+
+\noindent In this section, we summarize some related works regarding coverage
+problem and distinguish our DiLCO protocol from the works presented in the
+literature.
+
+The most discussed coverage problems in literature
+can be classified into three types \cite{li2013survey}: area coverage (where
+every point inside an area is to be monitored), target coverage (where the main
+objective is to cover only a finite number of discrete points called targets),
+and barrier coverage (to prevent intruders from entering into the region of
+interest).
+{\it In DiLCO protocol, the area coverage, i.e. the coverage of every point in
+ the sensing region, is transformed to the coverage of a fraction of points
+ called primary points. }
+
+The major approach to extend network lifetime while preserving coverage is to
+divide/organize the sensors into a suitable number of set covers (disjoint or
+non-disjoint) where each set completely covers a region of interest and to
+activate these set covers successively. The network activity can be planned in
+advance and scheduled for the entire network lifetime or organized in periods,
+and the set of active sensor nodes is decided at the beginning of each period.
+Active node selection is determined based on the problem requirements (e.g. area
+monitoring, connectivity, power efficiency). Different methods have been
+proposed in literature.
+{\it DiLCO protocol works in periods, where each period contains a preliminary
+ phase for information exchange and decisions, followed by a sensing phase
+ where one cover set is in charge of the sensing task.}
+
+Various approaches, including centralized, distributed, and localized
+algorithms, have been proposed to extend the network lifetime.
+%For instance, in order to hide the occurrence of faults, or the sudden unavailability of
+%sensor nodes, some distributed algorithms have been developed in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02}.
+In distributed algorithms~\cite{yangnovel,ChinhVu,qu2013distributed},
+information is disseminated throughout the network and sensors decide
+cooperatively by communicating with their neighbors which of them will remain in
+sleep mode for a certain period of time. The centralized
+algorithms~\cite{cardei2005improving,zorbas2010solving,pujari2011high} always
+provide nearly or close to optimal solution since the algorithm has global view
+of the whole network, but such a method has the disadvantage of requiring high
+communication costs, since the node (located at the base station) making the
+decision needs information from all the sensor nodes in the area.
+
+A large variety of coverage scheduling algorithms have been proposed. Many of
+the existing algorithms, dealing with the maximization of the number of cover
+sets, are heuristics. These heuristics involve the construction of a cover set
+by including in priority the sensor nodes which cover critical targets, that is
+to say targets that are covered by the smallest number of sensors. Other
+approaches are based on mathematical programming formulations and dedicated
+techniques (solving with a branch-and-bound algorithms available in optimization
+solver). The problem is formulated as an optimization problem (maximization of
+the lifetime or number of cover sets) under target coverage and energy
+constraints. Column generation techniques, well-known and widely practiced
+techniques for solving linear programs with too many variables, have been also
+used~\cite{castano2013column,rossi2012exact,deschinkel2012column}.
+
+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.
+
+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 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.
+
+In ~\cite{chenait2013distributed}, the authors presented a coverage-guaranteed
+distributed sleep/wake scheduling scheme so ass to prolong network lifetime
+while guaranteeing network coverage. This scheme mitigates scheduling process to
+be more stable by avoiding useless transitions between states without affecting
+the coverage level required by the application.
+
+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.
+
+{\it In DiLCO protocol, the area coverage is divided into several smaller
+ subregions, and in each of which, a node called the leader is on charge for
+ selecting the active sensors for the current period.}
+
+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.
+
+The work proposed by \cite{qu2013distributed} considers the coverage problem in
+WSNs where each sensor has variable sensing radius. The final objective is to
+maximize the network coverage lifetime in WSNs.
+
+{\it In DiLCO protocol, each leader, in each subregion, solves an integer
+ program with a double objective consisting in minimizing the overcoverage and
+ limiting the undercoverage. This program is inspired from the work of
+ \cite{pedraza2006} where the objective is to maximize the number of cover
+ sets.}
+
+\iffalse
+
+Some algorithms have been developed in ~\cite{yang2014energy,ChinhVu,vashistha2007energy,deschinkel2012column,shi2009,qu2013distributed,ling2009energy,xin2009area,cheng2014achieving,ling2009energy} to solve the area coverage problem so as to preserve coverage and prolong the network lifetime.
