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** Conference Paper **
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%
% paper title
% can use linebreaks \\ within to get better formatting as desired
-\title{Coverage and Lifetime Optimization in Heterogeneous Energy Wireless Sensor Networks}
+\title{Coverage and Lifetime Optimization \\
+in Heterogeneous Energy Wireless Sensor Networks}
-\author{\IEEEauthorblockN{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier}
+\author{\IEEEauthorblockN{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon,
+and Rapha\"el Couturier}
\IEEEauthorblockA{FEMTO-ST Institute, UMR 6174 CNRS \\
University of Franche-Comt\'e \\
Belfort, France\\
-Email: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}}
+Email: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon,
+raphael.couturier$\rbrace$@univ-fcomte.fr}}
\maketitle
-
\begin{abstract}
One of the fundamental challenges in Wireless Sensor Networks (WSNs)
-is the coverage preservation and the extension of the network lifetime
+is the coverage preservation and the extension of the network lifetime
continuously and effectively when monitoring a certain area (or
-region) of interest. In this paper, a coverage optimization protocol to
-improve the lifetime in heterogeneous energy wireless sensor networks
-is proposed. The area of interest is first divided into subregions
-using a divide-and-conquer method and then the scheduling of sensor node
-activity is planned for each subregion. The proposed scheduling
-considers rounds during which a small number of nodes, remaining
-active for sensing, is selected to ensure coverage. Each round
-consists of four phases: (i)~Information Exchange, (ii)~Leader
+region) of interest. In this paper, a coverage optimization protocol
+to improve the lifetime in heterogeneous energy wireless sensor
+networks is proposed. The area of interest is first divided into
+subregions using a divide-and-conquer method and then the scheduling
+of sensor node activity is planned for each subregion. The proposed
+scheduling considers rounds during which a small number of nodes,
+remaining active for sensing, is selected to ensure coverage. Each
+round consists of four phases: (i)~Information Exchange, (ii)~Leader
Election, (iii)~Decision, and (iv)~Sensing. The decision process is
-carried out by a leader node, which solves an integer program.
+carried out by a leader node, which solves an integer program.
Simulation results show that the proposed approach can prolong the
network lifetime and improve the coverage performance.
\end{abstract}
\begin{IEEEkeywords}
-Wireless Sensor Networks, Area Coverage, Network lifetime, Optimization, Scheduling.
+Wireless Sensor Networks, Area Coverage, Network lifetime,
+Optimization, Scheduling.
\end{IEEEkeywords}
%\keywords{Area Coverage, Network lifetime, Optimization, Distributed Protocol}
\IEEEpeerreviewmaketitle
-
\section{Introduction}
-\noindent The fast developments in the low-cost sensor devices and wireless communications have allowed the emergence the WSNs. 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 wireless communications , cooperate together to monitor the area of interest, and the measured data can be reported
- to a monitoring center
-called, sink, for analysis it~\cite{Ammari01, Sudip03}. There are several applications used the WSN including health, home, environmental, military,and industrial applications~\cite{Akyildiz02}.
-The coverage problem is one of the fundamental challenges in WSNs~\cite{Nayak04} that consists in monitoring efficiently and continuously the area of interest. The limited energy of sensors represents the main challenge in the WSNs design~\cite{Ammari01}, where it is difficult to replace and/or
- recharge their batteries because the the area of interest nature (such as hostile environments) and the cost. So, it is necessary that a WSN deployed with high density because spatial redundancy can then be exploited to increase the lifetime of the network . However, turn on all the sensor nodes, which monitor the same region at the same time leads to decrease the lifetime of the network. To extend the lifetime of the network, the main idea is to take advantage of the overlapping sensing regions of some sensor nodes to save energy by turning off some of them during the sensing phase~\cite{Misra05}. WSNs require energy-efficient solutions to improve the network lifetime that is constrained by the limited power of each sensor node ~\cite{Akyildiz02}.
-In this paper, we concentrate on the area
+\noindent The fast developments in the low-cost sensor devices and
+wireless communications have allowed the emergence the WSNs. 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 wireless communications
+, cooperate together to monitor the area of interest, and the measured
+data can be reported to a monitoring center called, sink, for analysis
+it~\cite{Ammari01, Sudip03}. There are several applications used the
+WSN including health, home, environmental, military,and industrial
+applications~\cite{Akyildiz02}. The coverage problem is one of the
+fundamental challenges in WSNs~\cite{Nayak04} that consists in
+monitoring efficiently and continuously the area of interest. The
+limited energy of sensors represents the main challenge in the WSNs
+design~\cite{Ammari01}, where it is difficult to replace and/or
+recharge their batteries because the the area of interest nature (such
+as hostile environments) and the cost. So, it is necessary that a WSN
+deployed with high density because spatial redundancy can then be
+exploited to increase the lifetime of the network . However, turn on
+all the sensor nodes, which monitor the same region at the same time
+leads to decrease the lifetime of the network. To extend the lifetime
+of the network, the main idea is to take advantage of the overlapping
+sensing regions of some sensor nodes to save energy by turning off
+some of them during the sensing phase~\cite{Misra05}. WSNs require
+energy-efficient solutions to improve the network lifetime that is
+constrained by the limited power of each sensor node
+~\cite{Akyildiz02}. In this paper, we concentrate on the area
coverage problem, with the objective of maximizing the network
lifetime by using an adaptive scheduling. The area of interest is
divided into subregions and an activity scheduling for sensor nodes is
-planned for each subregion.
- In fact, the nodes in a subregion can be seen as a cluster where
- each node sends sensing data to the cluster head or the sink node.
- Furthermore, the activities in a subregion/cluster can continue even
- if another cluster stops due to too many node failures.
-Our scheduling scheme considers rounds, where a round starts with a
-discovery phase to exchange information between sensors of the
-subregion, in order to choose in a suitable manner a sensor node to
-carry out a coverage strategy. This coverage strategy involves the
-solving of an integer program, which provides the activation of the
-sensors for the sensing phase of the current round.
+planned for each subregion. In fact, the nodes in a subregion can be
+seen as a cluster where each node sends sensing data to the cluster
+head or the sink node. Furthermore, the activities in a
+subregion/cluster can continue even if another cluster stops due to
+too many node failures. Our scheduling scheme considers rounds, where
+a round starts with a discovery phase to exchange information between
+sensors of the subregion, in order to choose in a suitable manner a
+sensor node to carry out a coverage strategy. This coverage strategy
+involves the solving of an integer program, which provides the
+activation of the sensors for the sensing phase of the current round.
The remainder of the paper is organized as follows. The next section
% Section~\ref{rw}
reviews the related work in the field. Section~\ref{pd} is devoted to
the scheduling strategy for energy-efficient coverage.
-Section~\ref{cp} gives the coverage model formulation, which is used to
-schedule the activation of sensors. 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 concluding remarks and some
-suggestions for future works in Section~\ref{sec:conclusion}.
-
+Section~\ref{cp} gives the coverage model formulation, which is used
+to schedule the activation of sensors. 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 concluding remarks and some suggestions
+for future works in Section~\ref{sec:conclusion}.
\section{Related works}
\label{rw}
-\indent In this section, we only review some recent work with the coverage lifetime maximization problem, where the objective is to optimally schedule sensors' activities in
-order to extend network lifetime in WSNS. Authors in \cite{chin2007} propose a novel
-distributed heuristic named Distributed Energy-efficient Scheduling
-for k-coverage (DESK) so that the energy consumption among all the
-sensors is balanced, and network lifetime is maximized while the
-coverage requirement is being maintained. This algorithm works in
-round, requires only 1-sensing-hop-neighbor information, and a sensor
-decides its status (active/sleep) based on the perimeter coverage
-model, which proposed in \cite{Huang:2003:CPW:941350.941367}.
-Shibo et al.\cite{Shibo} studied the coverage problem, which is formulated as a minimum weight submodular set cover problem. To address this problem,
- a distributed truncated greedy algorithm (DTGA) is proposed. They exploited from the
-temporal and spatialcorrelations among the data sensed by different sensor nodes and leverage
-prediction to extend the WSNs lifetime.
-Bang et al. \cite{Bang} proposed a coverage-aware clustering protocol(CACP), which used computation method for the optimal cluster size to minimize the average energy consumption rate per unit area. They defied in this protocol a cost metric that prefer the redundant sensors
-with higher power as best candidates for cluster heads and select the active sensors that cover the area of interest more efficiently.
-Zhixin et al. \cite{Zhixin} propose a Distributed Energy-
-Efficient Clustering with Improved Coverage(DEECIC) algorithm
-which aims at clustering with the least number of cluster
-heads to cover the whole network and assigning a unique ID
-to each node based on local information. In addition, this
-protocol periodically updates cluster heads according to the
-joint information of nodes $’ $residual energy and distribution.
-Although DEECIC does not require knowledge of a node's
-geographic location, it guarantees full coverage of the
-network. However, the protocol does not make any activity
-scheduling to set redundant sensors in passive mode in order
-to conserve energy. C. Liu and G. Cao \cite{Changlei} studied how to
-schedule sensor active time to maximize their coverage during a specified network lifetime. Their objective is to maximize the spatial-temporal coverage by scheduling sensors activity after they have been deployed. They proposed both centralized and distributed algorithms. The distributed parallel optimization protocol can ensure each sensor to converge to local optimality without conflict with each other. 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. L. Zhang et al. \cite{Zhang} presented a novel distributed clustering algorithm
-called Adaptive Energy Efficient Clustering (AEEC) to maximize network lifetime. In this study, they are introduced an optimization, which includes restricted global re-clustering,
-intra-cluster node sleeping scheduling and adaptive
-transmission range adjustment to conserve the energy, while connectivity and coverage is ensured. J. A. Torkestani \cite{Torkestani} proposed a learning automata-based energy-efficient coverage protocol
- named as LAEEC to construct the degree-constrained connected dominating set (DCDS) in WSNs. He shows that the correct choice of the degree-constraint of DCDS balances the network load on the active nodes and leads to enhance the coverage and network lifetime.
+\indent In this section, we only review some recent works dealing with
+the coverage lifetime maximization problem, where the objective is to
+optimally schedule sensors' activities in order to extend WSNs
+lifetime.
+
+In \cite{chin2007} is 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 1-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}. 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. A
+Coverage-Aware Clustering Protocol (CACP), which uses a computation
+method to find the cluster size minimizing the average energy
+consumption rate per unit area, has been proposed by Bang et al. in
+\cite{Bang}. Their protocol is based on a cost metric that selects the
+redundant sensors with higher power as best candidates for cluster
+heads and the active sensors that cover the area of interest the more
+efficiently.
+
+% TO BE CONTINUED
+
+Zhixin et al. \cite{Zhixin} propose a Distributed Energy- Efficient
+Clustering with Improved Coverage(DEECIC) algorithm which aims at
+clustering with the least number of cluster heads to cover the whole
+network and assigning a unique ID to each node based on local
+information. In addition, this protocol periodically updates cluster
+heads according to the joint information of nodes $’ $residual energy
+and distribution. Although DEECIC does not require knowledge of a
+node's geographic location, it guarantees full coverage of the
+network. However, the protocol does not make any activity scheduling
+to set redundant sensors in passive mode in order to conserve energy.
+
+C. Liu and G. Cao \cite{Changlei} studied how to schedule sensor
+active time to maximize their coverage during a specified network
+lifetime. Their objective is to maximize the spatial-temporal coverage
+by scheduling sensors activity after they have been deployed. They
+proposed both centralized and distributed algorithms. The distributed
+parallel optimization protocol can ensure each sensor to converge to
+local optimality without conflict with each other.
+
+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.
+
+L. Zhang et al. \cite{Zhang} presented a novel distributed clustering
+algorithm called Adaptive Energy Efficient Clustering (AEEC) to
+maximize network lifetime. In this study, they are introduced an
+optimization, which includes restricted global re-clustering,
+intra-cluster node sleeping scheduling and adaptive transmission range
+adjustment to conserve the energy, while connectivity and coverage is
+ensured.
+
+J. A. Torkestani \cite{Torkestani} proposed a learning automata-based
+energy-efficient coverage protocol named as LAEEC to construct the
+degree-constrained connected dominating set (DCDS) in WSNs. He shows
+that the correct choice of the degree-constraint of DCDS balances the
+network load on the active nodes and leads to enhance the coverage and
+network lifetime.
-The main contribution of our approach addresses three main questions to build a
-scheduling strategy. We give a brief answer to these three questions
-to describe our approach before going into details in the subsequent
-sections.
+The main contribution of our approach addresses three main questions
+to build a scheduling strategy:
%\begin{itemize}
%\item
{\bf How must the phases for information exchange, decision and
