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-\title{Multiperiod Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}
+\title{Multiround Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}
%% use optional labels to link authors explicitly to addresses:
%% \author[label1,label2]{}
%continuously and effectively when monitoring a certain area (or
%region) of interest.
Coverage and lifetime are two paramount problems in Wireless Sensor Networks
-(WSNs). In this paper, a method called Multiperiod Distributed Lifetime Coverage
+(WSNs). In this paper, a method called Multiround Distributed Lifetime Coverage
Optimization protocol (MuDiLCO) is proposed to maintain the coverage and to
improve the lifetime in wireless sensor networks. The area of interest is first
divided into subregions and then the MuDiLCO protocol is distributed on the
military, and industrial applications~\cite{Akyildiz02}.
On the one hand sensor nodes run on batteries with limited capacities, and it is
-often costly or simply impossible to replace and/or recharge batteries,
+often costly or simply impossible to replace and/or recharge batteries,
especially in remote and hostile environments. Obviously, to achieve a long life
-of the network it is important to conserve battery power. Therefore, lifetime
+of the network it is important to conserve battery power. Therefore, lifetime
optimization is one of the most critical issues in wireless sensor networks. On
-the other hand we must guarantee coverage over the area of interest. To fulfill
-these two objectives, the main idea is to take advantage of overlapping sensing
+the other hand we must guarantee coverage over the area of interest. To fulfill
+these two objectives, the main idea is to take advantage of overlapping sensing
regions to turn-off redundant sensor nodes and thus save energy. In this paper,
-we concentrate on the area coverage problem, with the objective of maximizing
+we concentrate on the area coverage problem, with the objective of maximizing
the network lifetime by using an optimized multirounds scheduling.
% One of the major scientific research challenges in WSNs, which are addressed by a large number of literature during the last few years is to design energy efficient approaches for coverage and connectivity in WSNs~\cite{conti2014mobile}. The coverage problem is one of the
The choice of non-disjoint or disjoint cover sets (sensors participate or not in
many cover sets) can be added to the above list.
% The independency in the cover set (i.e. whether the cover sets are disjoint or non-disjoint) \cite{zorbas2010solving} is another design choice that can be added to the above list.
+
+\subsection{Centralized Approaches}
+The major approach is to divide/organize the sensors into a suitable number of
+set covers where each set completely covers an interest region and to activate
+these set covers successively. The centralized algorithms always provide nearly
+or close to optimal solution since the algorithm has global view of the whole
+network. Note that centralized algorithms have the advantage of requiring very
+low processing power from the sensor nodes, which usually have limited
+processing capabilities. The main drawback of this kind of approach is its
+higher cost in communications, since the node that will take the decision needs
+information from all the sensor nodes. Moreover, centralized approaches usually
+suffer from the scalability problem, making them less competitive as the network
+size increases.
+
+The first algorithms proposed in the literature consider that the cover sets are
+disjoint: a sensor node appears in exactly one of the generated cover sets~\cite{abrams2004set,cardei2005improving,Slijepcevic01powerefficient}.
+
+
+In the case of non-disjoint algorithms \cite{pujari2011high}, sensors may
+participate in more than one cover set. In some cases, this may prolong the
+lifetime of the network in comparison to the disjoint cover set algorithms, but
+designing algorithms for non-disjoint cover sets generally induces a higher
+order of complexity. Moreover, in case of a sensor's failure, non-disjoint
+scheduling policies are less resilient and less reliable because a sensor may be
+involved in more than one cover sets. For instance, the proposed work in ~\cite{cardei2005energy, berman04}
+
+
+
+
+In~\cite{yang2014maximum}, the authors have proposed a linear programming
+approach for selecting the minimum number of working sensor nodes, in order to
+as to preserve a maximum coverage and extend lifetime of the network. Cheng et
+al.~\cite{cheng2014energy} have defined a heuristic algorithm called Cover Sets
+Balance (CSB), which choose a set of active nodes using the tuple (data coverage
+range, residual energy). Then, they have introduced a new Correlated Node Set
+Computing (CNSC) algorithm to find the correlated node set for a given node.
+After that, they proposed a High Residual Energy First (HREF) node selection
+algorithm to minimize the number of active nodes so as to prolong the network
+lifetime. Various centralized methods based on column generation approaches have
+also been proposed~\cite{castano2013column,rossi2012exact,deschinkel2012column}.
+
+
+
+
+
+\subsection{Distributed approaches}
+%{\bf Distributed approaches}
+In distributed and localized coverage algorithms, the required computation to
+schedule the activity of sensor nodes will be done by the cooperation among
+neighboring nodes. These algorithms may require more computation power for the
+processing by the cooperating sensor nodes, but they are more scalable for large
+WSNs. Localized and distributed algorithms generally result in non-disjoint set
+covers.
+
+Some distributed algorithms have been developed
+in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02, yardibi2010distributed, prasad2007distributed,Misra}
+to perform the scheduling so as to preserve coverage. 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} or maximum uncovered targets
+\cite{lu2003coverage}. The authors in \cite{yardibi2010distributed} have developed a Distributed
+Adaptive Sleep Scheduling Algorithm (DASSA) for WSNs with partial coverage.
+DASSA does not require location information of sensors while maintaining
+connectivity and satisfying a user defined coverage target. In DASSA, nodes use
+the residual energy levels and feedback from the sink for scheduling the
+activity of their neighbors. This feedback mechanism reduces the randomness in
+scheduling that would otherwise occur due to the absence of location
+information. In \cite{ChinhVu}, the author have proposed a novel distributed
+heuristic, called Distributed Energy-efficient Scheduling for k-coverage (DESK),
+which ensures that the energy consumption among the sensors is balanced and the
+lifetime maximized while the coverage requirement is maintained. This heuristic
+works in rounds, requires only one-hop neighbor information, and each sensor
+decides its status (active or sleep) based on the perimeter coverage model
+proposed in \cite{Huang:2003:CPW:941350.941367}.
+
+%Our Work, which is presented in~\cite{idrees2014coverage} proposed 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 consider only distributing the coverage protocol over two subregions.
+
+The works presented in \cite{Bang, Zhixin, Zhang} focuses on coverage-aware,
+distributed energy-efficient, and distributed clustering methods respectively,
+which aims to extend the network lifetime, while the coverage is ensured. More recently, Shibo et al. \cite{Shibo} have expressed the coverage
+problem as a minimum weight submodular set cover problem and proposed a
+Distributed Truncated Greedy Algorithm (DTGA) to solve it. They take advantage
+from both temporal and spatial correlations between data sensed by different
+sensors, and leverage prediction, to improve the lifetime. In
+\cite{xu2001geography}, Xu et al. have proposed an algorithm, called
+Geographical Adaptive Fidelity (GAF), 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.
+
+Some other approaches (outside the scope of our work) 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{cardei2005maximum} over time.
+
+The MuDiLCO protocol (for Multiround Distributed Lifetime Coverage Optimization
+protocol) presented in this paper is an extension of the approach introduced
+in~\cite{idrees2014coverage}. In~\cite{idrees2014coverage}, the protocol is
+deployed over only two subregions. Simulation results have shown that it was
+more interesting to divide the area into several subregions, given the
+computation complexity. Compared to our previous paper, in this one we study the
+possibility of dividing the sensing phase into multiple rounds and we also add
+an improved model of energy consumption to assess the efficiency of our
+approach.
+
+
+
+
+
+\iffalse
\subsection{Centralized Approaches}
%{\bf Centralized approaches}
lifetime. Various centralized methods based on column generation approaches have
also been proposed~\cite{castano2013column,rossi2012exact,deschinkel2012column}.
+
+
\subsection{Distributed approaches}
%{\bf Distributed approaches}
In distributed and localized coverage algorithms, the required computation to
not. Indeed, each sensor maintains its own timer and its wake-up time is
randomized \cite{Ye03} or regulated \cite{cardei2005maximum} over time.
-The MuDiLCO protocol (for Multiperiod Distributed Lifetime Coverage Optimization
+The MuDiLCO protocol (for Multiround Distributed Lifetime Coverage Optimization
protocol) presented in this paper is an extension of the approach introduced
in~\cite{idrees2014coverage}. In~\cite{idrees2014coverage}, the protocol is
deployed over only two subregions. Simulation results have shown that it was
an improved model of energy consumption to assess the efficiency of our
approach.
+
+
+
+\fi
%The main contributions of our MuDiLCO Protocol can be summarized as follows:
%(1) The high coverage ratio, (2) The reduced number of active nodes, (3) The distributed optimization over the subregions in the area of interest, (4) The distributed dynamic leader election at each round based on some priority factors that led to energy consumption balancing among the nodes in the same subregion, (5) The primary point coverage model to represent each sensor node in the network, (6) 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 for a multirounds to take the mission of the coverage in each subregion, (7) The very low energy consumption, (8) The higher network lifetime.
%\section{Preliminaries}
%minimizing overcoverage (points covered by multiple active sensors
%simultaneously).
-%In this section, we introduce a Multiperiod Distributed Lifetime Coverage Optimization protocol, which is called MuDiLCO. It is distributed on each subregion in the area of interest. It is based on two efficient techniques: network
+%In this section, we introduce a Multiround Distributed Lifetime Coverage Optimization protocol, which is called MuDiLCO. It is distributed on each subregion in the area of interest. It is based on two efficient techniques: network
%leader election and sensor activity scheduling for coverage preservation and energy conservation continuously and efficiently to maximize the lifetime in the network.
%The main features of our MuDiLCO protocol:
%i)It divides the area of interest into subregions by using divide-and-conquer concept, ii)It requires only the information of the nodes within the subregion, iii) it divides the network lifetime into periods, which consists in round(s), iv)It based on the autonomous distributed decision by the nodes in the subregion to elect the Leader, v)It apply the activity scheduling based optimization on the subregion, vi) it achieves an energy consumption balancing among the nodes in the subregion by selecting different nodes as a leader during the network lifetime, vii) It uses the optimization to select the best representative non-disjoint sets of sensors in the subregion by optimize the coverage and the lifetime over the area of interest, viii)It uses our proposed primary point coverage model, which represent the sensing range of the sensor as a set of points, which are used by the our optimization algorithm, ix) It uses a simple energy model that takes communication, sensing and computation energy consumptions into account to evaluate the performance of our Protocol.
\subsection{Background idea}
%%RC : we need to clarify the difference between round and period. Currently it seems to be the same (for me at least).
The area of interest can be divided using the divide-and-conquer strategy into
-smaller areas, called subregions, and then our MuDiLCO protocol will be
+smaller areas, called subregions, and then our MuDiLCO protocol will be
implemented in each subregion in a distributed way.
As can be seen in Figure~\ref{fig2}, our protocol works in periods fashion,
where each is divided into 4 phases: Information~Exchange, Leader~Election,
Decision, and Sensing. Each sensing phase may be itself divided into $T$ rounds
-and for each round a set of sensors (said a cover set) is responsible for the
-sensing task.
+and for each round a set of sensors (said a cover set) is responsible for the
+sensing task. A multiround optimization process executed in each period after information exchange and leader election in order to produce a $T$ cover sets of sensors to take the mission of sensing for $T$ rounds.
\begin{figure}[ht!]
\centering \includegraphics[width=100mm]{Modelgeneral.pdf} % 70mm
\caption{The MuDiLCO protocol scheme executed on each node}
% set cover responsible for the sensing task.
%For each round a set of sensors (said a cover set) is responsible for the sensing task.
-This protocol is reliable against an unexpected node failure, because it works
+This protocol is reliable against an unexpected node failure, because it works
in periods.
%%RC : why? I am not convinced
- On the one hand, if a node failure is detected before making the
-decision, the node will not participate to this phase, and, on the other hand,
-if the node failure occurs after the decision, the sensing task of the network
-will be temporarily affected: only during the period of sensing until a new
+ On the one hand, if a node failure is detected before making the
+decision, the node will not participate to this phase, and, on the other hand,
+if the node failure occurs after the decision, the sensing task of the network
+will be temporarily affected: only during the period of sensing until a new
period starts.
%%RC so if there are at least one failure per period, the coverage is bad...
cope with this problem, the field of sensing is divided into smaller subregions
using the concept of divide-and-conquer method, and then we propose a protocol
which optimizes coverage and lifetime performances in each subregion. Our
-protocol, called MuDiLCO (Multiperiod Distributed Lifetime Coverage
+protocol, called MuDiLCO (Multiround Distributed Lifetime Coverage
Optimization) combines two efficient techniques: network leader election and
sensor activity scheduling.
%, where the challenges
% use section* for acknowledgement
\section*{Acknowledgment}
-As a Ph.D. student, Ali Kadhum IDREES would like to gratefully acknowledge the
+This work is partially funded by the Labex ACTION program (contract ANR-11-LABX-01-01).
+As a Ph.D. student, Ali Kadhum IDREES would like to gratefully acknowledge the
University of Babylon - Iraq for the financial support, Campus France (The
French national agency for the promotion of higher education, international
-student services, and international mobility), and the University of
-Franche-Comt\'e - France for all the support in France. This work is partially funded by the Labex ACTION program (contract ANR-11-LABX-01-01).
+student services, and international mobility).%, and the University ofFranche-Comt\'e - France for all the support in France.
+
%% TeX file.
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-\bibliography{biblio}
+\bibliography{article}
\end{document}
