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
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 take the decision needs
+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.
%For instance, the proposed work in ~\cite{cardei2005energy, berman04}
In~\cite{yang2014maximum}, the authors have considered a linear programming
-approach for selecting the minimum number of working sensor nodes, in order to
-preserve a maximum coverage and extend lifetime of the network. Cheng et
+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 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}.
+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}
%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} focuse on coverage-aware,
+The works presented in \cite{Bang, Zhixin, Zhang} focus on coverage-aware,
distributed energy-efficient, and distributed clustering methods respectively,
-which aim to extend 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.
+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.
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
+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
%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} focuse on coverage-aware,
+The works presented in \cite{Bang, Zhixin, Zhang} focus on coverage-aware,
distributed energy-efficient, and distributed clustering methods respectively,
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
\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 participating to the monitoring of the area;
+\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}
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
+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
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.
+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
\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
+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}
\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);
+ 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 the round $t$;
+ 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 the round $t$ (1 if not covered and 0 if
+ point $p$ is being covered during round $t$ (1 if not covered and 0 if
covered).
\end{itemize}
active during the sensing phase time.
Some preliminary experiments were performed to study the choice of the number of
-subregions which subdivide the sensing field, considering different network
+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 much subdivisions
-reduces the advantage of the optimization. In fact, there is a balance between
+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.
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}.
+summarized in Table~\ref{table4}.
\begin{table}[ht]
\caption{The Energy Consumption Model}
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 its status), it can
+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
\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
+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
\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
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 $E^{\scriptsize \mbox{com}}_m$,
-represent the energy consumption spent by all the nodes for wireless
+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 $m$.
%%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 that uses optimization
+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
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 clearly outperforms them with only 24.8\% of active nodes. After the
-thirty fifth round, MuDiLCO exhibits larger number of active nodes, which agrees
+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 activates the available
%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 overcomes
DESK and GAF because the optimization process distributed on several subregions
\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
+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
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
+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 bee observed for MuDiLCO-7 in case
+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
\section{Conclusion and future works}
\label{sec:conclusion}
-We have addressed the problem of the coverage and the lifetime optimization in
+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
%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
+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
+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
+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
