%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}}
-}
-
+\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 Multiround 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 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. \textcolor{green}{The decision process is carried out by a leader node, which
-solves an optimization problem to produce the best representative sets to be used
-during the rounds of the sensing phase. The optimization problem formulated as an integer program is solved to optimality through a branch-and-Bound method for small instances. For larger instances, the best feasible solution found by the solver after a given time limit threshold is considered. }
+divided into subregions and then the MuDiLCO protocol is distributed on the
+sensor nodes in each subregion. The proposed MuDiLCO protocol works in periods
+during which sets of sensor nodes are scheduled, with one set for each round of
+a period, to remain active during the sensing phase and thus ensure coverage so
+as to maximize the WSN lifetime. \textcolor{blue}{The decision process is
+ carried out by a leader node, which solves an optimization problem to produce
+ the best representative sets to be used during the rounds of the sensing
+ phase. The optimization problem formulated as an integer program is solved to
+ optimality through a Branch-and-Bound method for small instances. For larger
+ instances, the best feasible solution found by the solver after a given time
+ limit threshold is considered.}
%The decision process is carried out by a leader node, which
%solves an integer program to produce the best representative sets to be used
%during the rounds of the sensing phase.
%\textcolor{red}{The integer program is solved by either GLPK solver or Genetic Algorithm (GA)}.
-Compared with some existing protocols,
-simulation results based on multiple criteria (energy consumption, coverage
-ratio, and so on) show that the proposed protocol can prolong efficiently the
-network lifetime and improve the coverage performance.
-
+Compared with some existing protocols, simulation results based on multiple
+criteria (energy consumption, coverage ratio, and so on) show that the proposed
+protocol can prolong efficiently the network lifetime and improve the coverage
+performance.
\end{abstract}
\begin{keyword}
Wireless Sensor Networks, Area Coverage, Network Lifetime,
Optimization, Scheduling, Distributed Computation.
-
\end{keyword}
\end{frontmatter}
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
-demonstrate the usefulness of the proposed approach. Finally, we give
+demonstrate the usefulness of the proposed approach. Finally, we give
concluding remarks and some suggestions for future works in
Section~\ref{sec:conclusion}.
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
+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
+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. \textcolor{green} {Exact or heuristics approaches are designed to provide cover sets.
- %(Moreover, centralized approaches usually
+information from all the sensor nodes. \textcolor{blue} {Exact or heuristics
+ approaches are designed to provide cover sets.
+%(Moreover, centralized approaches usually
%suffer from the scalability problem, making them less competitive as the network
%size increases.)
-Contrary to exact methods, heuristic methods can handle very large and centralized problems. They are proposed to reduce computational overhead such as energy consumption, delay and generally increase in
-the network lifetime. }
+Contrary to exact methods, heuristic ones can handle very large and centralized
+problems. They are proposed to reduce computational overhead such as energy
+consumption, delay, and generally allow to increase the network lifetime.}
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
+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
+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
+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
+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
+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{gentili2013,castano2013column,rossi2012exact,deschinkel2012column}.
-\textcolor{green}{In~\cite{gentili2013}, authors highlight the trade-off between the network lifetime and the coverage percentage. They show that network lifetime can be hugely improved by decreasing the coverage ratio. }
+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{gentili2013,castano2013column,rossi2012exact,deschinkel2012column}.
+\textcolor{blue}{In~\cite{gentili2013}, authors highlight the trade-off between
+ the network lifetime and the coverage percentage. They show that network
+ lifetime can be hugely improved by decreasing the coverage ratio.}
\subsection{Distributed approaches}
%{\bf Distributed approaches}
\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
+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
+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
+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. \textcolor{green}{The idea is to take advantage of the pre-sensing phase
- to plan the sensor's activity for several rounds instead of one, thus saving energy. In addition, when the optimization problem becomes more complex, its resolution is stopped after a given time threshold}.
+a single round optimization in our previous work. \textcolor{blue}{The idea is
+ to take advantage of the pre-sensing phase to plan the sensor's activity for
+ several rounds instead of one, thus saving energy. In addition, when the
+ optimization problem becomes more complex, its resolution is stopped after a
+ given time threshold}.
\iffalse
%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.
+\indent Instead of working with the coverage area, we consider for each sensor a
+set of points called primary points~\cite{idrees2014coverage}. We assume that
+the sensing disk defined by a sensor is covered if all the primary points of
+this sensor are covered. By knowing the position of wireless sensor node
+(centered at the the position $\left(p_x,p_y\right)$) and it's sensing range
+$R_s$, we define up to 25 primary points $X_1$ to $X_{25}$ as decribed on
+Figure~\ref{fig1}. The optimal number of primary points is investigated in
+subsection~\ref{ch4:sec:04:06}.
-\indent Instead of working with the coverage area, we consider for each sensor a set of points called primary points~\cite{idrees2014coverage}. We assume that the sensing disk defined by a sensor is covered if all the primary points of this sensor are covered. By knowing the position (point center: ($p_x,p_y$)) of a wireless sensor node and it's sensing range $R_s$, we define up to 25 primary points $X_1$ to $X_{25}$ as decribed on Figure~\ref{fig1}. The coordinates of the primary points are the following :\\
+The coordinates of the primary points are defined as follows:\\
%$(p_x,p_y)$ = point center of wireless sensor node\\
$X_1=(p_x,p_y)$ \\
$X_2=( p_x + R_s * (1), p_y + R_s * (0) )$\\
$X_{15}=( p_x + R_s * (\frac{-\sqrt{3}}{2}), p_y + R_s * (\frac{1}{2})) $\\
$X_{16}=( p_x + R_s * (\frac{\sqrt{3}}{2}), p_y + R_s * (\frac{- 1}{2})) $\\
$X_{17}=( p_x + R_s * (\frac{-\sqrt{3}}{2}), p_y + R_s * (\frac{- 1}{2})) $\\
-$X_{18}=( p_x + R_s * (\frac{\sqrt{3}}{2}), p_y + R_s * (0) $\\
-$X_{19}=( p_x + R_s * (\frac{-\sqrt{3}}{2}), p_y + R_s * (0) $\\
+$X_{18}=( p_x + R_s * (\frac{\sqrt{3}}{2}), p_y + R_s * (0)) $\\
+$X_{19}=( p_x + R_s * (\frac{-\sqrt{3}}{2}), p_y + R_s * (0)) $\\
$X_{20}=( p_x + R_s * (0), p_y + R_s * (\frac{1}{2})) $\\
$X_{21}=( p_x + R_s * (0), p_y + R_s * (-\frac{1}{2})) $\\
$X_{22}=( p_x + R_s * (\frac{1}{2}), p_y + R_s * (\frac{\sqrt{3}}{2})) $\\
$X_{25}=( p_x + R_s * (\frac{1}{2}), p_y + R_s * (\frac{-\sqrt{3}}{2})) $.
-
-\begin{figure} %[h!]
-\centering
- \begin{multicols}{2}
-\centering
-\includegraphics[scale=0.28]{fig21.pdf}\\~ (a)
-\includegraphics[scale=0.28]{principles13.pdf}\\~(c)
-\hfill \hfill
-\includegraphics[scale=0.28]{fig25.pdf}\\~(e)
-\includegraphics[scale=0.28]{fig22.pdf}\\~(b)
-\hfill \hfill
-\includegraphics[scale=0.28]{fig24.pdf}\\~(d)
-\includegraphics[scale=0.28]{fig26.pdf}\\~(f)
-\end{multicols}
-\caption{Wireless Sensor Node represented by (a) 5, (b) 9, (c) 13, (d) 17, (e) 21 and (f) 25 primary points respectively}
-\label{fig1}
-\end{figure}
+%\begin{figure} %[h!]
+%\centering
+% \begin{multicols}{2}
+%\centering
+%\includegraphics[scale=0.28]{fig21.pdf}\\~ (a)
+%\includegraphics[scale=0.28]{principles13.pdf}\\~(c)
+%\hfill \hfill
+%\includegraphics[scale=0.28]{fig25.pdf}\\~(e)
+%\includegraphics[scale=0.28]{fig22.pdf}\\~(b)
+%\hfill \hfill
+%\includegraphics[scale=0.28]{fig24.pdf}\\~(d)
+%\includegraphics[scale=0.28]{fig26.pdf}\\~(f)
+%\end{multicols}
+%\caption{Wireless Sensor Node represented by (a) 5, (b) 9, (c) 13, (d) 17, (e) 21 and (f) 25 primary points respectively}
+%\label{fig1}
+%\end{figure}
-
-
-
-
+\begin{figure}[h]
+ \centering
+ \includegraphics[scale=0.375]{fig26.pdf}
+ \label{fig1}
+ \caption{Wireless sensor node represented by up to 25~primary points}
+\end{figure}
%By knowing the position (point center: ($p_x,p_y$)) of a wireless
%sensor node and its $R_s$, we calculate the primary points directly
\subsection{Background idea}
%%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.
+%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.
+
+\textcolor{blue}{The WSN area of interest is, in a first step, divided into regular homogeneous
+subregions using a divide-and-conquer algorithm. In a second step our protocol
+will be executed in a distributed way in each subregion simultaneously to
+schedule nodes' activities for one sensing period. Sensor nodes are assumed to
+be deployed almost uniformly over the region. The regular subdivision is made
+such that the number of hops between any pairs of sensors inside a subregion is
+less than or equal to 3.}
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
-\textcolor{green} {of equal duration} and for each round a set of sensors (a cover set) is responsible for the sensing
+\textcolor{blue} {of equal duration} 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.
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. \textcolor{green}{The duration of the rounds are predefined parameters. Round duration should be long enough to hide the system control overhead and short enough to minimize the negative effects in case of node failure.}
+period starts. \textcolor{blue}{The duration of the rounds are predefined parameters. Round duration should be long enough to hide the system control overhead and short enough to minimize the negative effects in case of node failure.}
%%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
\subsection{Decision phase}
-Each WSNL will \textcolor{green}{ solve an integer program to select which cover sets will be
+Each WSNL will \textcolor{blue}{ 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. $T$ cover sets will be produced, 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. }
\end{equation}
\begin{equation}
- \sum_{t=1}^{T} X_{t,j} \leq \floor*{RE_{j}/E_{R}} \hspace{6 mm} \forall j \in J, t = 1,\dots,T
+ \sum_{t=1}^{T} X_{t,j} \leq \floor*{RE_{j}/E_{R}} \hspace{10 mm}\forall j \in J\hspace{6 mm}
\label{eq144}
\end{equation}
%% 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 $W_{\theta}$.
+
+\textcolor{blue}{The size of the problem depends on the number of variables and constraints. The number of variables is linked to the number of alive sensors $A \subset J$, the number of rounds $T$, and the number of primary points $P$. Thus the integer program contains $A*T$ variables of type $X_{t,j}$, $P*T$ overcoverage variables and $P*T$ undercoverage variables. The number of constraints is equal to $P*T$ (for constraints (\ref{eq16})) $+$ $A$ (for constraints (\ref{eq144})).}
%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.
\label{table3}
% is used to refer this table in the text
\end{table}
-
-\textcolor{red}{Our first protocol based GLPK optimization solver 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).
-The second protocol based based GLPK optimization solver with time limit is declined into four versions: TL-MuDiLCO-1, TL-MuDiLCO-3, TL-MuDiLCO-5, and TL-MuDiLCO-7. Table \ref{tl} shows time limit values for TL-MuDiLCO protocol versions. After extensive experiments, we chose the values that explained in Table \ref{tl} because they gave the best results. In Table \ref{tl}, "NO" refers to apply the GLPK solver without time limit because we did not find improvement on the results of MuDiLCO protocol with the time limit}.
+
+\textcolor{blue}{The MuDilLCO 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). Since the time resolution may be prohibitif when the size of the problem increases, a time limit treshold has been fixed to solve large instances. In these cases, the solver returns the best solution found, which is not necessary the optimal solution.
+ Table \ref{tl} shows time limit values. These time limit treshold have been set empirically. The basic idea consists in considering the average execution time to solve the integer programs to optimality, then by dividing this average time by three to set the threshold value. After that, this treshold value is increased if necessary such that the solver is able to deliver a feasible solution within the time limit. In fact, selecting the optimal values for the time limits will be investigated in future. In Table \ref{tl}, "NO" indicates that the problem has been solved to optimality without time limit. }.
\begin{table}[ht]
-\caption{Time limit values for TL-MuDiLCO protocol versions }
+\caption{Time limit values for MuDiLCO protocol versions }
\centering
\begin{tabular}{|c|c|c|c|c|}
\hline
- WSN size & TL-MuDiLCO-1 & TL-MuDiLCO-3 & TL-MuDiLCO-5 & TL-MuDiLCO-7 \\ [0.5ex]
+ WSN size & MuDiLCO-1 & MuDiLCO-3 & MuDiLCO-5 & MuDiLCO-7 \\ [0.5ex]
\hline
50 & NO & NO & NO & NO \\
\hline
100 & NO & NO & NO & NO \\
\hline
-150 & NO & 0.006 & NO & 0.03 \\
+150 & NO & NO & NO & 0.03 \\
\hline
-200 & 0.0035 & 0.0094 & 0.020 & 0.06 \\
+200 & NO & NO & NO & 0.06 \\
\hline
- 250 & 0.0055 & 0.013 & 0.03 & 0.08 \\
+ 250 & NO & NO & NO & 0.08 \\
\hline
\end{tabular}
\end{enumerate}
-\subsection{Performance Analysis for Different Number of Primary Points}
+\subsection{Performance analysis for different number of primary points}
\label{ch4:sec:04:06}
In this section, we study the performance of MuDiLCO-1 approach for different numbers of primary points. The objective of this comparison is to select the suitable primary point model to be used by a MuDiLCO protocol. In this comparison, MuDiLCO-1 protocol is used with five models, which are called Model-5 (it uses 5 primary points), Model-9, Model-13, Model-17, and Model-21.
%\begin{enumerate}[i)]
%\item {{\bf Coverage Ratio}}
-\subsubsection{Coverage Ratio}
+\subsubsection{Coverage ratio}
Figure~\ref{Figures/ch4/R2/CR} shows the average coverage ratio for 150 deployed nodes.
\parskip 0pt
%\item {{\bf Network Lifetime}}
-\subsubsection{Network Lifetime}
+\subsubsection{Network lifetime}
Finally, we study the effect of increasing the primary points on the lifetime of the network.
%In Figure~\ref{Figures/ch4/R2/LT95} and in Figure~\ref{Figures/ch4/R2/LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes.
\label{Figures/ch4/R2/LT}
\end{figure}
-Comparison shows that Model-5, which uses less number of primary points, is the best one because it is less energy consuming during the network lifetime. It is also the better one from the point of view of coverage ratio. Our proposed Model-5 efficiently prolongs the network lifetime with a good coverage ratio in comparison with other models. Therefore, we have chosen Model-5 for all the experiments presented thereafter.
+Comparison shows that Model-5, which uses less number of primary points, is the best one because it is less energy consuming during the network lifetime. It is also the better one from the point of view of coverage ratio. Our proposed Model-5 efficiently prolongs the network lifetime with a good coverage ratio in comparison with other models. Therefore, we have chosen the model with five primary points for all the experiments presented thereafter.
%\end{enumerate}
-
\subsection{Results and analysis}
\subsubsection{Coverage ratio}
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 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.
+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 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.
%\textcolor{red}{As shown in Figure~\ref{fig7}, GA-MuDiLCO consumes less energy than both DESK and GAF, but a little bit higher than MuDiLCO because it provides a near optimal solution by activating a larger number of nodes during the sensing phase. GA-MuDiLCO consumes less energy in comparison with MuDiLCO-7 version, especially for the dense networks. However, MuDiLCO protocol and GA-MuDiLCO protocol are 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.}
