-\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.
