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\begin{document}
\title{{\itshape Perimeter-based Coverage Optimization \\
to Improve Lifetime in Wireless Sensor Networks}}
-\author{Ali Kadhum Idrees$^{a,b}$, Karine Deschinkel$^{a}$$^{\ast}$\thanks{$^\ast$Corresponding author. Email: karine.deschinkel@univ-fcomte.fr}, Michel Salomon$^{a}$ and Rapha\"el Couturier $^{a}$
+\author{Ali Kadhum Idrees$^{a,b}$, Karine Deschinkel$^{a}$$^{\ast}$\thanks{$^\ast$Corresponding author. Email: karine.deschinkel@univ-fcomte.fr}, Michel Salomon$^{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}}
\label{sec:introduction}
The continuous progress in Micro Electro-Mechanical Systems (MEMS) and wireless
-communication hardware has given rise to the opportunity of using large networks
+communication hardware has given rise to the opportunity of using large networks
of tiny sensors, called Wireless Sensor Networks
(WSN)~\citep{akyildiz2002wireless,puccinelli2005wireless}, to fulfill monitoring
tasks. A WSN consists of small low-powered sensors working together by
communicating with one another through multi-hop radio communications. Each node
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 makes it suitable for a wide
-range of applications in areas such as business, environment, health, industry,
+user by means of sink nodes. The features of a WSN makes it suitable for a wide
+range of applications in areas such as business, environment, health, industry,
military, and so on~\citep{yick2008wireless}. Typically, a sensor node contains
three main components~\citep{anastasi2009energy}: a sensing unit able to measure
physical, chemical, or biological phenomena observed in the environment; a
The energy needed by an active sensor node to perform sensing, processing, and
communication is provided by a power supply which is a battery. This battery has
a limited energy provision and it may be unsuitable or impossible to replace or
-recharge in most applications. Therefore it is necessary to deploy WSN with
-high density in order to increase reliability and to exploit node redundancy
-thanks to energy-efficient activity scheduling approaches. Indeed, the overlap
-of sensing areas can be exploited to schedule alternatively some sensors in a
-low power sleep mode and thus save energy. Overall, the main question that must
-be answered is: how is it possible to extend the lifetime coverage of a WSN as long as possible
-while ensuring a high level of coverage? These past few years many
+recharge in most applications. Therefore it is necessary to deploy WSN with high
+density in order to increase reliability and to exploit node redundancy thanks
+to energy-efficient activity scheduling approaches. Indeed, the overlap of
+sensing areas can be exploited to schedule alternatively some sensors in a low
+power sleep mode and thus save energy. Overall, the main question that must be
+answered is: how is it possible to extend the lifetime coverage of a WSN as long
+as possible while ensuring a high level of coverage? These past few years many
energy-efficient mechanisms have been suggested to retain energy and extend the
lifetime of the WSNs~\citep{rault2014energy}.
architecture.
\item A new mathematical optimization model is proposed. Instead of trying to
cover a set of specified points/targets as in most of the methods proposed in
- the literature, we formulate a mixed-integer program based on the perimeter coverage of
- each sensor. The model involves integer variables to capture the deviations
- between the actual level of coverage and the required level. Hence, an
- optimal schedule will be obtained by minimizing a weighted sum of these
- deviations.
+ the literature, we formulate a mixed-integer program based on the perimeter
+ coverage of each sensor. The model involves integer variables to capture the
+ deviations between the actual level of coverage and the required level.
+ Hence, an optimal schedule will be obtained by minimizing a weighted sum of
+ these deviations.
\item Extensive simulation experiments are conducted using the discrete event
- simulator OMNeT++, to demonstrate the efficiency of our protocol. We have
+ simulator OMNeT++, to demonstrate the efficiency of our protocol. We have
compared the PeCO protocol to two approaches found in the literature:
DESK~\citep{ChinhVu} and GAF~\citep{xu2001geography}, and also to our previous
protocol DiLCO published in~\citep{Idrees2}. DiLCO uses the same framework as
targets, and barrier coverage~\citep{HeShibo,kim2013maximum} focuses on
preventing intruders from entering into the region of interest. In
\citep{Deng2012} authors transform the area coverage problem into the target
-coverage one, taking into account the intersection points among disks of sensors
-nodes or between disks of sensor nodes and boundaries. In
-\citep{Huang:2003:CPW:941350.941367} authors prove that if the perimeters of the
-sensors are sufficiently covered it will be the case for the whole area. They
-provide an algorithm in $O(nd~log~d)$ time to compute the perimeter-coverage of
-each sensor. $d$ denotes the maximum number of sensors that are neighbors to a
+coverage one, taking into account the intersection points among disks of sensors
+nodes or between disks of sensor nodes and boundaries. In
+\citep{huang2005coverage} authors prove that if the perimeters of the sensors
+are sufficiently covered it will be the case for the whole area. They provide an
+algorithm in $O(nd~log~d)$ time to compute the perimeter-coverage of each
+sensor. $d$ denotes the maximum number of sensors that are neighbors to a
sensor, and $n$ is the total number of sensors in the network. {\it In PeCO
protocol, instead of determining the level of coverage of a set of discrete
points, our optimization model is based on checking the perimeter-coverage of
its own activity scheduling after an information exchange with its neighbors.
The main interest of such an approach is to avoid long range communications and
thus to reduce the energy dedicated to the communications. Unfortunately, since
-each node has information on its immediate neighbors only (usually the one-hop
-ones), it may make a bad decision leading to a global suboptimal solution.
+each node has information on its immediate neighbors only (usually the one-hop
+ones), it may make a bad decision leading to a global suboptimal solution.
Conversely, centralized
algorithms~\citep{cardei2005improving,zorbas2010solving,pujari2011high} always
-provide nearly optimal solutions since the algorithm has a global
-view of the whole network. The disadvantage of a centralized method is obviously
-its high cost in communications needed to transmit to a single node, the base
-station which will globally schedule nodes' activities, data from all the other
-sensor nodes in the area. The price in communications can be huge since long
-range communications will be needed. In fact the larger the WSN, the higher the
+provide nearly optimal solutions since the algorithm has a global view of the
+whole network. The disadvantage of a centralized method is obviously its high
+cost in communications needed to transmit to a single node, the base station
+which will globally schedule nodes' activities, data from all the other sensor
+nodes in the area. The price in communications can be huge since long range
+communications will be needed. In fact the larger the WSN, the higher the
communication energy cost. {\it In order to be suitable for large-scale
- networks, in the PeCO protocol the area of interest is divided into several
+ networks, in the PeCO protocol the area of interest is divided into several
smaller subregions, and in each one, a node called the leader is in charge of
- selecting the active sensors for the current period. Thus the PeCO protocol is
- scalable and a globally distributed method, whereas it is centralized in each
- subregion.}
+ selecting the active sensors for the current period. Thus the PeCO protocol
+ is scalable and a globally distributed method, whereas it is centralized in
+ each subregion.}
Various coverage scheduling algorithms have been developed these past few years.
Many of them, dealing with the maximization of the number of cover sets, are
Optimization (DiLCO) protocol, which maintains the coverage and improves the
lifetime in WSNs. It is an improved version of a research work presented
in~\citep{idrees2014coverage}. First, the area of interest is partitioned into
-subregions using a divide-and-conquer method. The DiLCO protocol is then distributed
-on the sensor nodes in each subregion in a second step. Hence this protocol
-combines two techniques: a leader election in each subregion, followed by an
-optimization-based node activity scheduling performed by each elected
+subregions using a divide-and-conquer method. The DiLCO protocol is then
+distributed on the sensor nodes in each subregion in a second step. Hence this
+protocol combines two techniques: a leader election in each subregion, followed
+by an optimization-based node activity scheduling performed by each elected
leader. The proposed DiLCO protocol is a periodic protocol where each period is
decomposed into 4 phases: information exchange, leader election, decision, and
sensing. The simulations show that DiLCO is able to increase the WSN lifetime
and provides improved coverage performance. {\it In the PeCO protocol, a new
mathematical optimization model is proposed. Instead of trying to cover a set
- of specified points/targets as in the DiLCO protocol, we formulate an integer
- program based on the perimeter coverage of each sensor. The model involves integer
- variables to capture the deviations between the actual level of coverage and
- the required level. The idea is that an optimal scheduling will be obtained by
- minimizing a weighted sum of these deviations.}
+ of specified points/targets as in the DiLCO protocol, we formulate an integer
+ program based on the perimeter coverage of each sensor. The model involves
+ integer variables to capture the deviations between the actual level of
+ coverage and the required level. The idea is that an optimal scheduling will
+ be obtained by minimizing a weighted sum of these deviations.}
\section{ The P{\scshape e}CO Protocol Description}
\label{sec:The PeCO Protocol Description}
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 their one-hop live neighbors during the information exchange.
+and the number of their one-hop live neighbors during the information exchange. \textcolor{blue}{We suppose that both INFO packet and ActiveSleep packet contain two parts: header and data payload. The sensor id is included in the header, where the header size is 8 bits. The data part includes position coordinates (64 bits), remaining energy (32 bits), and the number of their one-hop live neighbors (8 bits). Therefore the size of the INFO packet is 112 bits. The ActiveSleep packet is 16 bits size, 8 bits for the header and 8 bits for data part that includes only sensor status (0 or 1)}
The sensors inside a same region cooperate to elect a leader. The selection
criteria for the leader are (in order of priority):
\begin{enumerate}
where $|A_r^p|$ is the number of active sensors in the subregion $r$ in the
sensing period~$p$, $R$ is the number of subregions, and $|J|$ is the number
of sensors in the network.
+
+\item {\bf \textcolor{blue}{Energy Saving Ratio (ESR)}}:
+\textcolor{blue}{we consider a performance metric linked to energy. This metric, called Energy Saving Ratio (ESR), is defined by:
+\begin{equation*}
+\scriptsize
+\mbox{ESR}(\%) = \frac{\mbox{Number of alive sensors during this round}}
+{\mbox{Total number of sensors in the network}} \times 100.
+\end{equation*}
+ }
\item {\bf Energy Consumption (EC)}: energy consumption can be seen as the total
energy consumed by the sensors during $Lifetime_{95}$ or $Lifetime_{50}$,
divided by the number of periods. The value of EC is computed according to
\subsection{Simulation Results}
In order to assess and analyze the performance of our protocol we have
-implemented the PeCO protocol in OMNeT++~\citep{varga} simulator. The simulations
-were run on a DELL laptop with an Intel Core~i3~2370~M (1.8~GHz) processor (2
-cores) whose MIPS (Million Instructions Per Second) rate is equal to 35330. To
-be consistent with the use of a sensor node based on Atmels AVR ATmega103L
-microcontroller (6~MHz) having a MIPS rate equal to 6, the original execution
-time on the laptop is multiplied by 2944.2 $\left(\frac{35330}{2} \times
-\frac{1}{6} \right)$. Energy consumption is calculated according to the power
-consumption values, in milliWatt per second, given in Table~\ref{tab:EC}.
+implemented the PeCO protocol in OMNeT++~\citep{varga} simulator. The
+simulations were run on a DELL laptop with an Intel Core~i3~2370~M (1.8~GHz)
+processor (2 cores) whose MIPS (Million Instructions Per Second) rate is equal
+to 35330. To be consistent with the use of a sensor node based on Atmels AVR
+ATmega103L microcontroller (6~MHz) having a MIPS rate equal to 6, the original
+execution time on the laptop is multiplied by 2944.2 $\left(\frac{35330}{2}
+\times \frac{1}{6} \right)$. Energy consumption is calculated according to the
+power consumption values, in milliWatt per second, given in Table~\ref{tab:EC},
based on the energy model proposed in \citep{ChinhVu}.
\begin{table}[h]
for a WSN of 200 sensor nodes, a leader node has to deal with constraints
induced by about 12 sensor nodes. In that case, to solve the optimization
problem a memory consumption of more than 1~MB can be observed with GLPK,
-whereas less than 300~kB would be needed with CPLEX.
+whereas less than 300~KB would be needed with CPLEX.
Besides PeCO, three other protocols will be evaluated for comparison
purposes. The first one, called DESK, is a fully distributed coverage algorithm
squares. Then, during the decision phase, in each square, one sensor is chosen
to remain active during the sensing phase. The last one, the DiLCO
protocol~\citep{Idrees2}, is an improved version of a research work we presented
-in~\citep{idrees2014coverage}. Let us notice that the PeCO and DiLCO protocols are
-based on the same framework. In particular, the choice for the simulations of a
-partitioning in 16~subregions was made because it corresponds to the
+in~\citep{idrees2014coverage}. Let us notice that the PeCO and DiLCO protocols
+are based on the same framework. In particular, the choice for the simulations
+of a partitioning in 16~subregions was made because it corresponds to the
configuration producing the best results for DiLCO. Of course, this number of
-subregions should be adapted according to the size of the area of interest and
+subregions should be adapted according to the size of the area of interest and
the number of sensors. The 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. The DiLCO protocol tries to satisfy the coverage of
-a set of primary points, whereas the objective of the PeCO protocol is to reach a desired
-level of coverage for each sensor perimeter. In our experimentations, we chose a
-level of coverage equal to one ($l=1$).
+activated in each sensing phase. The DiLCO protocol tries to satisfy the
+coverage of a set of primary points, whereas the objective of the PeCO protocol
+is to reach a desired level of coverage for each sensor perimeter. In our
+experimentations, we chose a level of coverage equal to one ($l=1$).
\subsubsection{Coverage Ratio}
obtained with the four protocols. DESK, GAF, and DiLCO provide a slightly better
coverage ratio with respectively 99.99\%, 99.91\%, and 99.02\%, compared to the
98.76\% produced by PeCO for the first periods. This is due to the fact that at
-the beginning the DiLCO and PeCO protocols put more redundant
-sensors to sleep status (which slightly decreases the coverage ratio), while the two other
-protocols activate more sensor nodes. Later, when the number of periods is
+the beginning the DiLCO and PeCO protocols put more redundant sensors to sleep
+status (which slightly decreases the coverage ratio), while the two other
+protocols activate more sensor nodes. Later, when the number of periods is
beyond~70, it clearly appears that PeCO provides a better coverage ratio and
keeps a coverage ratio greater than 50\% for longer periods (15 more compared to
DiLCO, 40 more compared to DESK). The energy saved by PeCO in the early periods
\label{figure6}
\end{figure}
+\subsubsection{\textcolor{blue}{Energy Saving Ratio (ESR)}}
+
+\textcolor{blue}{In this experiment, we consider an Energy Saving Ratio (see Figure~\ref{fig5}) for 200 deployed nodes.
+The longer the ratio is, the more redundant sensor nodes are switched off, and consequently the longer the network may live. }
+
+\begin{figure}[h!]
+%\centering
+% \begin{multicols}{6}
+\centering
+\includegraphics[scale=0.5]{ESR.eps} %\\~ ~ ~(a)
+\caption{Energy Saving Ratio for 200 deployed nodes}
+\label{fig5}
+\end{figure}
+
+\textcolor{blue}{The simulation results show that our protocol PeCO allows to efficiently save energy by turning off some sensors during the sensing phase.
+As shown in Figure~\ref{fig5}, GAF provides better energy saving than PeCO for the first fifty rounds, because GAF balance the energy consumption among sensor nodes inside each small fixed grid that permits to extend the life of sensors in each grid fairly but in the same time turn on large number of sensors during sensing that lead later to quickly deplete sensor's batteries togehter. After that GAF provide less energy saving compared with other approches because of the large number of dead nodes. DESK algorithm shows less energy saving compared with other approaches due to activate a larg number of sensors during the sensing. DiLCO protocol provides less energy saving ratio commpared with PeCO because it generally activate a larger number of sensor nodes during sensing. Note that again as the number of rounds increases PeCO becomes the most performing one, since it consumes less energy compared with other approaches.}
+
+
\subsubsection{Energy Consumption}
The effect of the energy consumed by the WSN during the communication,
computation, listening, active, and sleep status is studied for different
network densities and the four approaches compared. Figures~\ref{figure7}(a)
and (b) illustrate the energy consumption for different network sizes and for
-$Lifetime95$ and $Lifetime50$. The results show that the PeCO protocol is the most
+$Lifetime_{95}$ and $Lifetime_{50}$. The results show that the PeCO protocol is the most
competitive from the energy consumption point of view. As shown by both figures,
PeCO consumes much less energy than the other methods. One might think that the
resolution of the integer program is too costly in energy, but the results show
We observe the superiority of both the PeCO and DiLCO protocols in comparison with
the two other approaches in prolonging the network lifetime. In
-Figures~\ref{figure8}(a) and (b), $Lifetime95$ and $Lifetime50$ are shown for
+Figures~\ref{figure8}(a) and (b), $Lifetime_{95}$ and $Lifetime_{50}$ are shown for
different network sizes. As can be seen in these figures, the lifetime
increases with the size of the network, and it is clearly larger for the DiLCO and
PeCO protocols. For instance, for a network of 300~sensors and coverage ratio
Protocol/85, Protocol/90, and Protocol/95 the amount of time during which the
network can satisfy an area coverage greater than $50\%$, $80\%$, $85\%$,
$90\%$, and $95\%$ respectively, where the term Protocol refers to DiLCO or
-PeCO. Indeed there are applications that do not require a 100\% coverage of the
-area to be monitored. PeCO might be an interesting method since it achieves a
-good balance between a high level coverage ratio and network lifetime. PeCO
+PeCO. \textcolor{blue}{Indeed there are applications that do not require a 100\% coverage of the
+area to be monitored. For example, forest
+fire application might require complete coverage
+in summer seasons while only requires 80$\%$ of the area to be covered in rainy seasons \cite{li2011transforming}. As another example, birds habit study requires only 70$\%$-coverage at nighttime when the birds are sleeping while requires 100$\%$-coverage at daytime when the birds are active \cite{vu2009universal}. Mudflows monitoring applications may require part of the area to be covered in sunny days. Thus, to extend network lifetime, the coverage quality can be decreased if it is acceptable\cite{wang2014keeping}}. PeCO might be an interesting method since it achieves a good balance between a high level coverage ratio and network lifetime. PeCO
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 in that case PeCO is not ineffective for the smallest network
-sizes.
+improvements grow with the network size. \textcolor{blue}{DiLCO outperforms PeCO when the coverage ratio is required to be $>90\%$, but PeCo extends the network lifetime significantly when coverage ratio can be relaxed.}
+%DiLCO is better for coverage ratios near 100\%, but in that case PeCO is not ineffective for the smallest network sizes.
\begin{figure}[h!]
\centering \includegraphics[scale=0.55]{figure9.eps}
Table~\ref{my-labelx} shows network lifetime results for different values of
$\alpha$ and $\beta$, and a network size equal to 200 sensor nodes. On the one
-hand, the choice of $\beta \gg \alpha$ prevents the overcoverage, and also limits
-the activation of a large number of sensors, but as $\alpha$ is low, some areas
-may be poorly covered. This explains the results obtained for {\it Lifetime50}
-with $\beta \gg \alpha$: a large number of periods with low coverage ratio. On
-the other hand, when we choose $\alpha \gg \beta$, we favor the coverage even if
-some areas may be overcovered, so ahigh coverage ratio is reached, but a large
-number of sensors are activated to achieve this goal. Therefore the network
-lifetime is reduced. The choice $\alpha=0.6$ and $\beta=0.4$ seems to achieve
-the best compromise between lifetime and coverage ratio. That explains why we
-have chosen this setting for the experiments presented in the previous
-subsections.
+hand, the choice of $\beta \gg \alpha$ prevents the overcoverage, and also
+limits the activation of a large number of sensors, but as $\alpha$ is low, some
+areas may be poorly covered. This explains the results obtained for
+$Lifetime_{50}$ with $\beta \gg \alpha$: a large number of periods with low
+coverage ratio. On the other hand, when we choose $\alpha \gg \beta$, we favor
+the coverage even if some areas may be overcovered, so a high coverage ratio is
+reached, but a large number of sensors are activated to achieve this goal.
+Therefore the network lifetime is reduced. The choice $\alpha=0.6$ and
+$\beta=0.4$ seems to achieve the best compromise between lifetime and coverage
+ratio. That explains why we have chosen this setting for the experiments
+presented in the previous subsections.
%As can be seen in Table~\ref{my-labelx}, it is obvious and clear that when $\alpha$ decreased and $\beta$ increased by any step, the network lifetime for $Lifetime_{50}$ increased and the $Lifetime_{95}$ decreased. Therefore, selecting the values of $\alpha$ and $\beta$ depend on the application type used in the sensor nework. In PeCO protocol, $\alpha$ and $\beta$ are chosen based on the largest value of network lifetime for $Lifetime_{95}$.
We plan to extend our framework so that the schedules are planned for multiple
sensing periods. We also want to improve the integer program to take into
account heterogeneous sensors from both energy and node characteristics point of
-views. Finally, it would be interesting to implement the PeCO protocol using a
+views. Finally, it would be interesting to implement the PeCO protocol using a
sensor-testbed to evaluate it in real world applications.
-
-\subsection*{Acknowledgements}
+\subsection*{Acknowledgments}
The authors are deeply grateful to the anonymous reviewers for their
constructive advice, which improved the technical quality of the paper. As a
-Ph.D. student, Ali Kadhum IDREES would like to gratefully acknowledge the
+Ph.D. student, Ali Kadhum Idrees would like to gratefully acknowledge the
University of Babylon - Iraq for financial support and Campus France for the
received support. This work is also partially funded by the Labex ACTION program
-(contract ANR-11-LABX-01-01).
-
+(contract ANR-11-LABX-01-01).
+
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