, larger index. Observations on previous simulations suggest to use the
+number of one-hop neighbors as the primary criterion to reduce energy
+consumption due to the communications.
%the more priority selection factor is the number of $1-hop$ neighbors, $NBR j$, which can minimize the energy consumption during the communication Significantly.
%The pseudo-code for leader election phase is provided in Algorithm~1.
\subsection{Decision phase}
-Each WSNL will solve an integer program to select which cover sets
-will be activated in the following sensing phase to cover the
-subregion to which it belongs. The integer program will produce $T$
-cover sets, one for each round. The WSNL will send an Active-Sleep
-packet to each sensor in the subregion based on the algorithm's
-results, indicating if the sensor should be active or not in each
-round of the sensing phase. The integer program is based on the model
-proposed by \cite{pedraza2006} with some modification, where the
-objective is to find a maximum number of disjoint cover sets. To
-fulfill this goal, the authors proposed an integer program which
-forces undercoverage and overcoverage of targets to become minimal at
-the same time. They use binary variables $x_{jl}$ to indicate if
-sensor $j$ belongs to cover set $l$. In our model, we consider binary
-variables $X_{t,j}$ to determine the possibility of activation of
-sensor $j$ during the round $t$ of a given sensing phase. We also
-consider primary points as targets. The set of primary points is
-denoted by $P$ and the set of sensors by $J$. Only sensors able to be
-alive during at least one round are involved in the integer program.
+Each WSNL will solve an integer program to select which cover sets will be
+activated in the following sensing phase to cover the subregion to which it
+belongs. The integer program will produce $T$ cover sets, one for each round.
+The WSNL will send an Active-Sleep packet to each sensor in the subregion based
+on the algorithm's results, indicating if the sensor should be active or not in
+each round of the sensing phase. The integer program is based on the model
+proposed by \cite{pedraza2006} with some modifications, where the objective is
+to find a maximum number of disjoint cover sets. To fulfill this goal, the
+authors proposed an integer program which forces undercoverage and overcoverage
+of targets to become minimal at the same time. They use binary variables
+$x_{jl}$ to indicate if sensor $j$ belongs to cover set $l$. In our model, we
+consider binary variables $X_{t,j}$ to determine the possibility of activating
+sensor $j$ during round $t$ of a given sensing phase. We also consider primary
+points as targets. The set of primary points is denoted by $P$ and the set of
+sensors by $J$. Only sensors able to be alive during at least one round are
+involved in the integer program.
%parler de la limite en energie Et pour un round
-For a primary point $p$, let $\alpha_{j,p}$ denote the indicator
-function of whether the point $p$ is covered, that is:
+For a primary point $p$, let $\alpha_{j,p}$ denote the indicator function of
+whether the point $p$ is covered, that is:
\begin{equation}
\alpha_{j,p} = \left \{
\begin{array}{l l}
\end{array} \right.
\label{eq13}
\end{equation}
-More precisely, $\Theta_{t,p}$ represents the number of active sensor
-nodes minus one that cover the primary point $p$ during the round
-$t$. The Undercoverage variable $U_{t,p}$ of the primary point $p$
-during round $t$ is defined by:
+More precisely, $\Theta_{t,p}$ represents the number of active sensor nodes
+minus one that cover the primary point $p$ during round $t$. The
+Undercoverage variable $U_{t,p}$ of the primary point $p$ during round $t$ is
+defined by:
\begin{equation}
U_{t,p} = \left \{
\begin{array}{l l}
%(W_{\theta}+W_{\psi} = P) \label{eq19}
%\end{equation}
+%%RC why W_{\theta} is not defined (only one sentence)? How to define in practice Wtheta and Wu?
\begin{itemize}
-\item $X_{t,j}$: indicates whether or not the sensor $j$ is actively
- sensing during the round $t$ (1 if yes and 0 if not);
-\item $\Theta_{t,p}$ - {\it overcoverage}: the number of sensors minus
- one that are covering the primary point $p$ during the round $t$;
-\item $U_{t,p}$ - {\it undercoverage}: indicates whether or not the
- primary point $p$ is being covered during the round $t$ (1 if not
- covered and 0 if covered).
+\item $X_{t,j}$: indicates whether or not the sensor $j$ is actively sensing
+ during round $t$ (1 if yes and 0 if not);
+\item $\Theta_{t,p}$ - {\it overcoverage}: the number of sensors minus one that
+ are covering the primary point $p$ during round $t$;
+\item $U_{t,p}$ - {\it undercoverage}: indicates whether or not the primary
+ point $p$ is being covered during round $t$ (1 if not covered and 0 if
+ covered).
\end{itemize}
-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 equations by taking positive values. The constraint given
-by equation~(\ref{eq144}) guarantees that the sensor has enough energy
-($RE_j$ corresponds to its remaining energy) to be alive during the
-selected rounds knowing that $E_{R}$ is the amount of energy required
-to be alive during one round.
-
-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 weights $W_\theta$ and
-$W_U$ must be properly chosen so as to guarantee that the maximum
-number of points are covered during each round. In our simulations
-priority is given to the coverage by choosing $W_{\theta}$ very large
-compared to $W_U$.
+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 equations by taking
+positive values. The constraint given by equation~(\ref{eq144}) guarantees that
+the sensor has enough energy ($RE_j$ corresponds to its remaining energy) to be
+alive during the selected rounds knowing that $E_{R}$ is the amount of energy
+required to be alive during one round.
+
+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 weights $W_\theta$ and $W_U$ must be properly chosen so as
+to guarantee that the maximum number of points are covered during each round.
+%% MS W_theta is smaller than W_u => problem with the following sentence
+In our simulations priority is given to the coverage by choosing $W_{U}$ very
+large compared to $W_{\theta}$.
%The Active-Sleep packet includes the schedule vector with the number of rounds that should be applied by the receiving sensor node during the sensing phase.
\subsection{Sensing phase}
The sensing phase consists of $T$ rounds. Each sensor node in the subregion will
receive an Active-Sleep packet from WSNL, informing it to stay awake or to go to
-sleep for each round of the sensing phase. Algorithm~\ref{alg:MuDiLCO}, which
+sleep for each round of the sensing phase. Algorithm~\ref{alg:MuDiLCO}, which
will be executed by each node at the beginning of a period, explains how the
Active-Sleep packet is obtained.
\end{algorithm}
+%\textcolor{red}{\textbf{\textsc{Answer:} ali }}
+
+
+\section{Genetic Algorithm (GA) for Multiround Lifetime Coverage Optimization}
+\label{GA}
+Metaheuristics are a generic search strategies for exploring search spaces for solving the complex problems. These strategies have to dynamically balance between the exploitation of the accumulated search experience and the exploration of the search space. On one hand, this balance can find regions in the search space with high-quality solutions. On the other hand, it prevents waste too much time in regions of the search space which are either already explored or don’t provide high-quality solutions. Therefore, metaheuristic provides an enough good solution to an optimization problem, especially with incomplete information or limited computation capacity \cite{bianchi2009survey}. Genetic Algorithm (GA) is one of the population-based metaheuristic methods that simulates the process of natural selection \cite{hassanien2015applications}. GA starts with a population of random candidate solutions (called individuals or phenotypes) . GA uses genetic operators inspired by natural evolution, such as selection, mutation, evaluation, crossover, and replacement so as to improve the initial population of candidate solutions. This process repeated until a stopping criterion is satisfied.
+
+In this section, we present a metaheuristic based GA to solve our multiround lifetime coverage optimization problem. The proposed GA provides a near optimal sechedule for multiround sensing per period. The proposed GA is based on the mathematical model which is presented in Section \ref{pd}. Algorithm \ref{alg:GA} shows the proposed GA to solve the coverage lifetime optimization problem. We named the new protocol which is based on GA in the decision phase as GA-MuDiLCO. The proposed GA can be explained in more details as follow:
+
+\begin{algorithm}[h!]
+ \small
+ \SetKwInput{Input}{Input}
+ \SetKwInput{Output}{Output}
+ \Input{ $ P, J, T, S_{pop}, \alpha_{j,p}^{ind}, X_{t,j}^{ind}, \Theta_{t,p}^{ind}, U_{t,p}^{ind}, Child_{t,j}^{ind}, Ch.\Theta_{t,p}^{ind}, Ch.U_{t,p}^{ind_1}$}
+ \Output{$\left\{\left(X_{1,1},\dots, X_{t,j}, \dots, X_{T,J}\right)\right\}_{t \in T, j \in J}$}
+
+ \BlankLine
+ %\emph{Initialize the sensor node and determine it's position and subregion} \;
+ \ForEach {Individual $ind$ $\in$ $S_{pop}$} {
+ \emph{Generate Randomly Chromosome $\left\{\left(X_{1,1},\dots, X_{t,j}, \dots, X_{T,J}\right)\right\}_{t \in T, j \in J}$}\;
+
+ \emph{Update O-U-Coverage $\left\{(P, J, \alpha_{j,p}^{ind}, X_{t,j}^{ind}, \Theta_{t,p}^{ind}, U_{t,p}^{ind})\right\}_{p \in P}$}\;
+
+
+ \emph{Evaluate Individual $(P, J, X_{t,j}^{ind}, \Theta_{t,p}^{ind}, U_{t,p}^{ind})$}\;
+ }
+
+ \While{ Stopping criteria is not satisfied }{
+
+ \emph{Selection $(ind_1, ind_2)$}\;
+ \emph{Crossover $(P_c, X_{t,j}^{ind_1}, X_{t,j}^{ind_2}, Child_{t,j}^{ind_1}, Child_{t,j}^{ind_2})$}\;
+ \emph{Mutation $(P_m, Child_{t,j}^{ind_1}, Child_{t,j}^{ind_2})$}\;
+
+
+ \emph{Update O-U-Coverage $(P, J, \alpha_{j,p}^{ind}, Child_{t,j}^{ind_1}, Ch.\Theta_{t,p}^{ind_1}, Ch.U_{t,p}^{ind_1})$}\;
+ \emph{Update O-U-Coverage $(P, J, \alpha_{j,p}^{ind}, Child_{t,j}^{ind_2}, Ch.\Theta_{t,p}^{ind_2}, Ch.U_{t,p}^{ind_2})$}\;
+
+\emph{Evaluate New Individual$(P, J, Child_{t,j}^{ind_1}, Ch.\Theta_{t,p}^{ind_1}, Ch.U_{t,p}^{ind_1})$}\;
+ \emph{Replacement $(P, J, T, Child_{t,j}^{ind_1}, Ch.\Theta_{t,p}^{ind_1}, Ch.U_{t,p}^{ind_1}, X_{t,j}^{ind}, \Theta_{t,p}^{ind}, U_{t,p}^{ind} )$ }\;
+
+ \emph{Evaluate New Individual$(P, J, Child_{t,j}^{ind_2}, Ch.\Theta_{t,p}^{ind_2}, Ch.U_{t,p}^{ind_2})$}\;
+
+ \emph{Replacement $(P, J, T, Child_{t,j}^{ind_2}, Ch.\Theta_{t,p}^{ind_2}, Ch.U_{t,p}^{ind_2}, X_{t,j}^{ind}, \Theta_{t,p}^{ind}, U_{t,p}^{ind} )$ }\;
+
+
+ }
+ \emph{$\left\{\left(X_{1,1},\dots,X_{t,j},\dots,X_{T,J}\right)\right\}$ =
+ Select Best Solution ($S_{pop}$)}\;
+ \emph{return X} \;
+\caption{GA-MuDiLCO($s_j$)}
+\label{alg:GA}
+
+\end{algorithm}
+
+
+\begin{enumerate} [I)]
+\item \textbf{Representation:} Since the proposed GA's goal is to find the optimal schedule of the sensor nodes which take the responsibility of monitoring the subregion for $T$ rounds in the next phase, the chromosome is defined as a schedule for alive sensors and each chromosome contains $T$ rounds. Each round in the schedule includes J genes, the total alive sensors in the subregion. Therefore, the gene of such a chromosome is a schedule of a sensor. In other words, The genes corresponding to active nodes have the value of one, the others are zero. Figure \ref{chromo} shows solution representation in the proposed GA.
+%[scale=0.3]
+\begin{figure}[h!]
+\centering
+ \includegraphics [scale=0.35] {rep.eps}
+\caption{Candidate Solution representation by the proposed GA. }
+\label{chromo}
+\end{figure}
+
+
+
+\item \textbf{Initialize Population:} The initial population is randomly generated and each chromosome in the GA population represents a possible sensors schedule solution to cover the entire subregion for $T$ rounds during current period. Each sensor in the chromosome is given a random value (0 or 1) for all rounds. If the random value is 1, the remaining energy of this sensor should be adequate to activate this sensor during current round. Otherwise, the value is set to 0. The energy constraint is applied for each sensor during all rounds.
+
+
+\item \textbf{Update O-U-Coverage:}
+After creating the initial population, The overcoverage $\Theta_{t,p}$ and undercoverage $U_{t,p}$ for each candidate solution are computed (see Algorithm \ref{OU}) so as to use them in the next step.
+
+\begin{algorithm}[h!]
+
+ \SetKwInput{Input}{Input}
+ \SetKwInput{Output}{Output}
+ \Input{ parameters $P, J, ind, \alpha_{j,p}^{ind}, X_{t,j}^{ind}$}
+ \Output{$U^{ind} = \left\lbrace U_{1,1}^{ind}, \dots, U_{t,p}^{ind}, \dots, U_{T,P}^{ind} \right\rbrace$ and $\Theta^{ind} = \left\lbrace \Theta_{1,1}^{ind}, \dots, \Theta_{t,p}^{ind}, \dots, \Theta_{T,P}^{ind} \right\rbrace$}
+
+ \BlankLine
+
+ \For{$t\leftarrow 1$ \KwTo $T$}{
+ \For{$p\leftarrow 1$ \KwTo $P$}{
+
+ % \For{$i\leftarrow 0$ \KwTo $I_j$}{
+ \emph{$SUM\leftarrow 0$}\;
+ \For{$j\leftarrow 1$ \KwTo $J$}{
+ \emph{$SUM \leftarrow SUM + (\alpha_{j,p}^{ind} \times X_{t,j}^{ind})$ }\;
+ }
+
+ \If { SUM = 0} {
+ \emph{$U_{t,p}^{ind} \leftarrow 0$}\;
+ \emph{$\Theta_{t,p}^{ind} \leftarrow 1$}\;
+ }
+ \Else{
+ \emph{$U_{t,p}^{ind} \leftarrow SUM -1$}\;
+ \emph{$\Theta_{t,p}^{ind} \leftarrow 0$}\;
+ }
+
+ }
+
+ }
+\emph{return $U^{ind}, \Theta^{ind}$ } \;
+\caption{O-U-Coverage}
+\label{OU}
+
+\end{algorithm}
+
+
+
+\item \textbf{Evaluate Population:}
+After creating the initial population, each individual is evaluated and assigned a fitness value according to the fitness function is illustrated in Eq. \eqref{eqf}. In the proposed GA, the optimal (or near optimal) candidate solution, is the one with the minimum value for the fitness function. The lower the fitness values been assigned to an individual, the better opportunity it get survived. In our works, the function rewards the decrease in the sensor nodes which cover the same primary point and penalizes the decrease to zero in the sensor nodes which cover the primary point.
+
+\begin{equation}
+ F^{ind} \leftarrow \sum_{t=1}^{T} \sum_{p=1}^{P} \left(W_{\theta}* \Theta_{t,p} + W_{U} * U_{t,p} \right) \label{eqf}
+\end{equation}
+
+
+\item \textbf{Selection:} In order to generate a new generation, a portion of the existing population is elected based on a fitness function that ranks the fitness of each candidate solution and preferentially select the best solutions. Two parents should be selected to the mating pool. In the proposed GA-MuDiLCO algorithm, the first parent is selected by using binary tournament selection to select one of the parents \cite{goldberg1991comparative}. In this method, two individuals are chosen at random from population and the better of the two
+individuals is selected. If they have similar fitness values, one of them will be selected randomly. The best individual in the population is selected as a second parent.
+
+
+
+\item \textbf{Crossover:} Crossover is a genetic operator used to take more than one parent solutions and produce a child solution from them. If crossover probability $P_c$ is 100$\%$, then the crossover operation takes place between two individuals. If it is 0$\%$, the two selected individuals in the mating pool will be the new chromosomes without crossover. In the proposed GA, a two-point crossover is used. Figure \ref{cross} gives an example for a two-point crossover for 8 sensors in the subregion and the schedule for 3 rounds.
+
+
+\begin{figure}[h!]
+\centering
+ \includegraphics [scale = 0.3] {crossover.eps}
+\caption{Two-point crossover. }
+\label{cross}
+\end{figure}
+
+
+\item \textbf{Mutation:}
+Mutation is a divergence operation which introduces random modifications. The purpose of the mutation is to maintain diversity within the population and prevent premature convergence. Mutation is used to add new genetic information (divergence) in order to achieve a global search over the solution search space and avoid to fall in local optima. The mutation oprator in the proposed GA-MuDiLCO works as follow: If mutation probability $P_m$ is 100$\%$, then the mutation operation takes place on the the new individual. The round number is selected randomly within (1..T) in the schedule solution. After that one sensor within this round is selected randomly within (1..J). If the sensor is scheduled as active "1", it should be rescheduled to sleep "0". If the sensor is scheduled as sleep, it rescheduled to active only if it has adequate remaining energy.
+
+
+\item \textbf{Update O-U-Coverage for children:}
+Before evalute each new individual, Algorithm \ref{OU} is called for each new individual to compute the new undercoverage $Ch.U$ and overcoverage $Ch.\Theta$ parameters.
+
+\item \textbf{Evaluate New Individuals:}
+Each new individual is evaluated using Eq. \ref{eqf} but with using the new undercoverage $Ch.U$ and overcoverage $Ch.\Theta$ parameters of the new children.
+
+\item \textbf{Replacement:}
+After evaluatation of new children, Triple Tournament Replacement (TTR) will be applied for each new individual. In TTR strategy, three individuals are selected
+randomly from the population. Find the worst from them and then check its fitness with the new individual fitness. If the fitness of the new individual is better than the fitness of the worst individual, replace the new individual with the worst individual. Otherwise, the replacement is not done.
+
+
+\item \textbf{Stopping criteria:}
+The proposed GA-MuDiLCO stops when the stopping criteria is met. It stops after running for an amount of time in seconds equal to \textbf{Time limit}. The \textbf{Time limit} is the execution time obtained by the optimization solver GLPK for solving the same size of problem divided by two. The best solution will be selected as a schedule of sensors for $T$ rounds during the sensing phase in the current period.
+
+
+
+\end{enumerate}
+
+
+
\section{Experimental study}
\label{exp}
\subsection{Simulation setup}
25 runs.
%Based on the results of our proposed work in~\cite{idrees2014coverage}, we found as the region of interest are divided into larger subregions as the network lifetime increased. In this simulation, the network are divided into 16 subregions.
We performed simulations for five different densities varying from 50 to
-250~nodes. Experimental results are obtained from randomly generated networks in
-which nodes are deployed over a $50 \times 25~m^2 $ sensing field. More
+250~nodes deployed over a $50 \times 25~m^2 $ sensing field. More
precisely, the deployment is controlled at a coarse scale in order to ensure
that the deployed nodes can cover the sensing field with the given sensing
range.
+%%RC these parameters are realistic?
+%% maybe we can increase the field and sensing range. 5mfor Rs it seems very small... what do the other good papers consider ?
+
\begin{table}[ht]
\caption{Relevant parameters for network initializing.}
% title of Table
$E_{R}$ & 36 Joules\\
$R_s$ & 5~m \\
%\hline
-$w_{\Theta}$ & 1 \\
+$W_{\theta}$ & 1 \\
% [1ex] adds vertical space
%\hline
-$w_{U}$ & $|P^2|$
+$W_{U}$ & $|P|^2$
%inserts single line
\end{tabular}
\label{table3}
% is used to refer this table in the text
\end{table}
-Our protocol is declined into four versions: MuDiLCO-1, MuDiLCO-3, MuDiLCO-5,
+Our protocol is declined into four versions: MuDiLCO-1, MuDiLCO-3, MuDiLCO-5,
and MuDiLCO-7, corresponding respectively to $T=1,3,5,7$ ($T$ the number of
-rounds in one sensing period). In the following, the general case will be
-denoted by MuDiLCO-T. We are studied the impact of dividing the sensing feild on the performance of our MuDiLCO-T protocol with different network sizes using Divide and Conquer method, and we are found that as the number of subregions increase, the network lifetime increase and the MuDiLCO-T protocol become more powerful against the network disconnection.
-This subdivision should be stopped when there is no benefit from the optimization, therefore Our MuDiLCO-T protocol is distributed over 16 rather than 32 subregions because there is a balance between the benefit from the optimization and the execution time is needed to sove it. We compare MuDiLCO-T with two other methods. The first
-method, called DESK and proposed by \cite{ChinhVu} is a full distributed
-coverage algorithm. The second method, called GAF~\cite{xu2001geography},
-consists in dividing the region into fixed squares. During the decision phase,
-in each square, one sensor is then chosen to remain active during the sensing
-phase time.
-
-\subsection{Energy Model}
+rounds in one sensing period). In the following, we will make comparisons with
+two other methods. The first method, called DESK and proposed by \cite{ChinhVu},
+is a full distributed coverage algorithm. The second method, called
+GAF~\cite{xu2001geography}, consists in dividing the region into fixed squares.
+During the decision phase, in each square, one sensor is then chosen to remain
+active during the sensing phase time.
+
+Some preliminary experiments were performed to study the choice of the number of
+subregions which subdivides the sensing field, considering different network
+sizes. They show that as the number of subregions increases, so does the network
+lifetime. Moreover, it makes the MuDiLCO protocol more robust against random
+network disconnection due to node failures. However, too many subdivisions
+reduce the advantage of the optimization. In fact, there is a balance between
+the benefit from the optimization and the execution time needed to solve
+it. Therefore, we have set the number of subregions to 16 rather than 32.
+
+\subsection{Energy model}
We use an energy consumption model proposed by~\cite{ChinhVu} and based on
\cite{raghunathan2002energy} with slight modifications. The energy consumption
uses an Atmels AVR ATmega103L microcontroller~\cite{raghunathan2002energy}. The
typical architecture of a sensor is composed of four subsystems: the MCU
subsystem which is capable of computation, communication subsystem (radio) which
-is responsible for transmitting/receiving messages, sensing subsystem that
+is responsible for transmitting/receiving messages, the sensing subsystem that
collects data, and the power supply which powers the complete sensor node
\cite{raghunathan2002energy}. Each of the first three subsystems can be turned
on or off depending on the current status of the sensor. Energy consumption
(expressed in milliWatt per second) for the different status of the sensor is
-summarized in Table~\ref{table4}. The energy needed to send or receive a 1-bit
-packet is equal to $0.2575~mW$.
+summarized in Table~\ref{table4}.
\begin{table}[ht]
\caption{The Energy Consumption Model}
% is used to refer this table in the text
\end{table}
-For sake of simplicity we ignore the energy needed to turn on the radio, to
+For the sake of simplicity we ignore the energy needed to turn on the radio, to
start up the sensor node, to move from one status to another, etc.
%We also do not consider the need of collecting sensing data. PAS COMPRIS
-Thus, when a sensor becomes active (i.e., it already decides it's status), it
-can turn its radio off to save battery. MuDiLCO uses two types of packets for
+Thus, when a sensor becomes active (i.e., it has already chosen its status), it can
+turn its radio off to save battery. MuDiLCO uses two types of packets for
communication. The size of the INFO packet and Active-Sleep packet are 112~bits
and 24~bits respectively. The value of energy spent to send a 1-bit-content
message is obtained by using the equation in ~\cite{raghunathan2002energy} to
calculate the energy cost for transmitting messages and we propose the same
-value for receiving the packets.
+value for receiving the packets. The energy needed to send or receive a 1-bit
+packet is equal to 0.2575~mW.
The initial energy of each node is randomly set in the interval $[500;700]$. A
sensor node will not participate in the next round if its remaining energy is
(3600 seconds). According to the interval of initial energy, a sensor may be
alive during at most 20 rounds.
-
\subsection{Metrics}
To evaluate our approach we consider the following performance metrics:
\begin{enumerate}[i]
-\item {{\bf Coverage Ratio (CR)}:} the coverage ratio measures how much the area
+\item {{\bf Coverage Ratio (CR)}:} the coverage ratio measures how much of the area
of a sensor field is covered. In our case, the sensing field is represented as
- a connected grid of points and we use each grid point as a sample point for
- calculating the coverage. The coverage ratio can be calculated by:
+ a connected grid of points and we use each grid point as a sample point to
+ compute the coverage. The coverage ratio can be calculated by:
\begin{equation*}
\scriptsize
\mbox{CR}(\%) = \frac{\mbox{$n^t$}}{\mbox{$N$}} \times 100,
\end{equation*}
where $n^t$ is the number of covered grid points by the active sensors of all
-subregions during round $t$ in the current sensing phase and $N$ is total number
-of grid points in the sensing field of the network. In our simulation $N = 51 \times 26 = 1326$ grid points.
+subregions during round $t$ in the current sensing phase and $N$ is the total number
+of grid points in the sensing field of the network. In our simulations $N = 51
+\times 26 = 1326$ grid points.
%The accuracy of this method depends on the distance between grids. In our
%simulations, the sensing field has been divided into 50 by 25 grid points, which means
%there are $51 \times 26~ = ~ 1326$ points in total.
% Therefore, for our simulations, the error in the coverage calculation is less than ~ 1 $\% $.
\item{{\bf Number of Active Sensors Ratio (ASR)}:} it is important to have as
- few active nodes as possible in each round,in order to minimize the
+ few active nodes as possible in each round, in order to minimize the
communication overhead and maximize the network lifetime. The Active Sensors
Ratio is defined as follows:
\begin{equation*}
\end{equation*}
where $A_r^t$ is the number of active sensors in the subregion $r$ during round
$t$ in the current sensing phase, $|J|$ is the total number of sensors in the
-network, and $R$ is the total number of the subregions in the network.
+network, and $R$ is the total number of subregions in the network.
\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 $Lifetime_{50}$) as the amount of time during
+ $Lifetime_{95}$ (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 network is alive until all nodes have
been drained of their energy or the sensor network becomes
seen as the total energy consumed by the sensors during the $Lifetime_{95}$ or
$Lifetime_{50}$ divided by the number of rounds. EC can be computed as
follows:
- \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 \right) +
- \sum\limits_{t=1}^{T_L} \left( E^{a}_t+E^{s}_t \right)}{T_L},
-\end{equation*}
+ % New version with global loops on period
+ \begin{equation*}
+ \scriptsize
+ \mbox{EC} = \frac{\sum\limits_{m=1}^{M} \left[ \left( E^{\mbox{com}}_m+E^{\mbox{list}}_m+E^{\mbox{comp}}_m \right) +\sum\limits_{t=1}^{T_m} \left( E^{a}_t+E^{s}_t \right) \right]}{\sum\limits_{m=1}^{M} T_m},
+ \end{equation*}
+
+
+% Old version with loop on round outside the loop on period
+% \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 \right) +\sum\limits_{t=1}^{T_L} \left( E^{a}_t+E^{s}_t \right)}{T_L},
+% \end{equation*}
+
+% Ali version
%\begin{equation*}
%\scriptsize
%\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 $M_L$ and $T_L$ are respectively the number of periods and rounds during
-$Lifetime_{95}$ or $Lifetime_{50}$. 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 $m$.
-$E^{\scriptsize \mbox{list}}_m$, 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
-period. Finally, $E^a_t$ and $E^s_t$ indicate the energy consummed by the whole
-network in round $t$.
+% Old version -> where $M_L$ and $T_L$ are respectively the number of periods and rounds during
+%$Lifetime_{95}$ or $Lifetime_{50}$.
+% New version
+where $M$ is the number of periods and $T_m$ the number of rounds in a
+period~$m$, both during $Lifetime_{95}$ or $Lifetime_{50}$. The total energy
+consumed by the sensors (EC) comes through taking into consideration four main
+energy factors. The first one , denoted $E^{\scriptsize \mbox{com}}_m$,
+represents the energy consumption spent by all the nodes for wireless
+communications during period $m$. $E^{\scriptsize \mbox{list}}_m$, 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 period. Finally, $E^a_t$ and $E^s_t$
+indicate the energy consumed by the whole network in round $t$.
%\item {Network Lifetime:} we have defined the network lifetime as the time until all
%nodes have been drained of their energy or each sensor network monitoring an area has become disconnected.
\end{enumerate}
-%%%%%%%%%%%%%%%%%%%%%%%%VU JUSQU ICI**************************************************
-
-\section{Results and analysis}
+\subsection{Results and analysis}
-\subsection{Coverage ratio}
+\subsubsection{Coverage ratio}
Figure~\ref{fig3} shows the average coverage ratio for 150 deployed nodes. We
can notice that for the first thirty rounds both DESK and GAF provide a coverage
-which is a little bit better than the one of MuDiLCO-T. This is due to the fact
-that in comparison with MuDiLCO that uses optimization to put in SLEEP status
-redundant sensors, more sensor nodes remain active with DESK and GAF. As a
-consequence, when the number of rounds increases, a larger number of nodes
-failures can be observed in DESK and GAF, resulting in a faster decrease of the
-coverage ratio. Furthermore, our protocol allows to maintain a coverage ratio
-greater than 50\% for far more rounds. Overall, the proposed sensor activity
-scheduling based on optimization in MuDiLCO maintains higher coverage ratios of
-the area of interest for a larger number of rounds. It also means that MuDiLCO-T
-save more energy, with less dead nodes, at most for several rounds, and thus
-should extend the network lifetime.
+which is a little bit better than the one of MuDiLCO.
+%%RC : need to uniformize MuDiLCO or MuDiLCO-T?
+%%MS : MuDiLCO everywhere
+%%RC maybe increase the size of the figure for the reviewers, no?
+This is due to the fact that, in comparison with MuDiLCO which uses optimization
+to put in SLEEP status redundant sensors, more sensor nodes remain active with
+DESK and GAF. As a consequence, when the number of rounds increases, a larger
+number of node failures can be observed in DESK and GAF, resulting in a faster
+decrease of the coverage ratio. Furthermore, our protocol allows to maintain a
+coverage ratio greater than 50\% for far more rounds. Overall, the proposed
+sensor activity scheduling based on optimization in MuDiLCO maintains higher
+coverage ratios of the area of interest for a larger number of rounds. It also
+means that MuDiLCO saves more energy, with less dead nodes, at most for several
+rounds, and thus should extend the network lifetime.
-\begin{figure}[h!]
+\begin{figure}[ht!]
\centering
- \includegraphics[scale=0.5] {R1/CR.pdf}
+ \includegraphics[scale=0.5] {R/CR.pdf}
\caption{Average coverage ratio for 150 deployed nodes}
\label{fig3}
\end{figure}
-\subsection{Active sensors ratio}
+\subsubsection{Active sensors ratio}
It is crucial to have as few active nodes as possible in each round, in order to
minimize the communication overhead and maximize the network
lifetime. Figure~\ref{fig4} presents the active sensor ratio for 150 deployed
nodes all along the network lifetime. It appears that up to round thirteen, DESK
and GAF have respectively 37.6\% and 44.8\% of nodes in ACTIVE status, whereas
-MuDiLCO-T clearly outperforms them with only 24.8\% of active nodes. After the
-thirty fifth round, MuDiLCO-T exhibits larger number of active nodes, which
-agrees with the dual observation of higher level of coverage made previously.
+MuDiLCO clearly outperforms them with only 24.8\% of active nodes. After the
+thirty-fifth round, MuDiLCO exhibits larger numbers of active nodes, which agrees
+with the dual observation of higher level of coverage made previously.
Obviously, in that case DESK and GAF have less active nodes, since they have
-activated many nodes at the beginning. Anyway, MuDiLCO-T activates the available
+activated many nodes at the beginning. Anyway, MuDiLCO activates the available
nodes in a more efficient manner.
-\begin{figure}[h!]
+\begin{figure}[ht!]
\centering
-\includegraphics[scale=0.5]{R1/ASR.pdf}
+\includegraphics[scale=0.5]{R/ASR.pdf}
\caption{Active sensors ratio for 150 deployed nodes}
\label{fig4}
\end{figure}
-\subsection{Stopped simulation runs}
+\subsubsection{Stopped simulation runs}
%The results presented in this experiment, is to show the comparison of our MuDiLCO protocol with other two approaches from the point of view the stopped simulation runs per round. Figure~\ref{fig6} illustrates the percentage of stopped simulation
%runs per round for 150 deployed nodes.
Figure~\ref{fig6} reports the cumulative percentage of stopped simulations runs
-per round for 150 deployed nodes. This figure gives the breakpoint for each of
-the methods. DESK stops first, after around 45~rounds, because it consumes the
+per round for 150 deployed nodes. This figure gives the breakpoint for each method. DESK stops first, after approximately 45~rounds, because it consumes the
more energy by turning on a large number of redundant nodes during the sensing
-phase. GAF stops secondly for the same reason than DESK. MuDiLCO-T overcomes
+phase. GAF stops secondly for the same reason than DESK. MuDiLCO overcomes
DESK and GAF because the optimization process distributed on several subregions
leads to coverage preservation and so extends the network lifetime. Let us
emphasize that the simulation continues as long as a network in a subregion is
%%% The optimization effectively continues as long as a network in a subregion is still connected. A VOIR %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\begin{figure}[h!]
+\begin{figure}[ht!]
\centering
-\includegraphics[scale=0.5]{R1/SR.pdf}
+\includegraphics[scale=0.5]{R/SR.pdf}
\caption{Cumulative percentage of stopped simulation runs for 150 deployed nodes }
\label{fig6}
\end{figure}
-\subsection{Energy Consumption} \label{subsec:EC}
+\subsubsection{Energy consumption} \label{subsec:EC}
We measure the energy consumed by the sensors during the communication,
listening, computation, active, and sleep status for different network densities
\begin{figure}[h!]
\centering
\begin{tabular}{cl}
- \parbox{9.5cm}{\includegraphics[scale=0.5]{R1/EC95.pdf}} & (a) \\
+ \parbox{9.5cm}{\includegraphics[scale=0.5]{R/EC95.pdf}} & (a) \\
\verb+ + \\
- \parbox{9.5cm}{\includegraphics[scale=0.5]{R1/EC50.pdf}} & (b)
+ \parbox{9.5cm}{\includegraphics[scale=0.5]{R/EC50.pdf}} & (b)
\end{tabular}
\caption{Energy consumption for (a) $Lifetime_{95}$ and
(b) $Lifetime_{50}$}
\label{fig7}
\end{figure}
-The results show that MuDiLCO-T is the most competitive from the energy
+The results show that MuDiLCO is the most competitive from the energy
consumption point of view. The other approaches have a high energy consumption
due to activating a larger number of redundant nodes as well as the energy
consumed during the different status of the sensor node. Among the different
versions of our protocol, the MuDiLCO-7 one consumes more energy than the other
versions. This is easy to understand since the bigger the number of rounds and
-the number of sensors involved in the integer program, the larger the time
-computation to solve the optimization problem. To improve the performances of
+the number of sensors involved in the integer program are, the larger the time
+computation to solve the optimization problem is. To improve the performances of
MuDiLCO-7, we should increase the number of subregions in order to have less
sensors to consider in the integer program.
%In fact, a distributed optimization decision, which produces T rounds, on the subregions is greatly reduced the cost of communications and the time of listening as well as the energy needed for sensing phase and computation so thanks to the partitioning of the initial network into several independent subnetworks and producing T rounds for each subregion periodically.
-\subsection{Execution time}
+\subsubsection{Execution time}
We observe the impact of the network size and of the number of rounds on the
computation time. Figure~\ref{fig77} gives the average execution times in
-seconds (times needed to solve optimization problem) for different values of
-$T$. The original execution time is computed on a laptop DELL with Intel
-Core~i3~2370~M (2.4 GHz) processor (2 cores) and the MIPS (Million Instructions
-Per Second) rate equal to 35330. To be consistent with the use of a sensor node
-with Atmels AVR ATmega103L microcontroller (6 MHz) and a MIPS rate equal to 6 to
-run the optimization resolution, this time is multiplied by 2944.2 $\left(
+seconds (needed to solve optimization problem) for different values of $T$. The modeling language for Mathematical Programming (AMPL)~\cite{AMPL} is employed to generate the Mixed Integer Linear Program instance in a standard format, which is then read and solved by the optimization solver GLPK (GNU linear Programming Kit available in the public domain) \cite{glpk} through a Branch-and-Bound method. The
+original execution time is computed on a laptop DELL with Intel Core~i3~2370~M
+(2.4 GHz) processor (2 cores) and the MIPS (Million Instructions Per Second)
+rate equal to 35330. To be consistent with the use of a sensor node with Atmels
+AVR ATmega103L microcontroller (6 MHz) and a MIPS rate equal to 6 to run the
+optimization resolution, this time is multiplied by 2944.2 $\left(
\frac{35330}{2} \times \frac{1}{6} \right)$ and reported on Figure~\ref{fig77}
-for different network sizes.
+for different network sizes.
-\begin{figure}[h!]
+\begin{figure}[ht!]
\centering
-\includegraphics[scale=0.5]{R1/T.pdf}
+\includegraphics[scale=0.5]{R/T.pdf}
\caption{Execution Time (in seconds)}
\label{fig77}
\end{figure}
-As expected, the execution time increases with the number of rounds
-$T$ taken into account for scheduling of the sensing phase. The times
-obtained for $T=1,3$ or $5$ seems bearable, but for $T=7$ they become
-quickly unsuitable for a sensor node, especially when the sensor
-network size increases. Again, we can notice that if we want to
-schedule the nodes activities for a large number of rounds, we need to
-choose a relevant number of subregion in order to avoid a complicated
-and cumbersome optimization. On the one hand, a large value for $T$
-permits to reduce the energy-overhead due to the three pre-sensing
-phases, on the other hand a leader node may waste a considerable
-amount of energy to solve the optimization problem.
+As expected, the execution time increases with the number of rounds $T$ taken
+into account to schedule the sensing phase. The times obtained for $T=1,3$
+or $5$ seem bearable, but for $T=7$ they become quickly unsuitable for a sensor
+node, especially when the sensor network size increases. Again, we can notice
+that if we want to schedule the nodes activities for a large number of rounds,
+we need to choose a relevant number of subregions in order to avoid a complicated
+and cumbersome optimization. On the one hand, a large value for $T$ permits to
+reduce the energy-overhead due to the three pre-sensing phases, on the other
+hand a leader node may waste a considerable amount of energy to solve the
+optimization problem.
%While MuDiLCO-1, 3, and 5 solves the optimization process with suitable execution times to be used on wireless sensor network because it distributed on larger number of small subregions as well as it is used acceptable number of round(s) T. We think that in distributed fashion the solving of the optimization problem to produce T rounds in a subregion can be tackled by sensor nodes. Overall, to be able to deal with very large networks, a distributed method is clearly required.
-\subsection{Network Lifetime}
-
-The next two figures, Figures~\ref{fig8}(a) and \ref{fig8}(b),
-illustrate the network lifetime for different network sizes,
-respectively for $Lifetime_{95}$ and $Lifetime_{50}$. Both figures
-show that the network lifetime increases together with the number of
-sensor nodes, whatever the protocol, thanks to the node density which
-result in more and more redundant nodes that can be deactivated and
-thus save energy. Compared to the other approaches, our MuDiLCO-T
-protocol maximizes the lifetime of the network. In particular the
-gain in lifetime for a coverage over 95\% is greater than 38\% when
-switching from GAF to MuDiLCO-3. The slight decrease that can bee
-observed for MuDiLCO-7 in case of $Lifetime_{95}$ with large wireless
-sensor networks result from the difficulty of the optimization problem
-to be solved by the integer program. This point was already noticed
-in subsection \ref{subsec:EC} devoted to the energy consumption, since
-network lifetime and energy consumption are directly linked.
-
-\begin{figure}[h!]
+\subsubsection{Network lifetime}
+
+The next two figures, Figures~\ref{fig8}(a) and \ref{fig8}(b), illustrate the
+network lifetime for different network sizes, respectively for $Lifetime_{95}$
+and $Lifetime_{50}$. Both figures show that the network lifetime increases
+together with the number of sensor nodes, whatever the protocol, thanks to the
+node density which results in more and more redundant nodes that can be
+deactivated and thus save energy. Compared to the other approaches, our MuDiLCO
+protocol maximizes the lifetime of the network. In particular the gain in
+lifetime for a coverage over 95\% is greater than 38\% when switching from GAF
+to MuDiLCO-3. The slight decrease that can be observed for MuDiLCO-7 in case
+of $Lifetime_{95}$ with large wireless sensor networks results from the
+difficulty of the optimization problem to be solved by the integer program.
+This point was already noticed in subsection \ref{subsec:EC} devoted to the
+energy consumption, since network lifetime and energy consumption are directly
+linked.
+
+\begin{figure}[t!]
\centering
\begin{tabular}{cl}
- \parbox{9.5cm}{\includegraphics[scale=0.5]{R1/LT95.pdf}} & (a) \\
+ \parbox{9.5cm}{\includegraphics[scale=0.5]{R/LT95.pdf}} & (a) \\
\verb+ + \\
- \parbox{9.5cm}{\includegraphics[scale=0.5]{R1/LT50.pdf}} & (b)
+ \parbox{9.5cm}{\includegraphics[scale=0.5]{R/LT50.pdf}} & (b)
\end{tabular}
\caption{Network lifetime for (a) $Lifetime_{95}$ and
(b) $Lifetime_{50}$}
\label{fig8}
\end{figure}
-% By choosing the best suited nodes, for each round, by optimizing the coverage and lifetime of the network to cover the area of interest with a maximum number rounds and by letting the other nodes sleep in order to be used later in next rounds, our MuDiLCO-T protocol efficiently prolonges the network lifetime.
+% By choosing the best suited nodes, for each round, by optimizing the coverage and lifetime of the network to cover the area of interest with a maximum number rounds and by letting the other nodes sleep in order to be used later in next rounds, our MuDiLCO protocol efficiently prolonges the network lifetime.
-%In Figure~\ref{fig8}, Comparison shows that our MuDiLCO-T protocol, which are used distributed optimization on the subregions with the ability of producing T rounds, is the best one because it is robust to network disconnection during the network lifetime as well as it consume less energy in comparison with other approaches. It also means that distributing the protocol in each sensor node and 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.
+%In Figure~\ref{fig8}, Comparison shows that our MuDiLCO protocol, which are used distributed optimization on the subregions with the ability of producing T rounds, is the best one because it is robust to network disconnection during the network lifetime as well as it consume less energy in comparison with other approaches. It also means that distributing the protocol in each sensor node and 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.
%We see that our MuDiLCO-7 protocol results in execution times that quickly become unsuitable for a sensor network as well as the energy consumption seems to be huge because it used a larger number of rounds T during performing the optimization decision in the subregions, which is led to decrease the network lifetime. On the other side, our MuDiLCO-1, 3, and 5 protocol seems to be more efficient in comparison with other approaches because they are prolonged the lifetime of the network more than DESK and GAF.
-\section{Conclusion and Future Works}
+\section{Conclusion and future works}
\label{sec:conclusion}
-In this paper, we have addressed the problem of the coverage and the
-lifetime optimization in wireless sensor networks. This is a key issue
-as sensor nodes have limited resources in terms of memory, energy, and
-computational power. To cope with this problem, the field of sensing
-is divided into smaller subregions using the concept of
-divide-and-conquer method, and then we propose a protocol which
-optimizes coverage and lifetime performances in each subregion. Our
-protocol, called MuDiLCO (Multiperiod Distributed Lifetime Coverage
-Optimization) combines two efficient techniques: network leader
-election and sensor activity scheduling.
+We have addressed the problem of the coverage and of the lifetime optimization in
+wireless sensor networks. This is a key issue as sensor nodes have limited
+resources in terms of memory, energy, and computational power. To cope with this
+problem, the field of sensing is divided into smaller subregions using the
+concept of divide-and-conquer method, and then we propose a protocol which
+optimizes coverage and lifetime performances in each subregion. Our protocol,
+called MuDiLCO (Multiround Distributed Lifetime Coverage Optimization) combines
+two efficient techniques: network leader election and sensor activity
+scheduling.
%, where the challenges
%include how to select the most efficient leader in each subregion and
%the best cover sets %of active nodes that will optimize the network lifetime
%while taking the responsibility of covering the corresponding
%subregion using more than one cover set during the sensing phase.
-The activity scheduling in each subregion works in periods, where each
-period consists of four phases: (i) Information Exchange, (ii) Leader
-Election, (iii) Decision Phase to plan the activity of the sensors
-over $T$ rounds (iv) Sensing Phase itself divided into T rounds.
-
-Simulations results show the relevance of the proposed protocol in
-terms of lifetime, coverage ratio, active sensors ratio, energy
-consumption, execution time. Indeed, when dealing with large wireless
-sensor networks, a distributed approach like the one we propose allows
-to reduce the difficulty of a single global optimization problem by
-partitioning it in many smaller problems, one per subregion, that can
-be solved more easily. Nevertheless, results also show that it is not
-possible to plan the activity of sensors over too many rounds, because
-the resulting optimization problem leads to too high resolution time
-and thus to an excessive energy consumption.
+The activity scheduling in each subregion works in periods, where each period
+consists of four phases: (i) Information Exchange, (ii) Leader Election, (iii)
+Decision Phase to plan the activity of the sensors over $T$ rounds, (iv) Sensing
+Phase itself divided into $T$ rounds.
+
+Simulations results show the relevance of the proposed protocol in terms of
+lifetime, coverage ratio, active sensors ratio, energy consumption, execution
+time. Indeed, when dealing with large wireless sensor networks, a distributed
+approach, like the one we propose, allows to reduce the difficulty of a single
+global optimization problem by partitioning it in many smaller problems, one per
+subregion, that can be solved more easily. Nevertheless, results also show that
+it is not possible to plan the activity of sensors over too many rounds, because
+the resulting optimization problem leads to too high resolution times and thus to
+an excessive energy consumption.
%In future work, we plan to study and propose adjustable sensing range coverage optimization protocol, which computes all active sensor schedules in one time, by using
%optimization methods. This protocol can prolong the network lifetime by minimizing the number of the active sensor nodes near the borders by optimizing the sensing range of sensor nodes.
% use section* for acknowledgement
\section*{Acknowledgment}
-As a Ph.D. student, Ali Kadhum IDREES would like to gratefully acknowledge the University of Babylon - IRAQ for the financial support and in the same time would like to acknowledge Campus France (The French national agency for the promotion of higher education, international student services, and international mobility) and University of Franche-Comt\'e - FRANCE for all the support in FRANCE.
+This work is partially funded by the Labex ACTION program (contract ANR-11-LABX-01-01).
+As a Ph.D. student, Ali Kadhum IDREES would like to gratefully acknowledge the
+University of Babylon - Iraq for the financial support, Campus France (The
+French national agency for the promotion of higher education, international
+student services, and international mobility).%, and the University ofFranche-Comt\'e - France for all the support in France.
+
+
+
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+\bibliography{article}
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