distributed among sensor nodes in each subregion. A sensor node which runs LiCO
protocol repeats periodically four stages: information exchange, leader
election, optimization decision, and sensing. More precisely, the scheduling of
-nodes activities (sleep/wake up duty cycles) is achieved in each subregion by a
+nodes activities (sleep/wake up duty cycles) is achieved in each subregion by a
leader selected after cooperation between nodes within the same subregion. The
novelty of approach lies essentially in the formulation of a new mathematical
optimization model based on perimeter coverage level to schedule sensors
\IEEEpeerreviewmaketitle
-\section{\uppercase{Introduction}}
+\section{Introduction}
\label{sec:introduction}
\noindent The continuous progress in Micro Electro-Mechanical Systems (MEMS) and
of sensing areas can be exploited to schedule alternatively some sensors in a
low power sleep mode and thus save energy. Overall, the main question that must
be answered is: how to extend the lifetime coverage of a WSN as long as possible
-while ensuring a high level of coverage? So, this last years many
+while ensuring a high level of coverage? So, this last years many
energy-efficient mechanisms have been suggested to retain energy and extend the
lifetime of the WSNs~\cite{rault2014energy}.
decisions, followed by a sensing phase where one cover set is in charge of the
sensing task.}
-% MICHEL TO BE CONTINUED FROM HERE
-Various approaches, including centralized, or distributed algorithms, have been
-proposed to extend the network lifetime. 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
+Various centralized and distributed approaches, or even a mixing of these two
+concepts, have been proposed to extend the network lifetime. In distributed
+algorithms~\cite{yangnovel,ChinhVu,qu2013distributed} each sensors decides of
+its own activity scheduling after an information exchange with its neighbors.
+The main interest of a such approach is to avoid long range communications and
+thus to reduce the energy dedicated to the communications. Unfortunately, since
+each node has only information on its immediate neighbors (usually the one-hop
+ones) it may take a bad decision leading to a global suboptimal solution.
+Conversely, 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 and the amount
-of information can be huge. {\it In order to be suitable for large-scale
- network, in the LiCO protocol, the area of interest is divided into several
- smaller subregions, and in each one, a node called the leader is in charge for
- selecting the active sensors for the current period.}
-
-A large variety of coverage scheduling algorithms has been developed. 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
+provide nearly or close to optimal solution since the algorithm has a global
+view of the whole network. The disadvantage of a centralized method is obviously
+its high cost in communications needed to transmit to a single node, the base
+station which will globally schedule nodes activities, data from all the other
+sensor nodes in the area. The price in communications can be very huge since
+long range communications will be needed. In fact the larger the WNS, the higher
+the communication and thus energy cost. {\it In order to be suitable for
+ large-scale networks, in the LiCO protocol the area of interest is divided
+ into several smaller subregions, and in each one, a node called the leader is
+ in charge for selecting the active sensors for the current period. Thus our
+ protocol is scalable and a globally distributed method, whereas it is
+ centralized in each subregion.}
+
+Various coverage scheduling algorithms have been developed this last years.
+Many of them, 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
\cite{berman04,zorbas2010solving}. Other approaches are based on mathematical
programming formulations~\cite{cardei2005energy,5714480,pujari2011high,Yang2014}
-and dedicated techniques (solving with a branch-and-bound algorithms available
-in optimization solver). The problem is formulated as an optimization problem
+and dedicated techniques (solving with a branch-and-bound algorithm 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
also been
used~\cite{castano2013column,rossi2012exact,deschinkel2012column}. {\it In LiCO
- protocol, each leader, in each subregion, solves an integer program with the
- double objective consisting in minimizing the overcoverage and the
- undercoverage of the perimeter of each sensor.
-}
-
+ protocol, each leader, in charge of a subregion, solves an integer program
+ which has a twofold objective: minimize the overcoverage and the undercoverage
+ of the perimeter of each sensor.}
%\noindent Recently, the coverage problem has been received a high attention, which concentrates on how the physical space could be well monitored after the deployment. Coverage is one of the Quality of Service (QoS) parameters in WSNs, which is highly concerned with power depletion~\cite{zhu2012survey}. Most of the works about the coverage protocols have been suggested in the literature focused on three types of the coverage in WSNs~\cite{mulligan2010coverage}: the first, area coverage means that each point in the area of interest within the sensing range of at least one sensor node; the second, target coverage in which a fixed set of targets need to be monitored; the third, barrier coverage refers to detect the intruders crossing a boundary of WSN. The work in this paper emphasized on the area coverage, so, some area coverage protocols have been reviewed in this section, and the shortcomings of reviewed approaches are being summarized.
%\uppercase{\textbf{Our Protocol}}. In this paper, a Lifetime Coverage Optimization Protocol, called (LiCO) in WSNs is suggested. The sensing field is divided into smaller subregions by means of divide-and-conquer method, and a LiCO protocol is distributed in each sensor in the subregion. The network lifetime in each subregion is divided into periods, each period includes 4 stages: Information Exchange, Leader election, decision based activity scheduling optimization, and sensing. The leaders are elected in an independent, asynchronous, and distributed way in all the subregions of the WSN. After that, energy-efficient activity scheduling mechanism based new optimization model is performed by each leader in the subregions. This optimization model is based on the perimeter coverage model in order to producing the optimal cover set of active sensors, which are taken the responsibility of sensing during the current period. LiCO protocol merges between two energy efficient mechanisms, which are used the main advantages of the centralized and distributed approaches and avoids the most of their disadvantages.
-
\section{ The LiCO Protocol Description}
\label{sec:The LiCO Protocol Description}
-\noindent In this section, we describe our Lifetime Coverage Optimization Protocol which is called LiCO in more detail.
+
+\noindent In this section, we describe in details our Lifetime Coverage
+Optimization protocol. First we present the assumptions we made and the models
+we considered (in particular the perimeter coverage one), second we describe the
+background idea of our protocol, and third we give the outline of the algorithm
+executed by each node.
+
% It is based on two efficient-energy mechanisms: the first, is partitioning the sensing field into smaller subregions, and one leader is elected for each subregion; the second, a sensor activity scheduling based new optimization model so as to produce the optimal cover set of active sensors for the sensing stage during the period. Obviously, these two mechanisms can be contribute in extend the network lifetime coverage efficiently.
%Before proceeding in the presentation of the main ideas of the protocol, we will briefly describe the perimeter coverage model and give some necessary assumptions and definitions.
-\subsection{ Assumptions and Models}
-\noindent A WSN consisting of $J$ stationary sensor nodes randomly and uniformly distributed in a bounded sensor field is considered. The wireless sensors are deployed in high density to ensure initially a high coverage ratio of the interested area. We assume that all the sensor nodes are homogeneous in terms of communication, sensing, and processing capabilities and heterogeneous in term of energy supply. The location information is available to the sensor node either through hardware such as embedded GPS or through location discovery algorithms. We assume that each sensor node can directly transmit its measurements to a mobile sink node. For example, a sink can be an unmanned aerial vehicle (UAV) flying regularly over the sensor field to collect measurements from sensor nodes. A mobile sink node collects the measurements and transmits them to the base station. We consider a boolean disk coverage model which is the most widely used sensor coverage model in the literature. Each sensor has a constant sensing range $R_s$. All space points within a disk centered at the sensor with the radius of the sensing range is said to be covered by this sensor. We also assume that the communication range $R_c \geq 2R_s$. In fact, Zhang and Zhou~\cite{Zhang05} proved that if the transmission range fulfills the previous hypothesis, a complete coverage of a convex area implies connectivity among the working nodes in the active mode.
-
-\indent LiCO protocol uses the perimeter-coverage model which states in ~\cite{huang2005coverage} as following: The sensor is said to be perimeter covered if all the points on its perimeter are covered by at least one sensor other than itself. Huang and Tseng in \cite{huang2005coverage} proves that a network area is $k$-covered if and only if each sensor in the network is $k$-perimeter-covered.
+\subsection{Assumptions and Models}
+
+\noindent A WSN consisting of $J$ stationary sensor nodes randomly and uniformly
+distributed in a bounded sensor field is considered. The wireless sensors are
+deployed in high density to ensure initially a high coverage ratio of the area
+of interest. We assume that all the sensor nodes are homogeneous in terms of
+communication, sensing, and processing capabilities and heterogeneous from
+energy provision point of view. The location information is available to a
+sensor node either through hardware such as embedded GPS or location discovery
+algorithms. We assume that each sensor node can directly transmit its
+measurements to a mobile sink node. For example, a sink can be an unmanned
+aerial vehicle (UAV) flying regularly over the sensor field to collect
+measurements from sensor nodes. A mobile sink node collects the measurements and
+transmits them to the base station. We consider a Boolean disk coverage model,
+which is the most widely used sensor coverage model in the literature, and all
+sensor nodes have a constant sensing range $R_s$. Thus, all the space points
+within a disk centered at a sensor with a radius equal to the sensing range are
+said to be covered by this sensor. We also assume that the communication range
+$R_c$ satisfies $R_c \geq 2 \cdot R_s$. In fact, Zhang and Zhou~\cite{Zhang05}
+proved that if the transmission range fulfills the previous hypothesis, a
+complete coverage of a convex area implies connectivity among active nodes.
+
+\indent LiCO protocol uses the same perimeter-coverage model than Huang and
+Tseng in~\cite{huang2005coverage}. It can be expressed as follows: a sensor is
+said to be perimeter covered if all the points on its perimeter are covered by
+at least one sensor other than itself. They proved that a network area is
+$k$-covered if and only if each sensor in the network is $k$-perimeter-covered.
%According to this model, we named the intersections among the sensor nodes in the sensing field as intersection points. Instead of working with the coverage area, we consider for each sensor a set of intersection points which are determined by using perimeter-coverage model.
-Figure~\ref{pcmfig} illuminates the perimeter coverage of the sensor node $0$. On this figure, sensor $0$ has $9$ neighbors. We report for each sensor $i$ having an intersection with sensor $0$, the two intersection points, $iL$ for left point and $iR$ for right point. These intersections points subdivide the perimeter of the sensor $0$ (the perimeter of the disk covered by the sensor) into portions called segments.
+Figure~\ref{pcm2sensors}(a) shows the coverage of sensor node~$0$. On this
+figure, we can see that sensor~$0$ has nine neighbors and we have reported on
+its perimeter (the perimeter of the disk covered by the sensor) for each
+neighbor the two points resulting from intersection of the two sensing
+areas. These points are denoted for neighbor~$i$ by $iL$ and $iR$, respectively
+for left and right from neighbor point of view. The resulting couples of
+intersection points subdivide the perimeter of sensor~$0$ into portions called
+arcs.
\begin{figure}[ht!]
-\centering
-\includegraphics[width=75mm]{pcm.jpg}
-\caption{Perimeter coverage of sensor node 0}
-\label{pcmfig}
+ \centering
+ \begin{tabular}{@{}cr@{}}
+ \includegraphics[width=75mm]{pcm.jpg} & \raisebox{3.25cm}{(a)}
+ \\ \includegraphics[width=75mm]{twosensors.jpg} & \raisebox{2.75cm}{(b)}
+ \end{tabular}
+ \caption{Perimeter coverage of sensor node 0 (a) and finding the arc of $u$'s
+ perimeter covered by $v$.}
+ \label{pcm2sensors}
\end{figure}
-Figure~\ref{twosensors} demonstrates the way of locating the left and right points of a segment for a sensor node $u$ covered by a sensor node $v$. This figure assumes that the neighbor sensor node $v$ is located on the west of a sensor $u$. It is assumed that the two sensor nodes $v$ and $u$ are located in the positions $(v_x,v_y)$ and $(u_x,u_y)$, respectively. The distance between $v$ and $u$ is computed by $Dist(u,v) = \sqrt{\vert u_x - v_x \vert^2 + \vert u_y - v_y \vert^2}$. The angle $\alpha$ is computed through the formula $\alpha = arccos \left(\dfrac{Dist(u,v)}{2R_s} \right)$. So, the arch of sensor $u$ falling in the angle $[\pi - \alpha,\pi + \alpha]$, is said to be perimeter-covered by sensor node $v$.
-
-The left and right points of each segment are placed on the line segment $[0,2\pi]$. Figure~\ref{pcmfig} illustrates the segments for the 9 neighbors of sensor $0$. The points reported on the line segment $[0,2\pi]$ separates it in intervals as shown in figure~\ref{expcm}. For example, for each neighboring sensor of sensor 0, place the points $\alpha^ 1_L$, $\alpha^ 1_R$, $\alpha^ 2_L$, $\alpha^ 2_R$, $\alpha^ 3_L$, $\alpha^ 3_R$, $\alpha^ 4_L$, $\alpha^ 4_R$, $\alpha^ 5_L$, $\alpha^ 5_R$, $\alpha^ 6_L$, $\alpha^ 6_R$, $\alpha^ 7_L$, $\alpha^ 7_R$, $\alpha^ 8_L$, $\alpha^ 8_R$, $\alpha^ 9_L$, and $\alpha^ 9_R$; on the line segment $[0,2\pi]$, and then sort all these points in an ascending order into a list. Traverse the line segment $[0,2\pi]$ by visiting each point in the sorted list from left to right and determine the coverage level of each interval of the sensor 0 (see figure \ref{expcm}). For each interval, we sum up the number of parts of segments, and we deduce a level of coverage for each interval. For instance, the interval delimited by the points $5L$ and $6L$ contains three parts of segments. That means that this part of the perimeter of the sensor $0$ may be covered by three sensors, sensor $0$ itself and sensors $2$ and $5$. The level of coverage of this interval may reach $3$ if all previously mentioned sensors are active. Let say that sensors $0$, $2$ and $5$ are involved in the coverage of this interval. Table~\ref{my-label} summarizes the level of coverage for each interval and the sensors involved in for sensor node 0 in figure~\ref{pcmfig}.
+Figure~\ref{pcm2sensors}(b) describes the geometric information used to find the
+locations of the left and right points of an arc on the perimeter of a sensor
+node~$u$ covered by a sensor node~$v$. Node~$s$ is supposed to be located on the
+west side of sensor~$u$, with the following respective coordinates in the
+sensing area~: $(v_x,v_y)$ and $(u_x,u_y)$. From the previous coordinates we can
+compute the euclidean distance between nodes~$u$ and $v$: $Dist(u,v)=\sqrt{\vert
+ u_x - v_x \vert^2 + \vert u_y-v_y \vert^2}$, while the angle~$\alpha$ is
+obtained through the formula $\alpha = arccos \left(\dfrac{Dist(u,v)}{2R_s}
+\right)$. So, the arc on the perimeter of node~$u$ defined by the angular
+interval $[\pi - \alpha,\pi + \alpha]$ is said to be perimeter-covered by sensor
+node $v$.
+
+Every couple of intersection points is placed on the angular interval $[0,2\pi]$
+in a counterclockwise manner, leading to a partitioning of the interval.
+Figure~\ref{pcm2sensors}(a) illustrates the arcs for the nine neighbors of
+sensor $0$ and figure~\ref{expcm} gives the position of the corresponding arcs
+in the interval $[0,2\pi]$. More precisely, we can see that the points are
+ordered according to the measures of the angles defined by their respective
+positions. The intersection points are then visited one after another, starting
+from first intersection point after point~zero, and the maximum level of
+coverage is determined for each interval defined by two successive points. The
+maximum level of coverage is equal to the number of overlapping arcs. For
+example, between~$5L$ and~$6L$ the maximum level of coverage is equal to $3$
+(the value is highlighted in yellow at the bottom of figure~\ref{expcm}), which
+means that at most 2~neighbors can cover the perimeter in addition to node $0$.
+Table~\ref{my-label} summarizes for each coverage interval the maximum level of
+coverage and the sensor nodes covering the perimeter. The example discussed
+above is thus given by the sixth line of the table.
+
+%The points reported on the line segment $[0,2\pi]$ separates it in intervals as shown in figure~\ref{expcm}. For example, for each neighboring sensor of sensor 0, place the points $\alpha^ 1_L$, $\alpha^ 1_R$, $\alpha^ 2_L$, $\alpha^ 2_R$, $\alpha^ 3_L$, $\alpha^ 3_R$, $\alpha^ 4_L$, $\alpha^ 4_R$, $\alpha^ 5_L$, $\alpha^ 5_R$, $\alpha^ 6_L$, $\alpha^ 6_R$, $\alpha^ 7_L$, $\alpha^ 7_R$, $\alpha^ 8_L$, $\alpha^ 8_R$, $\alpha^ 9_L$, and $\alpha^ 9_R$; on the line segment $[0,2\pi]$, and then sort all these points in an ascending order into a list. Traverse the line segment $[0,2\pi]$ by visiting each point in the sorted list from left to right and determine the coverage level of each interval of the sensor 0 (see figure \ref{expcm}). For each interval, we sum up the number of parts of segments, and we deduce a level of coverage for each interval. For instance, the interval delimited by the points $5L$ and $6L$ contains three parts of segments. That means that this part of the perimeter of the sensor $0$ may be covered by three sensors, sensor $0$ itself and sensors $2$ and $5$. The level of coverage of this interval may reach $3$ if all previously mentioned sensors are active. Let say that sensors $0$, $2$ and $5$ are involved in the coverage of this interval. Table~\ref{my-label} summarizes the level of coverage for each interval and the sensors involved in for sensor node 0 in figure~\ref{pcm2sensors}(a).
% to determine the level of the perimeter coverage for each left and right point of a segment.
-\begin{figure}[ht!]
-\centering
-\includegraphics[width=75mm]{twosensors.jpg}
-\caption{Locating the segment of $u$$\rq$s perimeter covered by $v$.}
-\label{twosensors}
-\end{figure}
-
-\begin{figure}[ht!]
+\begin{figure*}[ht!]
\centering
-\includegraphics[width=75mm]{expcm.pdf}
-\caption{ Coverage levels for sensor node $0$.}
+\includegraphics[width=137.5mm]{expcm.pdf}
+\caption{Maximum coverage levels for perimeter of sensor node $0$.}
\label{expcm}
-\end{figure}
-
-
-
-
-
-
-
-
+\end{figure*}
%For example, consider the sensor node $0$ in figure~\ref{pcmfig}, which has 9 neighbors. Figure~\ref{expcm} shows the perimeter coverage level for all left and right points of a segment that covered by a neighboring sensor nodes. Based on the figure~\ref{expcm}, the set of sensors for each left and right point of the segments illustrated in figure~\ref{ex2pcm} for the sensor node 0.
%The optimization algorithm that used by LiCO protocol based on the perimeter coverage levels of the left and right points of the segments and worked to minimize the number of sensor nodes for each left or right point of the segments within each sensor node. The algorithm minimize the perimeter coverage level of the left and right points of the segments, while, it assures that every perimeter coverage level of the left and right points of the segments greater than or equal to 1.
-In LiCO protocol, scheduling of sensor nodes'activities is formulated with an integer program based on coverage intervals and is detailed in section~\ref{cp}.
-
-In the case of sensor node, which has a part of its sensing range outside the border of the WSN sensing field as in figure~\ref{ex4pcm}, the coverage level for this segment is set to $\infty$, and the corresponding interval will not be taken into account by the optimization algorithm.
-\begin{figure}[ht!]
+In LiCO protocol, scheduling of sensor nodes activities is formulated with an
+integer program based on coverage intervals. The formulation of the coverage
+optimization problem is detailed in~section~\ref{cp}. Note that when a sensor
+node has a part of its sensing range outside the WSN sensing field, as in
+figure~\ref{ex4pcm}, the maximum coverage level for this arc is set to $\infty$
+and the corresponding interval will not be taken into account by the
+optimization algorithm.
+
+\begin{figure}[t!]
\centering
-\includegraphics[width=75mm]{ex4pcm.jpg}
-\caption{Part of sensing range outside the the border of WSN sensing field.}
+\includegraphics[width=62.5mm]{ex4pcm.jpg}
+\caption{Sensing range outside the WSN's area of interest.}
\label{ex4pcm}
\end{figure}
%Figure~\ref{ex5pcm} gives an example to compute the perimeter coverage levels for the left and right points of the segments for a sensor node $0$, which has a part of its sensing range exceeding the border of the sensing field of WSN, and it has a six neighbors. In figure~\ref{ex5pcm}, the sensor node $0$ has two segments outside the border of the network sensing field, so the left and right points of the two segments called $-1L$, $-1R$, $-2L$, and $-2R$.
%\label{ex5pcm}
%\end{figure}
+% MICHEL TO BE CONTINUED FROM HERE
\subsection{The Main Idea}
\noindent The area of interest can be divided into smaller areas called subregions and
