+Yang et al.~\cite{yang2014energy} investigated full area coverage problem
+under the probabilistic sensing model in the sensor networks. They have studied the relationship between the
+coverage of two adjacent points mathematically and then convert the problem of full area coverage into point coverage problem. They proposed $\varepsilon$-full area coverage optimization (FCO) algorithm to select a subset
+of sensors to provide probabilistic area coverage dynamically so as to extend the network lifetime.
+
+
+Vu et al.~\cite{ChinhVu} proposed a localized and distributed greedy algorithm named DESK for generating non-disjoint cover sets which provide the k-area coverage for the whole network.
+
+
+Qu et al.~\cite{qu2013distributed} developed a distributed algorithm using adjustable sensing sensors
+for maintaining the full coverage of such sensor networks. The
+algorithm contains two major parts: the first part aims at
+providing $100\%$ coverage and the second part aims at saving
+energy by decreasing the sensing radius.
+
+Shi et al.~\cite{shi2009} modeled the Area Coverage Problem (ACP), which will be changed into a set coverage
+problem. By using this model, they are proposed an Energy-Efficient central-Scheduling greedy algorithm, which can reduces energy consumption and increases network lifetime, by selecting a appropriate subset of sensor nodes to support the networks periodically.
+
+The work in~\cite{cheng2014achieving} presented a unified sensing architecture for duty cycled sensor networks, called uSense, which comprises three ideas: Asymmetric Architecture, Generic Switching and Global Scheduling. The objective is to provide a flexible and efficient coverage in sensor networks.
+
+ In~\cite{ling2009energy}, the lifetime of
+a sensor node is divided into epochs. At each epoch, the
+base station deduces the current sensing coverage requirement
+from application or user request. It then applies the heuristic algorithm in order to produce the set of active nodes which take the mission of sensing during the current epoch. After that, the produced schedule is sent to the sensor nodes in the network.
+
+
+\iffalse
+
+The work in ~\cite{vu2009delaunay} considered the area coverage problem for variable sensing radii in WSNs by improving the energy balancing heuristic proposed in ~\cite{wang2007energy} so that the area of interest can be full covered using Delaunay triangulation structure.
+
+Diongue and Thiare~\cite{diongue2013alarm} proposed an energy aware sleep scheduling algorithm for lifetime maximization in wireless sensor networks (ALARM). The proposed approach permits to schedule redundant nodes according to the weibull distribution. This work did not analyze the ALARM scheme under the coverage problem.
+
+
+In~\cite{xin2009area}, the authors proposed a circle intersection localized coverage algorithm
+to maintain connectivity based on loose connectivity critical condition
+. By using the connected coverage node set, it can maintain network
+connection in the case which loose condition is not meet.
+The authors in ~\cite{vashistha2007energy} addressed the full area coverage problem using information
+coverage. They are proposed a low-complexity heuristic algorithm to obtain full area information covers (FAIC), which they refer to as Grid Based FAIC (GB-FAIC) algorithm. Using these FAICs, they are obtained the optimal schedule for applying the sensing activity of sensor nodes in order to
+achieve increased sensing lifetime of the network.
+
+
+\fi
+
+
+
+In \cite{xu2001geography}, Xu et al. proposed a Geographical Adaptive Fidelity (GAF) algorithm, 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.
+
+The main contributions of our DiLCO Protocol can be summarized as follows:
+(1) The distributed optimization over the subregions in the area of interest,
+(2) The distributed dynamic leader election at each round by each sensor node in the subregion,
+(3) The primary point coverage model to represent each sensor node in the network,
+(4) The activity scheduling based optimization on the subregion, which are based on the primary point coverage model to activate as less number as possible of sensor nodes to take the mission of the coverage in each subregion, and (5) The improved energy consumption model.
+
+\iffalse
+The work presented in~\cite{luo2014parameterized,tian2014distributed} tries to solve the target coverage problem so as to extend the network lifetime since it is easy to verify the coverage status of discreet target.
+%Je ne comprends pas la phrase ci-dessus
+The work proposed in~\cite{kim2013maximum} considers the barrier-coverage problem in WSNs. The final goal is to maximize the network lifetime such that any penetration of the intruder is detected.
+%inutile de parler de ce papier car il concerne barrier coverage
+In \cite{ChinhVu}, the authors propose a localized and distributed greedy algorithm named DESK for generating non-disjoint cover sets which provide the k-coverage for the whole network.
+Our Work in~\cite{idrees2014coverage} proposes a coverage optimization protocol to improve the lifetime in 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 are considered only distributing the coverage protocol over two subregions.
+
+The work presented in ~\cite{Zhang} focuses on a distributed clustering method, which aims to extend the network lifetime, while the coverage is ensured.
