-
-The most discussed coverage problems in literature can be classified into two types \cite{} : area coverage and targets coverage. An area coverage problem is to find a minimum number of sensors to work such that each physical point in the area is monitored by at least a working sensor. Target coverage problem is to cover only a finite number of discrete points called targets.
- Our work will concentrate on the area coverage by design and implement a strategy which efficiently select the active nodes that must maintain both sensing coverage and network connectivity and in the same time improve the lifetime of the wireless sensor network. But requiring that all physical points are covered may be too strict, specially where the sensor network is not dense.
-Our approach represents an area covered by a sensor as a set of principle points and tries to maximize the total number of principles points that are covered in each round, while minimizing overcoverage (points covered by multiple active sensors simultaneously).\\
-{\bf Lifetime}\\
-Various definitions exist for the lifetime of a sensor network. Main definitions proposed in the literature are related to the remaining energy of the nodes \cite{} or to the percentage of coverage \cite{}. The lifetime of the network is mainly defined as the amount of time that the network can satisfy its coverage objective (the amount of time that the network can cover a given percentage of its area or targets of interest) . In our simulation we assume that the network is alive until all sensor nodes are died and we measure the coverage ratio during the process.
-
-{\bf Activity scheduling}\\
-Activity scheduling is to schedule the activation and deactivation of nodes 'sensor units. The basic objective is to decide which sensors are in which states (active or sleeping mode) and for how long a time such that the application coverage requirement can be guaranteed and network lifetime can be prolonged. Various approaches, including centralized, distributed and localized algorithms, have been proposed for activity scheduling. In the distributed algorithms, each node in the network autonomously makes decisions on whether to turn on or turn off itself only using local neighbor information. In centralized algorithms, a central controller (node or base station) informs every sensor of the time intervals to be activated.
-
-{\bf Distributed approaches}
-
-Some distributed algorithms have been developed in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02}. Distributed algorithms typically operate in roundsf predetermined duration. At the beginning of each round, a sensor exchange information with its neighbors and makes a decision to either turn on or go to sleep for the round. This decision is basically based on simple greedy criteria like the largest uncovered area \cite{Berman05efficientenergy}, maximum uncovered targets \cite{1240799}.
-In \cite{Tian02}, the sheduling scheme is divided into rounds, where each round has a self-scheduling phase followed by a sensing phase. Each sensor broadcasts a message to its neighbors containing node ID and node location at the beginning of each round. Sensor determines its status by a rule named off-duty eligible rule which tells him to turn off if its sensing area is covered by its neighbors. A back-off scheme is introduced to let each sensor delay the decision process with a random period of time, in order to avoid that nodes make conflicting decisions simultaneously and that a part of the area is no longer covered.
-\cite{Prasad:2007:DAL:1782174.1782218} propose a model for capturing the dependencies between different cover sets and propose localized heuristic based on this dependency. The algorithm consists of two phases, an initial setup phase during which each sensor calculates and prioritize the covers and a sensing phase during which each sensor first decides its on/off status and then remains on or off for the rest of the duration.
-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 requirements being maintained. This algorithm works in round, requires only 1-sensing-hop-neigbor information, and a sensor decides its status (active/sleep) based on its perimeter coverage computed through the k-Non-Unit-disk coverage algorithm proposed in \cite{Huang:2003:CPW:941350.941367}.\\
-
-Some others approaches do not consider synchronized and predetermined period of time where the sensors are active or not. Each sensor maintains its own timer and its time wake-up is randomized \cite{Ye03} or regulated \cite{cardei05} over time.
+\subsection{Coverage}
+%{\bf Coverage}
+
+The most discussed coverage problems in literature can be classified
+into two types \cite{ma10}: area coverage (also called full or blanket
+coverage) and target coverage. An area coverage problem is to find a
+minimum number of sensors to work, such that each physical point in the
+area is within the sensing range of at least one working sensor node.
+Target coverage problem is to cover only a finite number of discrete
+points called targets. This type of coverage has mainly military
+applications. Our work will concentrate on the area coverage by design
+and implementation of a strategy, which efficiently selects the active
+nodes that must maintain both sensing coverage and network
+connectivity and at the same time improve the lifetime of the wireless
+sensor network. But, requiring that all physical points of the
+considered region are covered may be too strict, especially where the
+sensor network is not dense. Our approach represents an area covered
+by a sensor as a set of primary points and tries to maximize the total
+number of primary points that are covered in each round, while
+minimizing overcoverage (points covered by multiple active sensors
+simultaneously).
+
+\subsection{Lifetime}
+%{\bf Lifetime}
+
+Various definitions exist for the lifetime of a sensor
+network~\cite{die09}. The main definitions proposed in the literature are
+related to the remaining energy of the nodes or to the coverage percentage.
+The lifetime of the network is mainly defined as the amount
+of time during which the network can satisfy its coverage objective (the
+amount of time that the network can cover a given percentage of its
+area or targets of interest). In this work, we assume that the network
+is alive until all nodes have been drained of their energy or the
+sensor network becomes disconnected, and we measure the coverage ratio
+during the WSN lifetime. Network connectivity is important because an
+active sensor node without connectivity towards a base station cannot
+transmit information on an event in the area that it monitors.
+
+\subsection{Activity scheduling}
+%{\bf Activity scheduling}
+
+Activity scheduling is to schedule the activation and deactivation of
+sensor nodes. The basic objective is to decide which sensors are in
+what states (active or sleeping mode) and for how long, so that the
+application coverage requirement can be guaranteed and the network
+lifetime can be prolonged. Various approaches, including centralized,
+distributed, and localized algorithms, have been proposed for activity
+scheduling. In distributed algorithms, each node in the network
+autonomously makes decisions on whether to turn on or turn off itself
+only using local neighbor information. In centralized algorithms, a
+central controller (a node or base station) informs every sensors of
+the time intervals to be activated.
+
+\subsection{Distributed approaches}
+%{\bf Distributed approaches}
+
+Some distributed algorithms have been developed
+in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02} to perform the
+scheduling. Distributed algorithms typically operate in rounds for
+a predetermined duration. At the beginning of each round, a sensor
+exchanges information with its neighbors and makes a decision to either
+remain turned on or to go to sleep for the round. This decision is
+basically made on simple greedy criteria like the largest uncovered
+area \cite{Berman05efficientenergy}, maximum uncovered targets
+\cite{1240799}. In \cite{Tian02}, the scheduling scheme is divided
+into rounds, where each round has a self-scheduling phase followed by
+a sensing phase. Each sensor broadcasts a message containing the node ID
+and the node location to its neighbors at the beginning of each round. A
+sensor determines its status by a rule named off-duty eligible rule,
+which tells him to turn off if its sensing area is covered by its
+neighbors. A back-off scheme is introduced to let each sensor delay
+the decision process with a random period of time, in order to avoid
+simultaneous conflicting decisions between nodes and lack of coverage on any area.
+\cite{Prasad:2007:DAL:1782174.1782218} defines a model for capturing
+the dependencies between different cover sets and proposes localized
+heuristic based on this dependency. The algorithm consists of two
+phases, an initial setup phase during which each sensor computes and
+prioritizes the covers and a sensing phase during which each sensor
+first decides its on/off status, and then remains on or off for the
+rest of the duration. 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 its perimeter coverage
+computed through the k-Non-Unit-disk coverage algorithm proposed in
+\cite{Huang:2003:CPW:941350.941367}.
+
+Some other approaches do not consider a synchronized and predetermined
+period of time where the sensors are active or not. Indeed, each
+sensor maintains its own timer and its wake-up time is randomized
+\cite{Ye03} or regulated \cite{cardei05} over time.
