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/wakeup 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
activities. Extensive simulation experiments have been performed using OMNeT++,
the discrete event simulator, to demonstrate that LiCO is capable to offer
-longer lifetime coverage for WSNs in comparison with some other protols.
+longer lifetime coverage for WSNs in comparison with some other protocols.
\end{abstract}
% Note that keywords are not normally used for peerreview papers.
military, and son~\cite{yick2008wireless}. Typically, a sensor node contains
three main components~\cite{anastasi2009energy}: a sensing unit able to measure
physical, chemical, or biological phenomena observed in the environment; a
-processing unit which will process and store the measurements which are
-collected; a radio communication unit for data transmission and receiving.
+processing unit which will process and store the collected measurements; a radio
+communication unit for data transmission and receiving.
The energy needed by an active sensor node to perform sensing, processing, and
communication is supplied by a power supply which is a battery. This battery has
%\uppercase{\textbf{Our contributions.}}
-% MICHEL - TO CONTINUED FROM HERE
This paper makes the following contributions.
\begin{enumerate}
-\item We devise a framework to schedules nodes to be activated
- alternatively, such that the network lifetime may be prolonged ans
- certain coverage requirement can still be met. This framework is
- based on the division of the area of interest into several smaller
- subregions; on the division of timeline into periods of equal
- length. One leader is elected for each subregion in an independent,
- distributed, and simultaneous way by the cooperation among the
- sensor nodes within each subregion, and this is similar to cluster
- architecture
-\item We propose a new mathematical optimization model. Instead of
- trying to cover a set of specified points/targets as in most of the
- methods proposed in the literature, we formulate an integer program
- based on perimeter coverage of each sensor. The model involves
- integer variables to capture the deviations between the actual level
- of coverage and the required level. And a weighted sum of these
- deviations is minimized.
-\item We conducted extensive simulation experiments using the discrete
- event simulator OMNeT++, to demonstrate the efficiency of our
- protocol, compared to two approaches found in the literature, DESK
- \cite{ChinhVu} and GAF \cite{xu2001geography}, and compared to our
- previous work using another optimization model for sensor scheduling
- \cite{Idrees2}.
+\item We devise a framework to schedule nodes to be activated alternatively such
+ that the network lifetime is prolonged while ensuring that a certain level of
+ coverage is preserved. A key idea in our framework is to exploit spatial an
+ temporal subdivision. On the one hand the area of interest if divided into
+ several smaller subregions and on the other hand the time line is divided into
+ periods of equal length. In each subregion the sensor nodes will cooperatively
+ choose a leader which will schedule nodes activities, and this grouping of
+ sensors is similar to typical cluster architecture.
+\item We propose a new mathematical optimization model. Instead of trying to
+ cover a set of specified points/targets as in most of the methods proposed in
+ the literature, we formulate an integer program based on perimeter coverage of
+ each sensor. The model involves integer variables to capture the deviations
+ between the actual level of coverage and the required level. So that an
+ optimal scheduling will be obtained by minimizing a weighted sum of these
+ deviations.
+\item We conducted extensive simulation experiments, using the discrete event
+ simulator OMNeT++, to demonstrate the efficiency of our protocol. We compared
+ our LiCO protocol to two approaches found in the literature:
+ DESK~\cite{ChinhVu} and GAF~\cite{xu2001geography}, and also to our previous
+ work published in~\cite{Idrees2} which is based on another optimization model
+ for sensor scheduling.
\end{enumerate}
-
%Two combined integrated energy-efficient techniques have been used by LiCO protocol in order to maximize the lifetime coverage in WSN: the first, by dividing the area of interest into several smaller subregions based on divide-and-conquer method and then one leader elected for each subregion in an independent, distributed, and simultaneous way by the cooperation among the sensor nodes within each subregion, and this similar to cluster architecture;
% the second, activity scheduling based new optimization model has been used to provide the optimal cover set that will take the mission of sensing during current period. This optimization algorithm is based on a perimeter-coverage model so as to optimize the shared perimeter among the sensors in each subregion, and this represents as a energu-efficient control topology mechanism in WSN.
-
-The remainder of the paper is organized as follows. The next section
-reviews the related work in the field. Section~\ref{sec:The LiCO
- Protocol Description} is devoted to the LiCO protocol
-Description. Section~\ref{cp} gives the coverage model formulation
-which is used to schedule the activation of sensors.
-Section~\ref{sec:Simulation Results and Analysis} presents simulations
-results. Finally, we give concluding remarks and some suggestions for
-future works in Section~\ref{sec:Conclusion and Future Works}.
+The rest of the paper is organized as follows. In the next section we review
+some related work in the field. Section~\ref{sec:The LiCO Protocol Description}
+is devoted to the LiCO protocol description and Section~\ref{cp} focuses on the
+coverage model formulation which is used to schedule the activation of sensor
+nodes. Section~\ref{sec:Simulation Results and Analysis} presents simulations
+results and discusses the comparison with other approaches. Finally, concluding
+remarks are drawn and some suggestions given for future works in
+Section~\ref{sec:Conclusion and Future Works}.
% that show that our protocol outperforms others protocols.
-\section{\uppercase{Related Literature}}
+\section{Related Literature}
\label{sec:Literature Review}
-
\noindent In this section, we summarize some related works regarding the
-coverage problem and distinguish our LiCO protocol from the works presented in
+coverage problem and distinguish our LiCO 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 \cite{Misra} where every point inside
-an area is to be monitored, target coverage \cite{yang2014novel} where the main
-objective is to cover only a finite number of discrete points called targets,
-and barrier coverage \cite{HeShibo}\cite{kim2013maximum} to prevent intruders
-from entering into the region of interest. In \cite{Deng2012} authors transform
-the area coverage problem to the target coverage problem taking into account the
-intersection points among disks of sensors nodes or between disk of sensor nodes
-and boundaries. In \cite{Huang:2003:CPW:941350.941367} authors prove that if the perimeters of sensors are sufficiently covered, the whole area is sufficiently covered and they provide an algorithm in $O(nd~log~d)$ time to compute the perimeter-coverage of each sensor ($d$ the maximum number of sensors that are neighboring to a sensor, $n$ the total number of sensors in the network). {\it In LiCO protocol, instead of determining the level of coverage of a set of discrete points, our optimization model is based on checking the perimeter-coverage of each sensor to activate a minimal number of sensors.}
-
-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,
+The most discussed coverage problems in literature can be classified in three
+categories~\cite{li2013survey} according to their respective monitoring
+objective. Hence, area coverage \cite{Misra} means that every point inside a
+fixed area must be monitored, while target coverage~\cite{yang2014novel} refer
+to the objective of coverage for a finite number of discrete points called
+targets, and barrier coverage~\cite{HeShibo}\cite{kim2013maximum} focuses on
+preventing intruders from entering into the region of interest. In
+\cite{Deng2012} authors transform the area coverage problem to the target
+coverage one taking into account the intersection points among disks of sensors
+nodes or between disk of sensor nodes and boundaries. In
+\cite{Huang:2003:CPW:941350.941367} authors prove that if the perimeters of
+sensors are sufficiently covered it will be the case for the whole area. They
+provide an algorithm in $O(nd~log~d)$ time to compute the perimeter-coverage of
+each sensor, where $d$ denotes the maximum number of sensors that are
+neighboring to a sensor and $n$ is the total number of sensors in the
+network. {\it In LiCO protocol, instead of determining the level of coverage of
+ a set of discrete points, our optimization model is based on checking the
+ perimeter-coverage of each sensor to activate a minimal number of sensors.}
+
+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
\cite{ling2009energy}. Active node selection is determined based on the problem
-requirements (e.g. area monitoring, connectivity, power efficiency). For
-instance, Jaggi et al. \cite{jaggi2006} address the problem of maximizing
-network lifetime by dividing sensors into the maximum number of disjoint subsets
-such that each subset can ensure both coverage and connectivity. A greedy
+requirements (e.g. area monitoring, connectivity, or power efficiency). For
+instance, Jaggi {\em et al.}~\cite{jaggi2006} address the problem of maximizing
+the lifetime by dividing sensors into the maximum number of disjoint subsets
+such that each subset can ensure both coverage and connectivity. A greedy
algorithm is applied once to solve this problem and the computed sets are
activated in succession to achieve the desired network lifetime. Vu
-\cite{chin2007}, Padmatvathy et al. \cite{pc10}, propose algorithms working in a
-periodic fashion where a cover set is computed at the beginning of each period.
-{\it Motivated by these works, LiCO 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.}
-
+\cite{chin2007}, Padmatvathy {\em et al.}~\cite{pc10}, propose algorithms
+working in a periodic fashion where a cover set is computed at the beginning of
+each period. {\it Motivated by these works, LiCO 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.}
+
+% MICHEL TO BE CONTINUED FROM HERE
Various approaches, including centralized, or distributed algorithms, have been
-proposed to extend the network lifetime. In distributed
+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
-algorithms~\cite{cardei2005improving,zorbas2010solving,pujari2011high} always
+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 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
+ 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
+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
+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
(maximization of the lifetime or number of cover sets) under target coverage and
-energy constraints. Column generation techniques, well-known and widely
+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.
-
+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.
}
