\DeclareGraphicsRule{.ps}{pdf}{.pdf}{`ps2pdf -dEPSCrop -dNOSAFER #1 \noexpand\OutputFile}
\begin{document}
-\title{Lifetime Coverage Optimization Protocol \\
- in Wireless Sensor Networks} %LiCO Protocol
+%\title{Lifetime Coverage Optimization Protocol \\
+% in Wireless Sensor Networks}
+\title{Perimeter-based Coverage Optimization Protocol \\
+ to Improve Lifetime in Wireless Sensor Networks}
\author{Ali Kadhum Idrees,~\IEEEmembership{}
Karine Deschinkel,~\IEEEmembership{}
as long as possible. Among known available approaches that can be used to
improve power management, lifetime coverage optimization provides activity
scheduling which ensures sensing coverage while minimizing the energy cost. In
-this paper, we propose a such approach called Lifetime Coverage Optimization
+this paper, we propose such an approach called Lifetime Coverage Optimization
protocol (LiCO). It is a hybrid of centralized and distributed methods: the
region of interest is first subdivided into subregions and our protocol is then
distributed among sensor nodes in each subregion. A sensor node which runs LiCO
election, optimization decision, and sensing. More precisely, the scheduling of
nodes' activities (sleep/wake up duty cycles) is achieved in each subregion by a
leader selected after cooperation between nodes within the same subregion. The
-novelty of approach lies essentially in the formulation of a new mathematical
-optimization model based on perimeter coverage level to schedule sensors'
-activities. Extensive simulation experiments have been performed using OMNeT++,
-the discrete event simulator, to demonstrate that LiCO is capable to offer
-longer lifetime coverage for WSNs in comparison with some other protocols.
+novelty of our approach lies essentially in the formulation of a new
+mathematical optimization model based on perimeter coverage level to schedule
+sensors' activities. Extensive simulation experiments have been performed using
+OMNeT++, the discrete event simulator, to demonstrate that LiCO is capable to
+offer longer lifetime coverage for WSNs in comparison with some other protocols.
\end{abstract}
% Note that keywords are not normally used for peerreview papers.
can send the data it collects in its environment, thanks to its sensor, to the
user by means of sink nodes. The features of a WSN made it suitable for a wide
range of application in areas such as business, environment, health, industry,
-military, and son~\cite{yick2008wireless}. Typically, a sensor node contains
+military, and so on~\cite{yick2008wireless}. Typically, a sensor node contains
three main components~\cite{anastasi2009energy}: a sensing unit able to measure
physical, chemical, or biological phenomena observed in the environment; a
processing unit which will process and store the collected measurements; a radio
\begin{enumerate}
\item We devise a framework to schedule nodes to be activated alternatively such
that the network lifetime is prolonged while ensuring that a certain level of
- coverage is preserved. A key idea in our framework is to exploit spatial an
+ coverage is preserved. A key idea in our framework is to exploit spatial and
temporal subdivision. On the one hand the area of interest if divided into
several smaller subregions and on the other hand the time line is divided into
periods of equal length. In each subregion the sensor nodes will cooperatively
Various centralized and distributed approaches, or even a mixing of these two
concepts, have been proposed to extend the network lifetime. In distributed
-algorithms~\cite{yangnovel,ChinhVu,qu2013distributed} each sensors decides of
-its own activity scheduling after an information exchange with its neighbors.
-The main interest of a such approach is to avoid long range communications and
-thus to reduce the energy dedicated to the communications. Unfortunately, since
-each node has only information on its immediate neighbors (usually the one-hop
-ones) it may take a bad decision leading to a global suboptimal solution.
-Conversely, centralized
+algorithms~\cite{yangnovel,ChinhVu,qu2013distributed} each sensor decides of its
+own activity scheduling after an information exchange with its neighbors. The
+main interest of a such approach is to avoid long range communications and thus
+to reduce the energy dedicated to the communications. Unfortunately, since each
+node has only information on its immediate neighbors (usually the one-hop ones)
+it may take a bad decision leading to a global suboptimal solution. Conversely,
+centralized
algorithms~\cite{cardei2005improving,zorbas2010solving,pujari2011high} always
provide nearly or close to optimal solution since the algorithm has a global
view of the whole network. The disadvantage of a centralized method is obviously
Tseng in~\cite{huang2005coverage}. It can be expressed as follows: a sensor is
said to be perimeter covered if all the points on its perimeter are covered by
at least one sensor other than itself. They proved that a network area is
-$k$-covered if and only if each sensor in the network is $k$-perimeter-covered.
+$k$-covered if and only if each sensor in the network is $k$-perimeter-covered (perimeter covered by at least $k$ sensors).
%According to this model, we named the intersections among the sensor nodes in the sensing field as intersection points. Instead of working with the coverage area, we consider for each sensor a set of intersection points which are determined by using perimeter-coverage model.
Figure~\ref{pcm2sensors}(a) shows the coverage of sensor node~$0$. On this
figure, we can see that sensor~$0$ has nine neighbors and we have reported on
Figure~\ref{pcm2sensors}(b) describes the geometric information used to find the
locations of the left and right points of an arc on the perimeter of a sensor
-node~$u$ covered by a sensor node~$v$. Node~$s$ is supposed to be located on the
+node~$u$ covered by a sensor node~$v$. Node~$v$ is supposed to be located on the
west side of sensor~$u$, with the following respective coordinates in the
sensing area~: $(v_x,v_y)$ and $(u_x,u_y)$. From the previous coordinates we can
compute the euclidean distance between nodes~$u$ and $v$: $Dist(u,v)=\sqrt{\vert
from first intersection point after point~zero, and the maximum level of
coverage is determined for each interval defined by two successive points. The
maximum level of coverage is equal to the number of overlapping arcs. For
-example, between~$5L$ and~$6L$ the maximum level of coverage is equal to $3$
+example,
+between~$5L$ and~$6L$ the maximum level of coverage is equal to $3$
(the value is highlighted in yellow at the bottom of figure~\ref{expcm}), which
means that at most 2~neighbors can cover the perimeter in addition to node $0$.
Table~\ref{my-label} summarizes for each coverage interval the maximum level of
\begin{figure*}[ht!]
\centering
-\includegraphics[width=137.5mm]{expcm.pdf}
+\includegraphics[width=137.5mm]{expcm2.jpg}
\caption{Maximum coverage levels for perimeter of sensor node $0$.}
\label{expcm}
\end{figure*}
\caption{Coverage intervals and contributing sensors for sensor node 0.}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|}
\hline
-\begin{tabular}[c]{@{}c@{}}Left \\ point \\ angle~$\alpha$ \end{tabular} & \begin{tabular}[c]{@{}c@{}}Interval \\ left \\ point\end{tabular} & \begin{tabular}[c]{@{}c@{}}Interval \\ right \\ point\end{tabular} & \begin{tabular}[c]{@{}c@{}}Maximum \\ coverage\\ level\end{tabular} & \multicolumn{5}{c|}{\begin{tabular}[c]{@{}c@{}}Set of sensors\\ involved \\ in interval coverage\end{tabular}} \\ \hline
+\begin{tabular}[c]{@{}c@{}}Left \\ point \\ angle~$\alpha$ \end{tabular} & \begin{tabular}[c]{@{}c@{}}Interval \\ left \\ point\end{tabular} & \begin{tabular}[c]{@{}c@{}}Interval \\ right \\ point\end{tabular} & \begin{tabular}[c]{@{}c@{}}Maximum \\ coverage\\ level\end{tabular} & \multicolumn{5}{c|}{\begin{tabular}[c]{@{}c@{}}Set of sensors\\ involved \\ in coverage interval\end{tabular}} \\ \hline
0.0291 & 1L & 2L & 4 & 0 & 1 & 3 & 4 & \\ \hline
0.104 & 2L & 3R & 5 & 0 & 1 & 3 & 4 & 2 \\ \hline
0.3168 & 3R & 4R & 4 & 0 & 1 & 4 & 2 & \\ \hline
\begin{figure}[t!]
\centering
\includegraphics[width=80mm]{Model.pdf}
-\caption{LiCO protocol}
+\caption{LiCO protocol.}
\label{fig2}
\end{figure}
determine the activities scheduling;
\item ACTIVE: node is sensing;
\item SLEEP: node is turned off;
-\item COMMUNICATION: transmits or recevives packets.
+\item COMMUNICATION: transmits or receives packets.
\end{itemize}
%\end{enumerate}
%Below, we describe each phase in more details.
In this algorithm, K.CurrentSize and K.PreviousSize refer to the current size
and the previous size of the subnetwork in the subregion respectively. That
-means the number of sensor nodes which are still alive. Initially, the sensor
+means the number of sensor nodes which are still alive. Initially, the sensor
node checks its remaining energy $RE_k$, which must be greater than a threshold
-$E_{th}$ in order to participate in the current period. Each sensor node
+$E_{th}$ in order to participate in the current period. Each sensor node
determines its position and its subregion using an embedded GPS or a location
discovery algorithm. After that, all the sensors collect position coordinates,
-remaining energy, sensor node ID, and the number of its one-hop live neighbors
+remaining energy, sensor node ID, and the number of their one-hop live neighbors
during the information exchange. The sensors inside a same region cooperate to
elect a leader. The selection criteria for the leader, in order of priority,
are: larger number of neighbors, larger remaining energy, and then in case of
lifetime, the objective is to activate a minimal number of sensors in each
period to ensure the desired coverage level. As the number of alive sensors
decreases, it becomes impossible to reach the desired level of coverage for all
-coverage intervals. Therefore we uses variables $M^j_i$ and $V^j_i$ as a measure
+coverage intervals. Therefore we use variables $M^j_i$ and $V^j_i$ as a measure
of the deviation between the desired number of active sensors in a coverage
interval and the effective number. And we try to minimize these deviations,
first to force the activation of a minimal number of sensors to ensure the
The values of $\alpha^j_i$ and $\beta^j_i$ have been chosen to ensure a good
network coverage and a longer WSN lifetime. We have given a higher priority for
the undercoverage (by setting the $\alpha^j_i$ with a larger value than
-$\beta^j_i$) so as to prevent the non-coverage for the interval i of the sensor
-j. On the other hand, we have given a little bit lower value for $\beta^j_i$ so
-as to minimize the number of active sensor nodes which contribute in covering
-the interval.
+$\beta^j_i$) so as to prevent the non-coverage for the interval~$i$ of the
+sensor~$j$. On the other hand, we have given a little bit lower value for
+$\beta^j_i$ so as to minimize the number of active sensor nodes which contribute
+in covering the interval.
We introduce the following performance metrics to evaluate the efficiency of our
approach.
$Lifetime_{50}$ denote, respectively, the amount of time during which is
guaranteed a level of coverage greater than $95\%$ and $50\%$. The WSN can
fulfill the expected monitoring task until all its nodes have depleted their
- energy or if the network is not more connected. This last condition is crucial
+ energy or if the network is no more connected. This last condition is crucial
because without network connectivity a sensor may not be able to send to a
base station an event it has sensed.
\item {\bf Coverage Ratio (CR)} : it measures how well the WSN is able to
follows:
\begin{equation*}
\scriptsize
- \mbox{ASR}(\%) = \frac{\sum\limits_{r=1}^R \mbox{$|A_r|$}}{\mbox{$|S|$}} \times 100
+ \mbox{ASR}(\%) = \frac{\sum\limits_{r=1}^R \mbox{$|A_r^p|$}}{\mbox{$|S|$}} \times 100
\end{equation*}
where $|A_r^p|$ is the number of active sensors in the subregion $r$ in the
current sensing period~$p$, $|S|$ is the number of sensors in the network, and
protocols are distinguished from one another by the formulation of the integer
program providing the set of sensors which have to be activated in each sensing
phase. DiLCO protocol tries to satisfy the coverage of a set of primary points,
-whereas LICO protocol objectif is to reach a desired level of coverage for each
+whereas LiCO protocol objective is to reach a desired level of coverage for each
sensor perimeter. In our experimentations, we chose a level of coverage equal to
one ($l=1$).
We study the effect of the energy consumed by the WSN during the communication,
computation, listening, active, and sleep status for different network densities
-and compare it for the fours approaches. Figures~\ref{fig3EC}(a) and (b)
+and compare it for the four approaches. Figures~\ref{fig3EC}(a) and (b)
illustrate the energy consumption for different network sizes and for
$Lifetime95$ and $Lifetime50$. The results show that our LiCO protocol is the
most competitive from the energy consumption point of view. As shown in both
\subsubsection{\bf Network Lifetime}
We observe the superiority of LiCO and DiLCO protocols in comparison against the
-two other approaches in prolonging the network WSN. In figures~\ref{fig3LT}(a)
-and (b), $Lifetime95$ and $Lifetime50$ are shown for different network sizes.
-As highlighted by these figures, the lifetime increases with the size of the
-network, and it is clearly the larger for DiLCO and LiCO protocols. For
-instance, for a network of 300 sensors and coverage ratio greater than 50\%, we
-can see on figure~\ref{fig3LT}(b) that the lifetime is about two times longer
-with LiCO compared to DESK protocol. The performance difference is more obvious
-in figure~\ref{fig3LT}(b) than in figure~\ref{fig3LT}(a) because the gain
-induced by our protocols increases with the time, and the lifetime with a
-coverage of 50\% is far more longer than with 95\%.
+two other approaches in prolonging the network lifetime. In
+figures~\ref{fig3LT}(a) and (b), $Lifetime95$ and $Lifetime50$ are shown for
+different network sizes. As highlighted by these figures, the lifetime
+increases with the size of the network, and it is clearly the larger for DiLCO
+and LiCO protocols. For instance, for a network of 300~sensors and coverage
+ratio greater than 50\%, we can see on figure~\ref{fig3LT}(b) that the lifetime
+is about two times longer with LiCO compared to DESK protocol. The performance
+difference is more obvious in figure~\ref{fig3LT}(b) than in
+figure~\ref{fig3LT}(a) because the gain induced by our protocols increases with
+the time, and the lifetime with a coverage of 50\% is far more longer than with
+95\%.
\begin{figure}[h!]
\centering
an interesting method since it achieves a good balance between a high level
coverage ratio and network lifetime. LiCO always outperforms DiLCO for the three
lower coverage ratios, moreover the improvements grow with the network
-size. DiLCO is better for coverage ratios near 100\%, but LiCO is not so bad for
-the smallest network sizes.
+size. DiLCO is better for coverage ratios near 100\%, but in that case LiCO is
+not so bad for the smallest network sizes.
\begin{figure}[h!]
\centering \includegraphics[scale=0.5]{R/LTa.eps}
In this paper we have studied the problem of lifetime coverage optimization in
WSNs. We designed a new protocol, called Lifetime Coverage Optimization, which
-schedules node' activities (wake up and sleep stages) with the objective of
+schedules nodes' activities (wake up and sleep stages) with the objective of
maintaining a good coverage ratio while maximizing the network lifetime. This
protocol is applied in a distributed way in regular subregions obtained after
partitioning the area of interest in a preliminary step. It works in periods and
