From: Karine Deschinkel Date: Fri, 6 Feb 2015 14:40:25 +0000 (+0100) Subject: ok X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/LiCO.git/commitdiff_plain/a6b75d539ec83b4958277b957744cb0eb7349aea ok --- diff --git a/IOPEXPORT_BIB.bib b/IOPEXPORT_BIB.bib new file mode 100644 index 0000000..d522035 --- /dev/null +++ b/IOPEXPORT_BIB.bib @@ -0,0 +1,14 @@ + + +@article{0031-9155-44-1-012, + author={Eva K Lee and Richard J Gallagher and David Silvern and Cheng-Shie Wuu and Marco Zaider}, + title={Treatment planning for brachytherapy: an integer programming model, two computational approaches and experiments with permanent prostate implant planning}, + journal={Physics in Medicine and Biology}, + volume={44}, + number={1}, + pages={145}, + url={http://stacks.iop.org/0031-9155/44/i=1/a=012}, + year={1999}, + abstract={An integer linear programming model is proposed as a framework for optimizing seed placement and dose distribution in brachytherapy treatment planning. The basic model involves using 0/1 indicator variables to describe the placement or non-placement of seeds in a prespecified three-dimensional grid of potential locations. The dose delivered to each point in a discretized representation of the diseased organ and neighbouring healthy tissue can then be modelled as a linear combination of the indicator variables. A system of linear constraints is imposed to attempt to keep the dose level at each point to within specified target bounds. Since it is physically impossible to satisfy all constraints simultaneously, each constraint uses a variable to either record when the target dose level is achieved, or to record the deviation from the desired level. These additional variables are embedded into an objective function to be optimized. Variations on this model are discussed and two computational approaches - a branch-and-bound algorithm and a genetic algorithm - for finding `optimal' seed placements are described. Results of computational experiments on a collection of prostate cancer cases are reported. The results indicate that both optimization algorithms are capable of producing good solutions within 5 to 15 min, and that small variations in model parameters can have a measurable effect on the dose distribution of the resulting plans.} +} + \ No newline at end of file diff --git a/LiCO_Journal-1.tex b/LiCO_Journal-1.tex new file mode 100644 index 0000000..cf40f32 --- /dev/null +++ b/LiCO_Journal-1.tex @@ -0,0 +1,617 @@ + +\documentclass[journal]{IEEEtran} + + +\ifCLASSINFOpdf +\else +\fi + + +\hyphenation{op-tical net-works semi-conduc-tor} +\usepackage{float} +\usepackage{epsfig} +\usepackage{calc} + \usepackage{times,amssymb,amsmath,latexsym} +\usepackage{graphics} +\usepackage{graphicx} +\usepackage{amsmath} +%\usepackage{txfonts} +\usepackage{algorithmic} +\usepackage[T1]{fontenc} +\usepackage{tikz} +%\usepackage{algorithm} +%\usepackage{algpseudocode} +%\usepackage{algorithmwh} +\usepackage{subfigure} +\usepackage{float} +\usepackage{xspace} +\usepackage[linesnumbered,ruled,vlined,commentsnumbered]{algorithm2e} +\usepackage{epsfig} +\usepackage{caption} +\usepackage{multicol} +\usepackage{times} +\usepackage{graphicx,epstopdf} +\epstopdfsetup{suffix=} +\DeclareGraphicsExtensions{.ps} +\usepackage{xspace} +\def\bsq#1{%both single quotes +\lq{#1}\rq} +\DeclareGraphicsRule{.ps}{pdf}{.pdf}{`ps2pdf -dEPSCrop -dNOSAFER #1 \noexpand\OutputFile} +\begin{document} + +\title{Lifetime Coverage Optimization Protocol in Wireless Sensor Networks} %LiCO Protocol + + + +\author{Ali Kadhum Idrees,~\IEEEmembership{} + Karine Deschinkel,~\IEEEmembership{} + Michel Salomon,~\IEEEmembership{} + and~Rapha\"el Couturier ~\IEEEmembership{} +\thanks{The authors are with FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comt\'e, Belfort, France. Email: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}} +%\thanks{J. Doe and J. Doe are with Anonymous University.}% <-this % stops a space +%\thanks{Manuscript received April 19, 2005; revised December 27, 2012.}} + +\markboth{IEEE Communications Letters,~Vol.~11, No.~4, December~2014}% +{Shell \MakeLowercase{\textit{et al.}}: Bare Demo of IEEEtran.cls for Journals} + + +\maketitle + + +\begin{abstract} + + + One fundamental issue in Wireless Sensor Networks (WSNs) is the lifetime coverage optimization, which reflects how well a WSN is covered by a wireless sensors so that the network lifetime can be maximized. In this paper, a Lifetime Coverage Optimization Protocol (LiCO) in WSNs is proposed. The network is logically divided into subregions using divide-and-conquer method. LiCO protocol is distributed in each sensor node in the subregion. The lifetime coverage is divided into four stages: Information exchange, Leader Election, Optimization Decision, and Sensing. The optimization decision is made at each subregion, by a leader, who his election comes from the cooperation of the sensor nodes within the same subregion of WSN. A new mathematical optimization model is proposed to optimize the lifetime coverage in each subregion. Extensive simulation experiments have been performed using OMNeT++, the discrete event simulator, to demonstrate that LiCO is capable to extend the lifetime coverage of WSN as longer time as possible in comparison with some other protocols. + +\end{abstract} + + +% Note that keywords are not normally used for peerreview papers. +\begin{IEEEkeywords} +Wireless Sensor Networks, Area Coverage, Network lifetime, Optimization, Scheduling. +\end{IEEEkeywords} + + +\IEEEpeerreviewmaketitle + + + + + +\section{\uppercase{Introduction}} +\label{sec:introduction} +\noindent The great development in Micro Electro-Mechanical Systems (MEMS) and wireless communication hardware are being led to emerge networks of tiny distributed sensors called WSN~\cite{akyildiz2002wireless,puccinelli2005wireless}. WSN comprises of small, low-powered sensors working together for perform a typical mission by communicating with one another through multihop wireless connections. They can send the sensed measurements based on local decisions to the user by means of sink nodes. WSN has been used in many applications such as Military, Habitat, Environment, Health, industrial, and Business~\cite{yick2008wireless}.Typically, a sensor node contains three main parts~\cite{anastasi2009energy}: a sensing subsystem, for sense, measure, and gather the measurements from the real environment; processing subsystem, for measurements processing and storage; a communication subsystem, for data transmission and receiving. Moreover, the energy needed by the sensor node is supplied by a power supply, to accomplish the Scheduled task. This power supply is composed of a battery with a limited lifetime. Furthermore, it maybe be unsuitable or impossible to replace or recharge the batteries, since sensor nodes may be deployed in a hostile or unpractical environment. The sensor system ought to have a lifetime long enough to satisfy the application necessities. The lifetime coverage maximization is one of the fundamental requirements of any area coverage protocol in WSN implementation~\cite{nayak2010wireless}. In order to increase the reliability and prevent the possession of coverage holes (some parts are not covered in the area of interest) in the WSN, it is necessary to deploy the WSN with high density so as to increase the reliability and to exploit redundancy by using energy-efficient activity scheduling approaches. + +From a certain standpoint, the high coverage ratio is required by many applications such as military and health-care. Therefore, a suitable number of sensors are being chosen so as to cover the area of interest, is the first challenge. Meanwhile, the sensor nodes have a limited capabilities in terms of memory, processing, communication, and battery power being the most important and critical one. So, the main question is: how to extend the lifetime coverage of WSN as long time as possible?. There are many energy-efficient mechanisms have been suggested to retain energy and extend the lifetime of the WSNs~\cite{rault2014energy}. + +\uppercase{\textbf{Our contributions.}} 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} shows the simulation results. Finally, we give concluding remarks and some suggestions for +future works in Section~\ref{sec:Conclusion and Future Works}. + +\section{\uppercase{Related Literature}} +\label{sec:Literature Review} +\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. + +The problem of k-coverage in WSNs was addressed~\cite{ammari2012centralized}. It mathematically formulated and the spacial sensor density for full k-coverage determined, where the relation between the communication range and the sensing range constructed by this work to retain the k-coverage and connectivity in WSN. After that, a four configuration protocols have proposed for treating the k-coverage in WSNs. + +In~\cite{rebai2014branch}, the problem of full grid coverage is formulated using two integer linear programming models: the first, a model that takes into account only the overall coverage constraint; the second, both the connectivity and the full grid coverage constraints have taken into consideration. This work did not take into account the energy constraint. + +Li et al.~\cite{li2011transforming} presented a framework to convert any complete coverage problem to a partial coverage one with any coverage ratio by means of executing a complete coverage algorithm to find a full coverage sets with virtual radii and transforming the coverage sets to a partial coverage sets by adjusting sensing radii. The properties of the original algorithms can be maintained by this framework and the transformation process has a low execution time. + +The authors in~\cite{liu2014generalized} explained that in some applications of WSNs such as structural health monitoring (SHM) and volcano monitoring, the traditional coverage model which is a geographic area defined for individual sensors is not always valid. For this reason, they define a generalized coverage model, which is not need to have the coverage area of individual nodes, but only based on a function to determine whether a set of +sensor nodes is capable of satisfy the requested monitoring task for a certain area. They have proposed two approaches to divide the deployed nodes into suitable cover sets, which can be used to prolong the network lifetime. + +The work in~\cite{wang2010preserving} addressed the target area coverage problem by proposing a geometric-based activity scheduling scheme, named GAS, to fully cover the target area in WSNs. The authors deals with small area (target area coverage), which can be monitored by a single sensor instead of area coverage, which focuses on a large area that should be monitored by many sensors cooperatively. They explained that GAS is capable to monitor the target area by using a few sensors as possible and it can produce as many cover sets as possible. + +Cho et al.~\cite{cho2007distributed} proposed a distributed node scheduling protocol, which can retain sensing coverage needed by applications +and increase network lifetime via putting in sleep mode some redundant nodes. In this work, the effective sensing area (ESA) concept of a sensor node is used, which refers to the sensing area that is not overlapping with another sensor's sensing area. A sensor node and by compute it's ESA can be determine whether it will be active or sleep. The suggested work permits to sensor nodes to be in sleep mode opportunistically whilst fulfill the needed sensing coverage. + +In~\cite{quang2008algorithm}, the authors defined a maximum sensing coverage region problem (MSCR) in WSNs and then proposed an algorithm to solve it. The +maximum observed area fully covered by a minimum active sensors. In this work, the major property is to getting rid from the redundant sensors in high-density WSNs and putting them in sleep mode, and choosing a smaller number of active sensors so as to be sure that the full area is k-covered, and all events appeared in that area can be precisely and timely detected. This algorithm minimized the total energy consumption and increased the lifetime. + +A novel method to divide the sensors in the WSN, called node coverage grouping (NCG) suggested~\cite{lin2010partitioning}. The sensors in the connectivity group are within sensing range of each other, and the data collected by them in the same group are supposed to be similar. They are proved that dividing n sensors via NCG into connectivity groups is a NP-hard problem. So, a heuristic algorithm of NCG with time complexity of $O(n^3)$ is proposed. +For some applications, such as monitoring an ecosystem with extremely diversified environment, It might be premature assumption that sensors near to each other sense similar data. + +In~\cite{zaidi2009minimum}, the problem of minimum cost coverage in which full coverage is performed by using the minimum number of sensors for an arbitrary geometric shape region is addressed. a geometric solution to the minimum cost coverage problem under a deterministic deployment is proposed. The probabilistic coverage solution which provides a relationship between the probability of coverage and the number of randomly deployed sensors in an arbitrarily-shaped region is suggested. The authors are clarified that with a random deployment about seven times more nodes are required to supply full coverage. + +A graph theoretical framework for connectivity-based coverage with configurable coverage granularity was proposed~\cite{dong2012distributed}. A new coverage criterion and scheduling approach is proposed based on cycle partition. This method is capable of build a sparse coverage set in distributed way by means of only connectivity information. This work considers only the communication range of the sensor is smaller two times the sensing range of sensor. + +Liu et al.~\cite{liu2010energy} formulated maximum disjoint sets problem for retaining coverage and connectivity in WSN. Two algorithms are proposed for solving this problem, heuristic algorithm and network flow algorithm. This work did not take into account the sensor node failure, which is an unpredictable event because the two solutions are full centralized algorithms. + +The work that presented in~\cite{aslanyan2013optimal} solved the coverage and connectivity problem in sensor networks in +an integrated way. The network lifetime is divided in a fixed number of rounds. A coverage bitmap of sensors of the domain has been generated in each round and based on this bitmap, it has been decided which sensors +stay active or turn it to sleep. They checked the connection of the graph via laplacian of adjancy graph of active sensors in each round. the generation of coverage bitmap by using Minkowski technique, the network is able to providing the desired ratio of coverage. They have been defined the connected coverage problem as an optimization problem and a centralized genetic algorithm is used to find the solution. + +Several algorithms to retain the coverage and maximize the network lifetime were proposed in~\cite{cardei2006energy,wang2011coverage}. + +\uppercase{\textbf{shortcomings}}. In spite of many energy-efficient protocols for maintaining the coverage and improving the network lifetime in WSNs were proposed, non of them ensure the coverage for the sensing field with optimal minimum number of active sensor nodes, and for a long time as possible. For example, in a full centralized algorithms, an optimal solutions can be given by using optimization approaches, but in the same time, a high energy is consumed for the execution time of the algorithm and the communications among the sensors in the sensing field, so, the full centralized approaches are not good candidate to use it especially in large WSNs. Whilst, a full distributed algorithms can not give optimal solutions because this algorithms use only local information of the neighboring sensors, but in the same time, the energy consumption during the communications and executing the algorithm is highly lower. Whatever the case, this would result in a shorter lifetime coverage in WSNs. + +\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. 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) is 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 is used the perimeter-coverage model which stated 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. +%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, where L refers to left point of the segment and R refers to right point of the segment. + +\begin{figure}[ht!] +\centering +\includegraphics[width=75mm]{pcm.pdf} +\caption{Perimeter coverage of sensor node 0} +\label{pcmfig} +\end{figure} + +In order to determine the segments of each sensor node, which are perimeter covered by the neighboring sensors, figure~\ref{twosensors} demonstrates the way of locating the left and right points of a segment of the sensor node I covered by a sensor node J. This figure supposed that the neighbor sensor node J is located on the west of a sensor I. It Supposed that the two sensor nodes I and J are located in the positions $(I_x,I_y)$ and $(J_x,J_y)$, respectively. The distance between I and J is computed by $Dist(I,J) = \sqrt{\vert I_x - J_x \vert^2 + \vert I_y - J_y \vert^2}$ . The angle $\alpha = arccos \left(\dfrac{Dist(I,J)}{2R_s} \right) $. So, the $\pi - \alpha$ and the $\pi + \alpha$ of sensor I refers to the left and right points of the segment, which is perimeter covered by sensor node J. If the arch segment of sensor I is located within the angle $[\pi - \alpha,\pi + \alpha]$, this means it is perimeter covered by sensor node J. The left and right points of each segment are put it on the line segment $[0,2\pi]$ and then are sorted in an ascending order so as 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 I$\rq$s perimeter covered by J.} +\label{twosensors} +\end{figure} + +\begin{figure}[ht!] +\centering +\includegraphics[width=75mm]{expcm.pdf} +\caption{Perimeter segment coverage levels for sensor node 0.} +\label{expcm} +\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 segments 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. + +\begin{figure}[ht!] +\centering +\includegraphics[width=90mm]{ex2pcm.jpg} +\caption{The set of sensors for each left or right point of segments for sensor node 0.} +\label{ex2pcm} +\end{figure} + +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 the case of sensor node, which has a part of its sensing range outside the the border of the WSN sensing field as in figure~\ref{ex4pcm}, the perimeter coverage level for this segment is set to $\infty$, and the left and right points of the segments will not be taken into account by the optimization algorithm. +\begin{figure}[ht!] +\centering +\includegraphics[width=75mm]{ex4pcm.jpg} +\caption{Part of sensing range outside the the border of WSN sensing field.} +\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. +\begin{figure}[ht!] +\centering +\includegraphics[width=75mm]{ex5pcm.jpg} +\caption{Perimeter coverage levels for sensor node has a part of its sensing range outside the border.} +\label{ex5pcm} +\end{figure} + + +\subsection{The Main Idea} +\noindent The area of interest can be divided using the +divide-and-conquer strategy into smaller areas called subregions and +then our protocol will be implemented in each subregion simultaneously. LiCO protocol works into periods fashion as shown in figure~\ref{fig2}. +\begin{figure}[ht!] +\centering +\includegraphics[width=85mm]{Model.pdf} +\caption{LiCO protocol} +\label{fig2} +\end{figure} + +Each period is divided into 4 stages: Information (INFO) Exchange, Leader Election, Optimization Decision, and Sensing. For each period there is exactly one set cover responsible for the sensing task. LiCO is more powerful against an unexpected node failure because it works in periods. On the one hand, if the node failure is discovered before taking the decision of the optimization algorithm, the sensor node would not involved to current stage, and, on the other hand, if the sensor failure takes place after the decision, the sensing task of the network will be temporarily affected: only during the period of sensing until a new period starts, since a new set cover will take charge of the sensing task in the next period. The energy consumption and some other constraints can easily be taken into account since the sensors can update and then exchange their information (including their residual energy) at the beginning of each period. However, the pre-sensing phases (INFO Exchange, Leader Election, and Decision) are energy consuming for some sensor nodes, even when they do not join the network to monitor the area. + +We define two types of packets to be used by LiCO protocol. +%\begin{enumerate}[(a)] +\begin{itemize} +\item INFO packet: sent by each sensor node to all the nodes inside a same subregion for information exchange. +\item ActiveSleep packet: sent by the leader to all the nodes in its subregion to inform them to be Active or Sleep during the sensing phase. +\end{itemize} +%\end{enumerate} + +There are five status for each sensor node in the network : +%\begin{enumerate}[(a)] +\begin{itemize} +\item LISTENING: Sensor is waiting for a decision (to be active or not) +\item COMPUTATION: Sensor applies the optimization process as leader +\item ACTIVE: Sensor is active +\item SLEEP: Sensor is turned off +\item COMMUNICATION: Sensor is transmitting or receiving packet +\end{itemize} +%\end{enumerate} +%Below, we describe each phase in more details. + +\subsection{LiCO Protocol Algorithm} +The pseudo-code for LiCO Protocol is illustrated as follows: + + +\begin{algorithm}[h!] + % \KwIn{all the parameters related to information exchange} +% \KwOut{$winer-node$ (: the id of the winner sensor node, which is the leader of current round)} + \BlankLine + %\emph{Initialize the sensor node and determine it's position and subregion} \; + + \If{ $RE_k \geq E_{th}$ }{ + \emph{$s_k.status$ = COMMUNICATION}\; + \emph{Send $INFO()$ packet to other nodes in the subregion}\; + \emph{Wait $INFO()$ packet from other nodes in the subregion}\; + \emph{Update K.CurrentSize}\; + \emph{LeaderID = Leader election}\; + \If{$ s_k.ID = LeaderID $}{ + \emph{$s_k.status$ = COMPUTATION}\; + + \If{$ s_k.ID $ is Not previously selected as a Leader }{ + \emph{ Execute the perimeter coverage model}\; + % \emph{ Determine the segment points using perimeter coverage model}\; + } + + \If{$ (s_k.ID $ is the same Previous Leader) AND (K.CurrentSize = K.PreviousSize)}{ + + \emph{ Use the same previous cover set for current sensing stage}\; + } + \Else{ + \emph{ Update $a^j_{ik}$ and prepare data to Algorithm}\; + \emph{$\left\{\left(X_{1},\dots,X_{l},\dots,X_{K}\right)\right\}$ = Execute Integer Program Algorithm($K$)}\; + \emph{K.PreviousSize = K.CurrentSize}\; + } + + \emph{$s_k.status$ = COMMUNICATION}\; + \emph{Send $ActiveSleep()$ to each node $l$ in subregion} \; + \emph{Update $RE_k $}\; + } + \Else{ + \emph{$s_k.status$ = LISTENING}\; + \emph{Wait $ActiveSleep()$ packet from the Leader}\; + \emph{Update $RE_k $}\; + } + } + \Else { Exclude $s_k$ from entering in the current sensing stage} + + +\caption{LiCO($s_k$)} +\label{alg:LiCO} + +\end{algorithm} + +\noindent Algorithm 1 gives a brief description of the protocol applied by each sensor node (denoted by $s_k$ for a sensor node indexed by $k$). In this algorithm, the K.CurrentSize and K.PreviousSize refer to the current size and the previous size of sensor nodes in the subregion respectively. +Initially, the sensor node checks its remaining energy in order to participate in the current period. Each sensor node determines its position and its subregion based Embedded GPS or Location Discovery Algorithm. After that, all the sensors collect position coordinates, remaining energy $RE_k$, sensor node id, and the number of its one-hop live neighbors during the information exchange. +After the cooperation among the sensor nodes in the same subregion, the leader will be elected in distributed way, where each sensor node and based on it's information decide who is the leader. The selection criteria for the leader in order of priority are: larger number of neighbors, larger remaining energy, and then in case of equality, larger index. Thereafter, if the sensor node is leader, it will execute the perimeter-coverage model for each sensor in the subregion in order to determine the segment points which would be used in the next stage by the optimization algorithm of the LiCO protocol. Every sensor node is selected as a leader, it is executed the perimeter coverage model only one time during it's life in the network. The leader has the responsibility of applying the integer program algorithm (see section~\ref{cp}), which provides a set of sensors planned to be active in the sensing stage. As leader, it will send an Active-Sleep packet to each sensor in the same subregion to inform it if it has to be active or not. On the contrary, if the sensor is not the leader, it will wait for the Active-Sleep packet to know its state for the sensing stage. + + +\section{Lifetime Coverage problem formulation} +\label{cp} +In this section, the coverage model is mathematically formulated. +For convenience, the notations are described first. +%Then the lifetime problem of sensor network is formulated. +\noindent $S :$ the set of all sensors in the network.\\ +\noindent $A :$ the set of alive sensors within $S$.\\ +%\noindent $I :$ the set of segment points.\\ +\noindent $I_j :$ the set of coverage intervals (CI) for sensor $j$.\\ + +\noindent For a coverage interval $i$, let $a^j_{ik}$ denote the indicator function of whether the sensor $k$ is involved in the coverage interval $i$ of sensor $j$, that is: + +\begin{equation} +a^j_{ik} = \left \{ +\begin{array}{lll} + 1 & \mbox{if the sensor $k$ is involved in the } \\ + & \mbox{coverage interval $i$ of sensor $j$}, \\ + 0 & \mbox{Otherwise.}\\ +\end{array} \right. +%\label{eq12} +\notag +\end{equation} +%, where the objective is to find the maximum number of non-disjoint sets of sensor nodes such that each set cover can assure the coverage for the whole region so as to extend the network lifetime in WSN. Our model uses the PCL~\cite{huang2005coverage} in order to optimize the lifetime coverage in each subregion. +%We defined some parameters, which are related to our optimization model. In our model, we consider binary variables $X_{k}$, which determine the activation of sensor $k$ in the sensing round $k$. . +We consider binary variables $X_{k}$ ($X_k=1$ if the sensor $k$ is active or 0 otherwise), which determine the activation of sensor $k$ in the sensing phase. We define the integer variable $M^j_i$ which measures the undercoverage for the coverage interval $i$ for sensor $j$. In the same way, we define the integer variable $V^j_i$, which measures the overcoverage for the coverage interval $i$ for sensor $j$. If we decide to sustain a level of coverage equal to $l$ all along the perimeter of the sensor $j$, we have to ensure that at least $l$ sensors involved in each coverage interval $i$ ($i \in I_j$) of sensor $j$ are active. According to the previous notations, the number of active sensors in the coverage interval $i$ of sensor $j$ is given by $\sum_{k \in K} a^j_{ik} X_k$. To extend the network lifetime, the objective is to active a minimal number of sensors in each period to ensure the desired coverage level. As the number of alive sensors decreases, it becomes impossible to satisfy the level of coverage for all covergae intervals. We uses variables $M^j_i$ and $V^j_i$ as a measure of the deviation between the desired number of active sensors in a coverage interval and the effective number of active sensors. And we try to minimize these deviations, first to force the activation of a minimal number of sensors to ensure the desired coverage level, and if the desired level can not be completely satisfied, to reach a coverage level as close as possible that the desired one. + + + +%A system of linear constraints is imposed to attempt to keep the coverage level in each coverage interval to within specified PCL. Since it is physically impossible to satisfy all constraints simultaneously, each constraint uses a variable to either record when the coverage level is achieved, or to record the deviation from the desired coverage level. These additional variables are embedded into an objective function to be minimized. + +%\noindent In this paper, let us define some parameters, which are used in our protocol. +%the set of segment points is denoted by $I$, the set of all sensors in the network by $J$, and the set of alive sensors within $J$ by $K$. + + +%\noindent \begin{equation} +%X_{k} = \left \{ +%\begin{array}{l l} + % 1& \mbox{if sensor $k$ is active,} \\ +% 0 & \mbox{otherwise.}\\ +%\end{array} \right. +%\label{eq11} +%\notag +%\end{equation} + +%\noindent $M^j_i (undercoverage): $ integer value $\in \mathbb{N}$ for segment point $i$ of sensor $j$. + +%\noindent $V^j_i (overcoverage): $ integer value $\in \mathbb{N}$ for segment point $i$ of sensor $j$. + + + + + +\noindent Our coverage optimization problem can be mathematically formulated as follows: \\ +%Objective: + +\begin{equation} \label{eq:ip2r} +\left \{ +\begin{array}{ll} +\min \sum_{j \in S} \sum_{i \in I_j} (\alpha^j_i ~ M^j_i + \beta^j_i ~ V^j_i )&\\ +\textrm{subject to :}&\\ +\sum_{k \in A} ( a^j_{ik} ~ X_{k}) + M^j_i \geq l \quad \forall i \in I_j, \forall j \in S\\ +%\label{c1} +\sum_{k \in A} ( a^j_{ik} ~ X_{k}) - V^j_i \leq l \quad \forall i \in I_j, \forall j \in S\\ +% \label{c2} +% \Theta_{p}\in \mathbb{N}, &\forall p \in P\\ +% U_{p} \in \{0,1\}, &\forall p \in P\\ +X_{k} \in \{0,1\}, \forall k \in A +\end{array} +\right. +\end{equation} + + +\noindent $\alpha^j_i$ and $\beta^j_i$ are nonnegative weights selected according to the +relative importance of satisfying the associated +level of coverage. For example, weights associated with coverage intervals of a specified part of a region +may be given a relatively +larger magnitude than weights associated +with another region. This kind of integer program is inspired from the model developed for brachytherapy treatment planning for optimizing dose distribution \ref{0031-9155-44-1-012}. The integer program must be solved by the leader in each subregion at the beginning of each sensing phase, whenever the environment has changed (new leader, death of some sensors). Note that the number of constraints in the model is constant (constraints of coverage expressed for all sensors), whereas the number of variables $X_k$ decreases over periods, since we consider only alive sensors (sensors with enough energy to be alive during one sensing phase) in the model. + + +\section{\uppercase{PERFORMANCE EVALUATION AND ANALYSIS}} +\label{sec:Simulation Results and Analysis} +%\noindent \subsection{Simulation Framework} + +\subsection{Simulation Settings} +%\label{sub1} +In this section, we focused on the performance of LiCO protocol, which is distributed in each sensor node in the sixteen subregions of WSN. We used the same energy consumption model which are used in~\cite{Idrees2}. Table~\ref{table3} gives the chosen parameters setting. + +\begin{table}[ht] +\caption{Relevant parameters for network initializing.} +% title of Table +\centering +% used for centering table +\begin{tabular}{c|c} +% centered columns (4 columns) +\hline +Parameter & Value \\ [0.5ex] + +\hline +% inserts single horizontal line +Sensing Field & $(50 \times 25)~m^2 $ \\ + +Nodes Number & 100, 150, 200, 250 and 300~nodes \\ +%\hline +Initial Energy & 500-700~joules \\ +%\hline +Sensing Period & 60 Minutes \\ +$E_{th}$ & 36 Joules\\ +$R_s$ & 5~m \\ +%\hline +$\alpha^j_i$ & 0.6 \\ +% [1ex] adds vertical space +%\hline +$\beta^j_i$ & 0.4 +%inserts single line +\end{tabular} +\label{table3} +% is used to refer this table in the text +\end{table} +Simulations with five different node densities going from 100 to 250~nodes were +performed considering each time 25~randomly generated networks, to obtain +experimental results which are relevant. All simulations are repeated 25 times and the results are averaged. The nodes are deployed on a field of interest of $(50 \times 25)~m^2 $ in such a way that they cover the field with a high coverage ratio. + +Each node has an initial energy level, in Joules, which is randomly drawn in the +interval $[500-700]$. If it's energy provision reaches a value below the +threshold $E_{th}=36$~Joules, the minimum energy needed for a node to stay +active during one period, it will no more participate in the coverage task. This +value corresponds to the energy needed by the sensing phase, obtained by +multiplying the energy consumed in active state (9.72 mW) by the time in seconds +for one period (3600 seconds), and adding the energy for the pre-sensing phases. +According to the interval of initial energy, a sensor may be active during at +most 20 rounds. + +In the simulations, we introduce the following performance metrics to evaluate +the efficiency of our approach: + +%\begin{enumerate}[i)] +\begin{itemize} +\item {{\bf Network Lifetime}:} we define the network lifetime as the time until + the coverage ratio drops below a predefined threshold. We denote by + $Lifetime_{95}$ (respectively $Lifetime_{50}$) the amount of time during which + the network can satisfy an area coverage greater than $95\%$ (respectively + $50\%$). We assume that the sensor network can fulfill its task until all its + nodes have been drained of their energy or it becomes disconnected. Network + connectivity is crucial because an active sensor node without connectivity + towards a base station cannot transmit any information regarding an observed + event in the area that it monitors. + + +\item {{\bf Coverage Ratio (CR)}:} it measures how well the WSN is able to + observe the area of interest. In our case, we discretized the sensor field + as a regular grid, which yields the following equation to compute the + coverage ratio: +\begin{equation*} +\scriptsize +\mbox{CR}(\%) = \frac{\mbox{$n$}}{\mbox{$N$}} \times 100. +\end{equation*} +where $n$ is the number of covered grid points by active sensors of every +subregions during the current sensing phase and $N$ is total number of grid +points in the sensing field. In our simulations, we have a layout of $N = 51 +\times 26 = 1326$ grid points. + + +\item{{\bf Number of Active Sensors Ratio(ASR)}:} It is important to have as few active nodes as possible in each round, +in order to minimize the communication overhead and maximize the +network lifetime. The Active Sensors Ratio is defined as follows: +\begin{equation*} +\scriptsize +\mbox{ASR}(\%) = \frac{\sum\limits_{r=1}^R \mbox{$A_r$}}{\mbox{$S$}} \times 100 . +\end{equation*} +Where: $A_r^t$ is the number of active sensors in the subregion $r$ in the current sensing stage, $S$ is the total number of sensors in the network, and $R$ is the total number of the subregions in the network. + + + +\item {{\bf Energy Consumption}:} energy consumption (EC) can be seen as the + total energy consumed by the sensors during $Lifetime_{95}$ or + $Lifetime_{50}$, divided by the number of periods. Formally, the computation + of EC can be expressed as follows: + \begin{equation*} + \scriptsize + \mbox{EC} = \frac{\sum\limits_{m=1}^{M} \left( E^{\mbox{com}}_m+E^{\mbox{list}}_m+E^{\mbox{comp}}_m + + E^{a}_m+E^{s}_m \right)}{M}, + \end{equation*} + +where $M$ corresponds to the number of periods. The total energy consumed by +the sensors (EC) comes through taking into consideration four main energy factors. The first one, denoted $E^{\scriptsize \mbox{com}}_m$, represent the +energy consumption spent by all the nodes for wireless communications during +period $m$. $E^{\scriptsize \mbox{list}}_m$, the next factor, corresponds to +the energy consumed by the sensors in LISTENING status before receiving the +decision to go active or sleep in period $m$. $E^{\scriptsize \mbox{comp}}_m$ +refers to the energy needed by all the leader nodes to solve the integer program +during a period. Finally, $E^a_{m}$ and $E^s_{m}$ indicate the energy consumed +by the whole network in the sensing phase (active and sleeping nodes). + + +\end{itemize} +%\end{enumerate} + +\subsection{Simulation Results} +In this section, we present the simulation results of LiCO protocol and the other protocols using a discrete event simulator OMNeT++ \cite{varga} to run different series of simulations. We implemented all protocols precisely on a laptop DELL with Intel Core~i3~2370~M (2.4 GHz) processor (2 cores) and the MIPS (Million Instructions Per Second) rate equal to 35330. To be consistent with the use of a sensor node with Atmels AVR ATmega103L microcontroller (6 MHz) and a MIPS rate equal to 6, the original execution time on the laptop is multiplied by 2944.2 $\left(\frac{35330}{2} \times \frac{1}{6} \right)$ so as to use it by the energy consumption model especially, after the computation and listening. Employing the modeling language ????\ref{}, the associated integer program instance is generated in a standard format, which is then read and solved by the optimization solver GLPK (GNU linear Programming Kit available in the public domain) \cite{glpk} through a Branch-and-Bound method. + +We compared LiCO protocol to three other approaches: the first, called DESK and proposed by ~\cite{ChinhVu} is a fully distributed coverage algorithm; the second, called GAF ~\cite{xu2001geography}, consists in dividing the region +into fixed squares. During the decision phase, in each square, one sensor is +chosen to remain active during the sensing phase; the third, DiLCO protocol~\cite{Idrees2} is an improved version on the work presented in ~\cite{idrees2014coverage}. DNote that the LiCO protocol is based on the same framework as that of DiLCO. For thes two protocols, the division of the region of interest in 16 subregions was chosen since it produces the best results. The difference between the two protocols relies on the use of the integer programming to provide the set of sensors that have to be actived in each sensing phase. Whereas DilCO protocol tries to satisfy the coverage of a set of primary points, LiCO protocol tries to reach a desired level of coverage $l$ for each sensor's perimeter. In the experimentations, we chose a level of coverage equal to 1 ($l=1$). + +\subsubsection{\textbf{Coverage Ratio}} +Figure~\ref{fig333} shows the average coverage ratio for 200 deployed nodes obtained with the four methods. + +\parskip 0pt +\begin{figure}[h!] +\centering + \includegraphics[scale=0.5] {R/CR.eps} +\caption{The coverage ratio for 200 deployed nodes} +\label{fig333} +\end{figure} + +DESK, GAF, and DiLCO provides a little better coverage ratio with 99.99\%, 99.91\%, and 99.02\% against 98.76\% produced by LiCO for the lowest number of periods. This is due to the fact that DiLCO protocol put in sleep mode redundant sensors using optimization (which lightly decreases the coverage ratio) while there are more active nodes in the case of others methods. + +Moreover, when the number of periods increases, coverage ratio produced by DESK and GAF protocols decreases. This is due to dead nodes. However, DiLCO protocol maintains almost a good coverage from the round 31 to the round 63 and it is close to LiCO protocol. This is because it optimizes the coverage and the lifetime in WSN based on the primary points by selecting the best representative sensor nodes for the sensing stage. LiCO protocol put in sleep mode a higher number of redundant sensors starting from the round 19 using the new optimization model. The coverage ratio of LiCO Protocol seems to be better than other approaches starting from the round 64 because the optimization algorithm used by LiCO has been optimized the lifetime coverage based on the perimeter coverage model, so it provided acceptable coverage for a larger number of periods and prolonging the network lifetime based on the perimeter of the sensor nodes in each subregion of WSN. Although some nodes are dead, sensor activity scheduling based optimization of LiCO selected another nodes to ensure the coverage of the area of interest. i.e. DiLCO-16 showed a good coverage in the beginning then LiCO, when the number of periods increases, the coverage ratio decreases due to died sensor nodes. Meanwhile, thanks to sensor activity scheduling based new optimization model, which is used by LiCO protocol to ensure a longer lifetime coverage in comparison with other approaches. + + +\subsubsection{\textbf{Active Sensors Ratio}} +It is important to have as few active nodes as possible in each period, in order to minimize the energy consumption and maximize the network lifetime. Figure~\ref{fig444} shows the average active nodes ratio for 200 deployed nodes. + +\begin{figure}[h!] +\centering +\includegraphics[scale=0.5]{R/ASR.eps} +\caption{The active sensors ratio for 200 deployed nodes } +\label{fig444} +\end{figure} + +From figure~~\ref{fig444}, We observed that DESK and GAF have 30.36 \% and 34.96 \% active nodes for the first fourteen rounds and DiLCO-16 and LiCO protocols competes perfectly with only 17.92 \% and 20.16 \% active nodes for the first 17 rounds. Then as the number of rounds increases our LiCO protocol has a lower number of active nodes in comparison with DiLCO-16, DESK and GAF, especially from the round $19^{th}$ because it optimizes the lifetime coverage into the subregion based on the perimeter coverage model, which made LiCO improves the coverage ratio and for a longer time in comparison with other approaches. + +\subsubsection{\textbf{The Energy Consumption}} +In this experiment, we study the effect of the energy consumed by the WSN during the communication, computation, listening, active, and sleep modes for different network densities and compare it with other approaches. Figures~\ref{fig3EC95} and ~\ref{fig3EC50} illustrate the energy consumption for different network sizes for $Lifetime95$ and $Lifetime50$. + +\begin{figure}[h!] +\centering +\includegraphics[scale=0.5]{R/EC95.eps} +\caption{The Energy Consumption per period with $Lifetime_{95}$} +\label{fig3EC95} +\end{figure} + +\begin{figure}[h!] +\centering +\includegraphics[scale=0.5]{R/EC50.eps} +\caption{The Energy Consumption per period with $Lifetime_{50}$} +\label{fig3EC50} +\end{figure} + +The results show that our LiCO protocol is the most competitive from the energy consumption point of view. As shown in figures Figures~\ref{fig3EC95} and ~\ref{fig3EC50}, LiCO consumes less energy especially when the network size increases because it puts in sleep mode less active sensor number as possible in most periods of the network lifetime. The optimization algorithm, which used by LiCO protocol, was improved the lifetime coverage efficiently based on the perimeter coverage model. + + The other approaches have a high energy consumption due to activating a larger number of redundant nodes as well as the energy consumed during the different modes of sensor nodes. In fact, a distributed method on the subregions greatly reduces the number of communications and the time of listening so thanks to the partitioning of the initial network into several independent subnetworks. + + +%\subsubsection{Execution Time} + +\subsubsection{\textbf{The Network Lifetime}} +In this experiment, we are observed the superiority of LiCO and DiLCO-16 protocols against other two approaches in prolonging the network lifetime. In figures~\ref{fig3LT95} and \ref{fig3LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes. + +\begin{figure}[h!] +\centering +\includegraphics[scale=0.5]{R/LT95.eps} +\caption{The Network Lifetime for $Lifetime_{95}$} +\label{fig3LT95} +\end{figure} + + +\begin{figure}[h!] +\centering +\includegraphics[scale=0.5]{R/LT50.eps} +\caption{The Network Lifetime for $Lifetime_{50}$} +\label{fig3LT50} +\end{figure} + +As highlighted by figures~\ref{fig3LT95} and \ref{fig3LT50}, the network lifetime obviously increases when the size of the network increases, with our LiCO and DiLCO-16 protocols that leads to maximize the lifetime of the network compared with other approaches. + +By choosing the best suited nodes, for each round, by optimizing the coverage and lifetime of the network to cover the area of interest and by letting the other ones sleep in order to be used later in next rounds, LiCO protocol efficiently prolonged the network lifetime especially for a coverage ratio greater than $50 \%$, whilst it stayed very near to DiLCO-16 protocol for $95 \%$. Figure~\ref{figLTALL} introduces the comparisons of the lifetime coverage for different coverage ratios between LiCO and DiLCO-16 protocols. +We denote by Protocol/50, Protocol/80, Protocol/85, Protocol/90, and Protocol/95 the amount of time during which the network can satisfy an area coverage greater than $50\%$, $80\%$, $85\%$, $90\%$, and $95\%$ respectively. + +\begin{figure}[h!] +\centering +\includegraphics[scale=0.5]{R/LTa.eps} +\caption{The Network Lifetime for different coverage ratios} +\label{figLTALL} +\end{figure} + +Comparison shows that LiCO protocol, which are used distributed optimization over the subregions, is the more relevance one for most coverage ratios and WSN sizes because it is robust to network disconnection during the network lifetime as well as it consume less energy in comparison with other approaches. LiCO protocol gave acceptable coverage ratio for a larger number of periods using new optimization algorithm that based on a perimeter coverage model. It also means that distributing the algorithm in each node and subdividing the sensing field into many subregions, which are managed independently and simultaneously, is the most relevant way to maximize the lifetime of a network. + + +\section{\uppercase{Conclusion and Future Works}} +\label{sec:Conclusion and Future Works} +In this paper, we have studied the problem of lifetime coverage optimization in +WSNs. To cope with this problem, the area of interest is divided into a smaller subregions using divide-and-conquer method, and then a LiCO protocol for optimizing the lifetime coverage in each subregion. LiCO protocol combines two efficient techniques: the first, network +leader election, which executes the perimeter coverage model (only one time), the optimization algorithm, and sending the schedule produced by the optimization algorithm to other nodes in the subregion ; the second, sensor activity scheduling based optimization in which a new lifetime coverage optimization model is proposed. The main challenges include how to select the most efficient leader in each subregion and the best schedule of sensor nodes that will optimize the network lifetime coverage +in the subregion. The network lifetime coverage in each subregion is divided into +periods, each period consists of four stages: (i) Information Exchange, +(ii) Leader Election, (iii) a Decision based new optimization model in order to +select the nodes remaining active for the last stage, and (iv) Sensing. +The simulation results show that LiCO is is more energy-efficient than other approaches, with respect to lifetime, coverage ratio, active sensors ratio, and energy consumption. Indeed, when dealing with large and dense WSNs, a distributed optimization approach on the subregions of WSN like the one we are proposed allows to reduce the difficulty of a single global optimization problem by partitioning it in many smaller problems, one per subregion, that can be solved more easily. + +Our future work is four-fold: the first, we plan to extend a lifetime coverage optimization problem in order to computes all active sensor schedules in only one step for many periods; +the second, we focus on extend our protocol and optimization algorithm to take into account the heterogeneous sensors, which do not have the same energy, processing, sensing and communication capabilities; +the third, we are investigating new optimization model based on the sensing range so as to maximize the lifetime coverage in WSN; +Finally, our final goal is to implement our protocol using a sensor-testbed to evaluate their performance in real world applications. + +\section*{\uppercase{Acknowledgements}} +\noindent As a Ph.D. student, Ali Kadhum IDREES would like to gratefully acknowledge the University of Babylon - IRAQ for the financial support and Campus France for the received support. + + + + +\ifCLASSOPTIONcaptionsoff + \newpage +\fi + + +\bibliographystyle{IEEEtran} +%\bibliographystyle{IEEEbiographynophoto} +\bibliography{LiCO_Journal} + + + +%\begin{IEEEbiographynophoto}{Jane Doe} + + + + +\end{document} + + diff --git a/LiCO_Journal.aux b/LiCO_Journal.aux new file mode 100644 index 0000000..1fef502 --- /dev/null +++ b/LiCO_Journal.aux @@ -0,0 +1,134 @@ +\relax +\citation{akyildiz2002wireless} +\citation{puccinelli2005wireless} +\citation{yick2008wireless} +\citation{anastasi2009energy} +\citation{rault2014energy} +\citation{ChinhVu} +\citation{xu2001geography} +\citation{Idrees2} +\citation{li2013survey} +\citation{Misra} +\citation{yang2014novel} +\citation{HeShibo} +\citation{kim2013maximum} +\citation{Deng2012} 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+\bibcite{ChinhVu}{6} +\bibcite{xu2001geography}{7} +\bibcite{Idrees2}{8} +\bibcite{li2013survey}{9} +\bibcite{Misra}{10} +\bibcite{yang2014novel}{11} +\bibcite{HeShibo}{12} +\bibcite{kim2013maximum}{13} +\bibcite{Deng2012}{14} +\bibcite{Huang:2003:CPW:941350.941367}{15} +\bibcite{ling2009energy}{16} +\bibcite{jaggi2006}{17} +\bibcite{chin2007}{18} +\bibcite{pc10}{19} +\bibcite{yangnovel}{20} +\bibcite{qu2013distributed}{21} +\bibcite{cardei2005improving}{22} +\bibcite{zorbas2010solving}{23} +\bibcite{pujari2011high}{24} +\bibcite{berman04}{25} +\bibcite{cardei2005energy}{26} +\bibcite{5714480}{27} +\bibcite{Yang2014}{28} +\bibcite{castano2013column}{29} +\bibcite{rossi2012exact}{30} +\bibcite{deschinkel2012column}{31} +\bibcite{Zhang05}{32} +\bibcite{huang2005coverage}{33} +\bibcite{0031-9155-44-1-012}{34} +\bibcite{varga}{35} +\bibcite{AMPL}{36} +\@writefile{toc}{\contentsline {section}{References}{9}} +\bibcite{glpk}{37} +\bibcite{idrees2014coverage}{38} diff --git a/LiCO_Journal.bbl b/LiCO_Journal.bbl new file mode 100644 index 0000000..410dcb1 --- /dev/null +++ b/LiCO_Journal.bbl @@ -0,0 +1,228 @@ +% Generated by IEEEtran.bst, version: 1.12 (2007/01/11) +\begin{thebibliography}{10} +\providecommand{\url}[1]{#1} +\csname url@samestyle\endcsname +\providecommand{\newblock}{\relax} +\providecommand{\bibinfo}[2]{#2} +\providecommand{\BIBentrySTDinterwordspacing}{\spaceskip=0pt\relax} +\providecommand{\BIBentryALTinterwordstretchfactor}{4} +\providecommand{\BIBentryALTinterwordspacing}{\spaceskip=\fontdimen2\font plus +\BIBentryALTinterwordstretchfactor\fontdimen3\font minus + \fontdimen4\font\relax} +\providecommand{\BIBforeignlanguage}[2]{{% +\expandafter\ifx\csname l@#1\endcsname\relax +\typeout{** WARNING: IEEEtran.bst: No hyphenation pattern has been}% +\typeout{** loaded for the language `#1'. Using the pattern for}% +\typeout{** the default language instead.}% +\else +\language=\csname l@#1\endcsname +\fi +#2}} +\providecommand{\BIBdecl}{\relax} +\BIBdecl + +\bibitem{akyildiz2002wireless} +I.~F. 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Lopes", + TITLE = "Paper templates", + BOOKTITLE = "TEMPLATE'06, 1st International Conference on Template Production", + YEAR = "1999", + publisher = "SCITEPRESS", + file = F +} + +@BOOK{Smith98, + AUTHOR = "J. Smith", + TITLE = "The Book", + PUBLISHER = "The publishing company", + YEAR = "1998", + address = "London", + edition = "2nd", + file = F +} + + +@inproceedings{vashistha2007energy, + title={Energy efficient area monitoring using information coverage in wireless sensor networks}, + author={Vashistha, Sumit and Azad, Amar Prakash and Chockalingam, Ananthanarayanan}, + booktitle={World of Wireless, Mobile and Multimedia Networks, 2007. WoWMoM 2007. IEEE International Symposium on a}, + pages={1--10}, + year={2007}, + organization={IEEE} +} + + +@ARTICLE{Torkestani, + author = "J. A. Torkestani", + title = "An adaptive energy-efficient area coverage algorithm for wireless sensor networks ", + journal = {Ad Hoc Networks}, + volume = {11}, + number = {6}, + pages = {1655-1666}, + year = {2013}, + } + +@ARTICLE{Zhang, + author = "L. Zhang and Q. Zhu and J. Wang", + title = "Adaptive Clustering for Maximizing Network Lifetime and Maintaining Coverage ", + journal = {JOURNAL OF NETWORKS}, + volume = {8}, + number = {3}, + pages = {616-622}, + year = {2013}, + } + +@ARTICLE{pc10, + author = "T.V. Padmavathy and M. Chitra", + title = "Extending the Network Lifetime of Wireless Sensor Networks Using Residual Energy Extraction-Hybrid Scheduling Algorithm", + JOURNAL = {Int. J. of Communications, Network and System Sciences}, + VOLUME = {3}, + NUMBER = {1}, +PAGES = {98-106}, +YEAR = {2010}, + } + + + +@ARTICLE{Zhixin, + author = "Z. Liu and Q. Zheng and L. Xue and X. Guan", + title = "A distributed energy-efficient clustering algorithm with improved coverage in +wireless sensor networks", + JOURNAL = {Future Generation Computer Systems}, + VOLUME = {28}, + NUMBER = {5}, + PAGES = {780-790}, + YEAR = {2012}, + } + +@ARTICLE{Bang, + author = "B. Wang and H. B. Lim and D. Ma ", + title = "A coverage-aware clustering protocol for wireless sensor networks", + JOURNAL = {Computer Networks}, + VOLUME = {56}, + NUMBER = {5}, + PAGES = {1599-1611}, + YEAR = {2012}, + } + + + +@book{Akyildiz02, + author = {I. F. Akyildiz and M. C. Vuran}, + title = {Wireless Sensor Networks}, + publisher = {John Wiley and Sons Ltd.}, + year = {2010}, +} + +@book{Sudip03, + author = {S. Misra and I. Woungang and S. C. Misra}, + title = {Guide to Wireless Sensor Networks}, + publisher = {Springer-Verlag London Limited}, + year = {2009}, +} + +@book{Nayak04, + author = {A. Nayak and I. Stojmenovic}, + title = {Wireless Sensor and Actuator Networks: Algorithms and Protocols for Scalable Coordination and Data Communication}, + publisher = {John Wiley and Sons, Inc}, + year = {2010}, +} + + + +@ARTICLE{Huang:2003:CPW:941350.941367, + author = "C.-F. HUANG and Y.-C. TSENG", + title = "The Coverage Problem in a Wireless Sensor Network", + JOURNAL = {Mobile Networks and Applications}, + VOLUME = {10}, + NUMBER = {4}, + PAGES = {519-528}, + YEAR = {2005}, + } + + +@ARTICLE{Zhang05, + author = {H. Zhang and J. C. Hou}, + title = {Maintaining Sensing Coverage and Connectivity in Large Sensor Networks}, + journal = {Ad Hoc {\&} Sensor Wireless Networks}, + volume = {1}, + number = {1-2}, + year = {2005}, + +} + +@ARTICLE{HeinzelmanCB02, + author = {W. B. Heinzelman and A. P. Chandrakasan and H. Balakrishnan}, + title = {An application-specific protocol architecture for wireless microsensor networks}, + journal = {IEEE Transactions on Wireless Communications}, + volume = {1}, + number = {4}, + pages = {660-670}, + year = {2002}, +} + + +@inproceedings{pedraza2006, + author = {F. Pedraza and A. L. Medaglia and A. Garcia}, + title = {Efficient coverage algorithms for wireless sensor networks}, + booktitle = {Proceedings of the 2006 Systems and Information Engineering Design Symposium}, + pages = {78-83}, + YEAR = {2006}, +} + +@PhDThesis{chin2007, +author = {C. T. Vu}, +title = {DISTRIBUTED ENERGY-EFFICIENT SOLUTIONS FOR AREA COVERAGE PROBLEMS IN WIRELESS SENSOR NETWORKS}, +school = {Georgia State University}, +year = {2009}, +} + +@ARTICLE{varga, +author = {A. Varga}, +title = {OMNeT++ Discrete Event Simulation System}, +journal = {Available: http://www.omnetpp.org}, +year = {2003}, +} + + +@ARTICLE{die09, + author = {Isabel Dietrich and + Falko Dressler}, + title = {On the lifetime of wireless sensor networks}, + journal = {TOSN}, + volume = {5}, + number = {1}, + year = {2009}, + ee = {http://doi.acm.org/10.1145/1464420.1464425}, + bibsource = {DBLP, http://dblp.uni-trier.de} +} + + + +@ARTICLE{mulligan2010coverage, + title={Coverage in Wireless Sensor Networks: A Survey.}, + author={Mulligan, Raymond and Ammari, Habib M}, + journal={Network Protocols \& Algorithms}, + volume={2}, + number={2}, + year={2010} +} + + +@phdthesis{vu2009distributed, + title={Distributed energy-efficient solutions for area coverage problems in wireless sensor networks}, + author={Vu, Chinh Trung}, + year={2009}, + school={GEORGIA STATE UNIVERSITY} +} + + +@ARTICLE{raghunathan2002energy, + title={Energy-aware wireless microsensor networks}, + author={Raghunathan, Vijay and Schurgers, Curt and Park, Sung and Srivastava, Mani B}, + journal={Signal Processing Magazine, IEEE}, + volume={19}, + number={2}, + pages={40--50}, + year={2002}, + publisher={IEEE} +} + +@ARTICLE{ChinhVu, +author = {Chinh Vu and Shan Gao and Wiwek Deshmukh and Yingshu Li}, +title = {Distributed Energy-Efficient Scheduling Approach for K-Coverage in Wireless Sensor Networks}, +journal ={MILCOM}, +volume = {0}, +isbn = {1-4244-0617-X}, +year = {2006}, +pages = {1-7}, +doi = {http://doi.ieeecomputersociety.org/10.1109/MILCOM.2006.302146}, +publisher = {IEEE Computer Society}, +address = {Los Alamitos, CA, USA}, +} + + +@ARTICLE{ghosh2008coverage, + title={Coverage and connectivity issues in wireless sensor networks: A survey}, + author={Ghosh, Amitabha and Das, Sajal K}, + journal={Pervasive and Mobile Computing}, + volume={4}, + number={3}, + pages={303--334}, + year={2008}, + publisher={Elsevier} +} + + + +@ARTICLE{yang2014novel, +author={Changlin Yang and Kwan-Wu Chin}, +journal={Communications Letters, IEEE}, +title={Novel Algorithms for Complete Targets Coverage in Energy Harvesting Wireless Sensor Networks}, +year={2014}, +month={January}, +volume={18}, +number={1}, +pages={118-121}, +keywords={energy harvesting;linear programming;secondary cells;telecommunication power supplies;wireless sensor networks;MUA;WSN;activation schedule;complete targets coverage;energy conservation;energy harvesting wireless sensor networks;linear programming based solution;maximum utility algorithm;network lifetime maximization problem;recharging opportunities;sensor nodes;sleep state;time interval;wake-up schedule;Batteries;Energy harvesting;Erbium;Monitoring;Schedules;Sensors;Wireless sensor networks;Wireless sensor network;energy harvesting;targets coverage}, +doi={10.1109/LCOMM.2013.111513.132436}, +ISSN={1089-7798},} + +@ARTICLE{HeShibo, +author={He, Shibo and Gong, Xiaowen and Zhang, Junshan and Chen, Jiming and Sun, Youxian}, +journal={Wireless Communications, IEEE Transactions on}, +title={Curve-Based Deployment for Barrier Coverage in Wireless Sensor Networks}, +year={2014}, +month={February}, +volume={13}, +number={2}, +pages={724-735}, +keywords={Algorithm design and analysis;Approximation algorithms;Approximation methods;Educational institutions;Sensors;Wireless communication;Wireless sensor networks;Barrier coverage;curved-based deployment;deployment algorithms;optimal deployment curve}, +doi={10.1109/TWC.2013.121813.130198}, +ISSN={1536-1276}, +} + + + +@inproceedings{Zorbas2007, + author = {D. Zorbas and D. Glynos and P. Kotzanikolaou and C. Douligeris}, + title = {B\{GOP\}: an adaptive coverage algorithm for wireless sensor networks}, + booktitle = {Proceedings of the 13th European Wireless Conference}, + series = {EW'07}, + year = {2007}, + location = {Paris, France}, + +} + +@ARTICLE{zorbas2010solving, + title={Solving coverage problems in wireless sensor networks using cover sets}, + author={Zorbas, Dimitrios and Glynos, Dimitris and Kotzanikolaou, Panayiotis and Douligeris, Christos}, + journal={Ad Hoc Networks}, + volume={8}, + number={4}, + pages={400-415}, + year={2010}, + publisher={Elsevier}, +} + +@INPROCEEDINGS{Slijepcevic01powerefficient, + author = {S. Slijepcevic and M. Potkonjak}, + title = {Power Efficient Organization of Wireless Sensor Networks}, + booktitle = {IEEE International conference on Communications}, + year = {2001}, + pages = {472-476} +} + +@ARTICLE{cardei2006energy, + title={Energy-efficient coverage problems in wireless ad-hoc sensor networks}, + author={Cardei, Mihaela and Wu, Jie}, + journal={Computer communications}, + volume={29}, + number={4}, + pages={413-420}, + year={2006}, + publisher={Elsevier} +} + +@inproceedings{cardei2005energy, + title={Energy-efficient target coverage in wireless sensor networks}, + author={Cardei, Mihaela and Thai, My T and Li, Yingshu and Wu, Weili}, + booktitle={INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE}, + volume={3}, + pages={1976-1984}, + year={2005}, + organization={IEEE} +} + +@ARTICLE{cardei2005improving, + title={Improving wireless sensor network lifetime through power aware organization}, + author={Cardei, Mihaela and Du, Ding-Zhu}, + journal={Wireless Networks}, + volume={11}, + number={3}, + pages={333-340}, + year={2005}, + publisher={Springer-Verlag New York, Inc.} +} + +@inproceedings{abrams2004set, + title={Set k-cover algorithms for energy efficient monitoring in wireless sensor networks}, + author={Abrams, Zo{\"e} and Goel, Ashish and Plotkin, Serge}, + booktitle={Proceedings of the 3rd international symposium on Information processing in sensor networks}, + pages={424-432}, + year={2004}, + organization={ACM} +} + +@ARTICLE{pujari2011high, + title={High-Energy-First (HEF) Heuristic for Energy-Efficient Target Coverage Problem.}, + author={Pujari, Arun K}, + journal={International Journal of Ad Hoc, Sensor \& Ubiquitous Computing}, + volume={2}, + number={1}, + year={2011} +} + +@INPROCEEDINGS{berman04, + author = {P. Berman and G. 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Berman and G. Calinescu and C. Shah and A. Zelikovsky}, + title = {Efficient energy management in sensor networks}, + booktitle = {Ad Hoc and Sensor Networks. Nova Science Publishers}, + year = {2005}, + publisher = {Nova Science Publisher} +} + +@inproceedings{lu2003coverage, + title={Coverage-aware self-scheduling in sensor networks}, + author={Lu, Jun and Suda, Tatsuya}, + booktitle={Computer Communications, 2003. CCW 2003. Proceedings. 2003 IEEE 18th Annual Workshop on}, + pages={117--123}, + year={2003}, + organization={IEEE} +} + +@incollection{prasad2007distributed, + title={Distributed algorithms for lifetime of wireless sensor networks based on dependencies among cover sets}, + author={Prasad, Sushil K and Dhawan, Akshaye}, + booktitle={High Performance Computing--HiPC 2007}, + pages={381-392}, + year={2007}, + publisher={Springer} +} + +@inproceedings{cardei2005maximum, + title={Maximum network lifetime in wireless sensor networks with adjustable sensing ranges}, + author={Cardei, Mihaela and Wu, Jie and Lu, Mingming and Pervaiz, Mohammad O}, + booktitle={Wireless And Mobile Computing, Networking And Communications, 2005.(WiMob'2005), IEEE International Conference on}, + volume={3}, + pages={438--445}, + year={2005}, + organization={IEEE} +} + +@inproceedings{garg98, + author = {Garg, Naveen and Koenemann, Jochen}, + title = {Faster and Simpler Algorithms for Multicommodity Flow and other Fractional Packing Problems.}, + booktitle = {Proceedings of the 39th Annual Symposium on Foundations of Computer Science}, + series = {FOCS '98}, + year = {1998}, + pages = {300-309}, +} + +@article{wang2010clique, + title={A clique base node scheduling method for wireless sensor networks}, + author={Wang, Lei and Wei, Ruizhong and Lin, Yaping and Wang, Bo}, + journal={Journal of Network and Computer Applications}, + volume={33}, + number={4}, + pages={383--396}, + year={2010}, + publisher={Elsevier} +} + +@article{yardibi2010distributed, + title={A distributed activity scheduling algorithm for wireless sensor networks with partial coverage}, + author={Yardibi, Tarik and Karasan, Ezhan}, + journal={Wireless Networks}, + volume={16}, + number={1}, + pages={213--225}, + year={2010}, + publisher={Springer} +} + +@article{conti2014mobile, + title={Mobile ad hoc networking: milestones, challenges, and new research directions}, + author={Conti, Marco and Giordano, Silvia}, + journal={Communications Magazine, IEEE}, + volume={52}, + number={1}, + pages={85--96}, + year={2014}, + publisher={IEEE} +} + +@inproceedings{idrees2014coverage, + title={Coverage and Lifetime Optimization in Heterogeneous Energy Wireless Sensor Networks}, + author={Idrees, Ali Kadhum and Deschinkel, Karine and Salomon, Michel and Couturier, Rapha{\"e}l}, + booktitle={ICN 2014, The Thirteenth International Conference on Networks}, + pages={49--54}, + year={2014} +} + +@inproceedings{xu2001geography, + title={Geography-informed energy conservation for ad hoc routing}, + author={Xu, Ya and Heidemann, John and Estrin, Deborah}, + booktitle={Proceedings of the 7th annual international conference on Mobile computing and networking}, + pages={70--84}, + year={2001}, + organization={ACM} +} + +@article{zhou2009variable, + title={Variable radii connected sensor cover in sensor networks}, + author={Zhou, Zongheng and Das, Samir R and Gupta, Himanshu}, + journal={ACM Transactions on Sensor Networks (TOSN)}, + volume={5}, + number={1}, + pages={8}, + year={2009}, + publisher={ACM} +} + +@article{wang2009parallel, + title={Parallel energy-efficient coverage optimization with maximum entropy clustering in wireless sensor networks}, + author={Wang, Xue and Ma, Junjie and Wang, Sheng}, + journal={Journal of Parallel and Distributed Computing}, + volume={69}, + number={10}, + pages={838--847}, + year={2009}, + publisher={Elsevier} +} + +@article{yan2008design, + title={Design and optimization of distributed sensing coverage in wireless sensor networks}, + author={Yan, Ting and Gu, Yu and He, Tian and Stankovic, John A}, + journal={ACM Transactions on Embedded Computing Systems (TECS)}, + volume={7}, + number={3}, + pages={33}, + year={2008}, + publisher={ACM} +} + +@article{he2012leveraging, + title={Leveraging prediction to improve the coverage of wireless sensor networks}, + author={He, Shibo and Chen, Jiming and Li, Xu and Shen, Xuemin and Sun, Youxian}, + journal={Parallel and Distributed Systems, IEEE Transactions on}, + volume={23}, + number={4}, + pages={701-712}, + year={2012}, + publisher={IEEE} +} + +@article{esnaashari2010learning, + title={A learning automata based scheduling solution to the dynamic point coverage problem in wireless sensor networks}, + author={Esnaashari, Mehdi and Meybodi, Mohammad Reza}, + journal={Computer Networks}, + volume={54}, + number={14}, + pages={2410--2438}, + year={2010}, + publisher={Elsevier} +} + + +@ARTICLE{Misra, + author = "S. Misra and M. P. Kumar and M. S. Obaidat", + title = "Connectivity preserving localized coverage algorithm for area monitoring using +wireless sensor networks ", + JOURNAL = {Computer Communications}, + VOLUME = {34}, + NUMBER = {12}, + PAGES = {1484-1496}, + YEAR = {2011}, +} + + + + +@article{rizvi2012a1, + title={A1: An energy efficient topology control algorithm for connected area coverage in wireless sensor networks}, + author={Rizvi, Sajjad and Qureshi, Hassaan Khaliq and Ali Khayam, Syed and Rakocevic, Veselin and Rajarajan, Muttukrishnan}, + journal={Journal of Network and Computer Applications}, + volume={35}, + number={2}, + pages={597--605}, + year={2012}, + publisher={Elsevier} +} + +@article{tran2009novel, + title={A novel gossip-based sensing coverage algorithm for dense wireless sensor networks}, + author={Tran-Quang, Vinh and Miyoshi, Takumi}, + journal={Computer Networks}, + volume={53}, + number={13}, + pages={2275--2287}, + year={2009}, + publisher={Elsevier} +} + +@ARTICLE{wang2011coverage, + title={Coverage problems in sensor networks: A survey}, + author={Wang, Bang}, + journal={ACM Computing Surveys (CSUR)}, + volume={43}, + number={4}, + pages={32}, + year={2011}, + publisher={ACM} +} + + +@article{luo2014parameterized, + title={Parameterized complexity of max-lifetime target coverage in wireless sensor networks}, + author={Luo, Weizhong and Wang, Jianxin and Guo, Jiong and Chen, Jianer}, + journal={Theoretical Computer Science}, + volume={518}, + pages={32--41}, + year={2014}, + publisher={Elsevier} +} + +@article{jarray2013lagrangean, + title={A Lagrangean-based heuristics for the target covering problem in wireless sensor network}, + author={Jarray, Fethi}, + journal={Applied Mathematical Modelling}, + volume={37}, + number={10}, + pages={6780--6785}, + year={2013}, + publisher={Elsevier} +} + +@article{naderan2013sensing, + title={Sensing task assignment via sensor selection for maximum target coverage in WSNs}, + author={Naderan, Marjan and Dehghan, Mehdi and Pedram, Hossein}, + journal={Journal of Network and Computer Applications}, + volume={36}, + number={1}, + pages={262--273}, + year={2013}, + publisher={Elsevier} +} + +@article{erdelj2013multiple, + title={Multiple point of interest discovery and coverage with mobile wireless sensors}, + author={Erdelj, Milan and Loscri, Valeria and Natalizio, Enrico and Razafindralambo, Tahiry}, + journal={Ad Hoc Networks}, + volume={11}, + number={8}, + pages={2288--2300}, + year={2013}, + publisher={Elsevier} +} + +@article{tian2014distributed, + title={Distributed Multitarget Probabilistic Coverage Control Algorithm for Wireless Sensor Networks}, + author={Tian, Ying and Ou, Yang and Hamid, Reza Karimi and Liu, Yan Tao and Han, Jian Qun}, + journal={Mathematical Problems in Engineering}, + volume={2014}, + pages={1--8}, + year={2014}, + publisher={Hindawi Publishing Corporation} +} + +@article{castano2013column, + title={A column generation approach to extend lifetime in wireless sensor networks with coverage and connectivity constraints}, + author={Casta{\~n}o, Fabian and Rossi, Andr{\'e} and Sevaux, Marc and Velasco, Nubia}, + journal={Computers \& Operations Research}, + year={2013}, + publisher={Elsevier} +} + +@article{yu2013cwsc, + title={CWSC: Connected< i> k-coverage working sets construction algorithm in wireless sensor networks}, + author={Yu, Jiguo and Deng, Xiu and Yu, Dongxiao and Wang, Guanghui and Gu, Xin}, + journal={AEU-International Journal of Electronics and Communications}, + volume={67}, + number={11}, + pages={937--946}, + year={2013}, + publisher={Elsevier} +} + +@article{cheng2014achieving, + title={Achieving Asymmetric Sensing Coverage for Duty Cycled Wireless Sensor Networks}, + author={Cheng, Long and He, Tian and Niu, Jianwei and Du, David HC}, + journal={IEEE Transactions on Parallel and Distributed Systems}, + pages={1--11}, + year={2014}, + publisher={IEEE} +} + +@article{yang2013energy, + title={Energy-Efficient Probabilistic Area Coverage in Wireless Sensor Networks}, + author={Yang, Qianqian and He, Shibo and Li, Junkun and Chen, Jiming and Sun, Youxian}, + journal={IEEE Transactions on Vehicular Technology}, + year={2013}, + publisher={IEEE} +} + +@inproceedings{kim2013maximum, + title={Maximum lifetime of reinforced barrier-coverage in Wireless Sensor Networks}, + author={Kim, Hyunbum and Cobb, Jorge A}, + booktitle={Networks (ICON), 2013 19th IEEE International Conference on}, + pages={1--6}, + year={2013}, + organization={IEEE} +} + + + +@inproceedings{chang2012joint, + title={Joint energy-balanced and full-coverage mechanism using sensing range control for maximizing network lifetime in WSNs}, + author={Chang, Chih-Yung and Yu, Gwo-Jong and Lin, Chih-Yu and Wang, Tzu-Lin}, + booktitle={Ubiquitous and Future Networks (ICUFN), 2012 Fourth International Conference on}, + pages={34--39}, + year={2012}, + organization={IEEE} +} + + + +@inproceedings{al2013energy, + title={Energy-efficient k-coverage scheduling algorithm for sensor networks with adjustable sensing range}, + author={Al-Shalabi, Ahmed Abdullah and Manaf, Mazani}, + booktitle={Wireless Technology and Applications (ISWTA), 2013 IEEE Symposium on}, + pages={316--321}, + year={2013}, + organization={IEEE} +} + +@inproceedings{kim2012sleep, + title={On sleep-wakeup scheduling of non-penetrable barrier-coverage of wireless sensors}, + author={Kim, Donghyun and Kim, Jiwoong and Li, Deying and Kwon, Sung-Sik and Tokuta, Alade O}, + booktitle={Global Communications Conference (GLOBECOM), 2012 IEEE}, + pages={321--327}, + year={2012}, + organization={IEEE} +} + +@article{li2013survey, + title={A Survey on Topology Control in Wireless Sensor Networks: Taxonomy, Comparative Study, and Open Issues}, + author={Li, Mo and Vasilakos, Athanasios V}, + journal={Proceedings of the IEEE}, + volume={101}, + number={12}, + year={2013} +} + + +@inproceedings{wang2007energy, + title={Energy efficient coverage with variable sensing radii in wireless sensor networks}, + author={Wang, Jiong and Medidi, Sirisha}, + booktitle={Wireless and Mobile Computing, Networking and Communications, 2007. WiMOB 2007. Third IEEE International Conference on}, + pages={61--61}, + year={2007}, + organization={IEEE} +} + +@inproceedings{vu2009delaunay, + title={Delaunay-triangulation based complete coverage in wireless sensor networks}, + author={Vu, Chinh T and Li, Yingshu}, + booktitle={Pervasive Computing and Communications, 2009. PerCom 2009. IEEE International Conference on}, + pages={1--5}, + year={2009}, + organization={IEEE} +} + +@article{yang2014energy, +author={Yang, Q. and He, S. and Li, J. and Chen, J. and Sun, Y.}, +journal={Vehicular Technology, IEEE Transactions on}, +title={Energy-Efficient Probabilistic Area Coverage in Wireless Sensor Networks}, +volume={PP}, +number={99}, +pages={1-1}, +year={2014}, + +} + +@inproceedings{ling2009energy, + title={Energy efficient adaptive sensing for dynamic coverage in wireless sensor networks}, + author={Ling, Hui and Znati, Taieb}, + booktitle={Wireless Communications and Networking Conference, 2009. WCNC 2009. IEEE}, + pages={1--6}, + year={2009}, + organization={IEEE} +} + +@inproceedings{diongue2013alarm, + title={ALARM: An energy aware sleep scheduling algorithm for lifetime maximization in wireless sensor networks}, + author={Diongue, Dame and Thiare, Ousmane}, + booktitle={Wireless Technology and Applications (ISWTA), 2013 IEEE Symposium on}, + pages={74--79}, + year={2013}, + organization={IEEE} +} + + +@INPROCEEDINGS{shi2009, +author={Qinghua Shi and Xiaozhen Wei and Yu Peng and Shiqian Li}, +booktitle={Computer Network and Multimedia Technology, 2009. CNMT 2009. International Symposium on}, +title={An Energy-Efficient Central-Scheduling Scheme in Wireless Sensor Networks}, +year={2009}, +month={Jan}, +pages={1-4}, +} + +@inproceedings{qu2013distributed, + title={A distributed area coverage algorithm for maintenance of randomly distributed sensors with adjustable sensing range}, + author={Qu, Yipeng and Georgakopoulos, Stavros V}, + booktitle={Global Communications Conference (GLOBECOM), 2013 IEEE}, + pages={286--291}, + year={2013}, + organization={IEEE} +} + +@inproceedings{xin2009area, + title={The area coverage algorithm to maintain connectivity for WSN}, + author={Xin, He and Ke, Yin and Xiaolin, Gui}, + booktitle={Computer and Information Technology, 2009. CIT'09. Ninth IEEE International Conference on}, + volume={2}, + pages={81--86}, + year={2009}, + organization={IEEE} +} + +\fi + +@article{huang2005coverage, + title={The coverage problem in a wireless sensor network}, + author={Huang, Chi-Fu and Tseng, Yu-Chee}, + journal={Mobile Networks and Applications}, + volume={10}, + number={4}, + pages={519--528}, + year={2005}, + publisher={Springer-Verlag New York, Inc.} +} + + +@techreport{Idrees2, + author = {Idrees, Ali Kadhum and Deschinkel, Karine and Salomon, Michel and Couturier, Rapha{\"e}l}, + institution = {University of Franche-Comte - FEMTO-ST Institute, DISC Research Department}, + title = {Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}, + number = {DISC2014-X}, + month = {Octobre}, + year = {2014} +} + +@article{puccinelli2005wireless, + title={Wireless sensor networks: applications and challenges of ubiquitous sensing}, + author={Puccinelli, Daniele and Haenggi, Martin}, + journal={Circuits and Systems Magazine, IEEE}, + volume={5}, + number={3}, + pages={19--31}, + year={2005}, + publisher={IEEE} +} + +@article{akyildiz2002wireless, + title={Wireless sensor networks: a survey}, + author={Akyildiz, Ian F and Su, Weilian and Sankarasubramaniam, Yogesh and Cayirci, Erdal}, + journal={Computer networks}, + volume={38}, + number={4}, + pages={393--422}, + year={2002}, + publisher={Elsevier} +} + +@article{anastasi2009energy, + title={Energy conservation in wireless sensor networks: A survey}, + author={Anastasi, Giuseppe and Conti, Marco and Di Francesco, Mario and Passarella, Andrea}, + journal={Ad Hoc Networks}, + volume={7}, + number={3}, + pages={537--568}, + year={2009}, + publisher={Elsevier} +} + +@article{yick2008wireless, + title={Wireless sensor network survey}, + author={Yick, Jennifer and Mukherjee, Biswanath and Ghosal, Dipak}, + journal={Computer networks}, + volume={52}, + number={12}, + pages={2292--2330}, + year={2008}, + publisher={Elsevier} +} + +@article{rault2014energy, + title={Energy efficiency in wireless sensor networks: A top-down survey}, + author={Rault, Tifenn and Bouabdallah, Abdelmadjid and Challal, Yacine}, + journal={Computer Networks}, + volume={67}, + pages={104--122}, + year={2014}, + publisher={Elsevier} +} + +@article{zhu2012survey, + title={A survey on coverage and connectivity issues in wireless sensor networks}, + author={Zhu, Chuan and Zheng, Chunlin and Shu, Lei and Han, Guangjie}, + journal={Journal of Network and Computer Applications}, + volume={35}, + number={2}, + pages={619--632}, + year={2012}, + publisher={Elsevier} +} + +@article{ammari2012centralized, + title={Centralized and clustered k-coverage protocols for wireless sensor networks}, + author={Ammari, Habib M and Das, Sajal K}, + journal={Computers, IEEE Transactions on}, + volume={61}, + number={1}, + pages={118--133}, + year={2012}, + publisher={IEEE} +} + +@article{rebai2014branch, + title={A Branch and Bound Algorithm for the Critical Grid Coverage Problem in Wireless Sensor Networks}, + author={Rebai, Maher and Le Berre, Matthieu and Hnaien, Faicel and Snoussi, Hichem and Khoukhi, Lyes}, + journal={International Journal of Distributed Sensor Networks}, + volume={2014}, + year={2014}, + publisher={Hindawi Publishing Corporation} +} + +@inproceedings{rebai2013linear, + title={Linear models for the total coverage problem in wireless sensor networks}, + author={Rebai, Maher and Snoussi, Hichem and Khoukhi, I and Hnaien, Faicel}, + booktitle={Modeling, Simulation and Applied Optimization (ICMSAO), 2013 5th International Conference on}, + pages={1--4}, + year={2013}, + organization={IEEE} +} + +@article{nayak2010wireless, + title={Wireless sensor and actuator networks}, + author={Nayak, Amiya and Stojmenovic, Ivan}, + journal={John-Whiley \& sons}, + year={2010}, + publisher={Wiley Online Library} +} + +@article{li2011transforming, + title={Transforming complete coverage algorithms to partial coverage algorithms for wireless sensor networks}, + author={Li, Yingshu and Vu, Chinh and Ai, Chunyu and Chen, Guantao and Zhao, Yi}, + journal={Parallel and Distributed Systems, IEEE Transactions on}, + volume={22}, + number={4}, + pages={695--703}, + year={2011}, + publisher={IEEE} +} + +@inproceedings{liu2014generalized, + title={A generalized coverage-preserving scheduling in WSNs: A case study in structural health monitoring}, + author={Liu, Xuefeng and Cao, Jiannong and Tang, Shaojie and Guo, Peng}, + booktitle={INFOCOM, 2014 Proceedings IEEE}, + pages={718--726}, + year={2014}, + organization={IEEE} +} + +@inproceedings{wang2010preserving, + title={Preserving target area coverage in wireless sensor networks by using computational geometry}, + author={Wang, San-Yuan and Shih, Kuei-Ping and Chen, Yen-Da and Ku, Hsin-Hui}, + booktitle={Wireless Communications and Networking Conference (WCNC), 2010 IEEE}, + pages={1--6}, + year={2010}, + organization={IEEE} +} + +@inproceedings{cho2007distributed, + title={A Distributed node scheduling protocol considering sensing coverage in wireless sensor networks}, + author={Cho, Jaekyu and Kim, Gilsoo and Kwon, Taekyoung and Choi, Yanghee}, + booktitle={Vehicular Technology Conference, 2007. VTC-2007 Fall. 2007 IEEE 66th}, + pages={352--356}, + year={2007}, + organization={IEEE} +} + +@inproceedings{quang2008algorithm, + title={An algorithm for sensing coverage problem in wireless sensor networks}, + author={Quang, Vinh Tran and Miyoshi, Takumi}, + booktitle={Sarnoff Symposium, 2008 IEEE}, + pages={1--5}, + year={2008}, + organization={IEEE} +} + + +@inproceedings{lin2010partitioning, + title={Partitioning sensors by node coverage grouping in wireless sensor networks}, + author={Lin, Chow-Sing and Chen, Chih-Chung and Chen, An-Chi}, + booktitle={Parallel and Distributed Processing with Applications (ISPA), 2010 International Symposium on}, + pages={306--312}, + year={2010}, + organization={IEEE} +} + +@inproceedings{zaidi2009minimum, + title={On minimum cost coverage in wireless sensor networks}, + author={Zaidi, Syed Ali Raza and Hafeez, Maryam and Khayam, Syed A and McLernon, Desmond C and Ghogho, Mounir and Kim, K}, + booktitle={Information Sciences and Systems, 2009. CISS 2009. 43rd Annual Conference on}, + pages={213--218}, + year={2009}, + organization={IEEE} +} + +@article{dong2012distributed, + title={Distributed coverage in wireless ad hoc and sensor networks by topological graph approaches}, + author={Dong, Dezun and Liao, Xiangke and Liu, Kebin and Liu, Yunhao and Xu, Weixia}, + journal={Computers, IEEE Transactions on}, + volume={61}, + number={10}, + pages={1417--1428}, + year={2012}, + publisher={IEEE} +} + +@article{liu2010energy, + title={Energy conservation algorithms for maintaining coverage and connectivity in wireless sensor networks}, + author={Liu, L and Hu, B and Li, L}, + journal={IET communications}, + volume={4}, + number={7}, + pages={786--800}, + year={2010}, + publisher={IET} +} + +@inproceedings{aslanyan2013optimal, + title={Optimal node scheduling for integrated connected-coverage in wireless sensor networks}, + author={Aslanyan, Levon and Aslanyan, Hakob and Khosravi, Hamid}, + booktitle={Computer Science and Information Technologies (CSIT), 2013}, + pages={1--13}, + year={2013}, + organization={IEEE} +} + diff --git a/LiCO_Journal.blg b/LiCO_Journal.blg new file mode 100644 index 0000000..b096024 --- /dev/null +++ b/LiCO_Journal.blg @@ -0,0 +1,56 @@ +This is BibTeX, Version 0.99d (TeX Live 2012/Debian) +Capacity: max_strings=35307, hash_size=35307, hash_prime=30011 +The top-level auxiliary file: LiCO_Journal.aux +The style file: IEEEtran.bst +Reallocated singl_function (elt_size=4) to 100 items from 50. +Reallocated singl_function (elt_size=4) to 100 items from 50. +Reallocated singl_function (elt_size=4) to 100 items from 50. +Reallocated wiz_functions (elt_size=4) to 6000 items from 3000. +Reallocated singl_function (elt_size=4) to 100 items from 50. +Database file #1: LiCO_Journal.bib +-- IEEEtran.bst version 1.12 (2007/01/11) by Michael Shell. +-- http://www.michaelshell.org/tex/ieeetran/bibtex/ +-- See the "IEEEtran_bst_HOWTO.pdf" manual for usage information. + +Done. +You've used 38 entries, + 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Email: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, + michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}} + +\markboth{IEEE Communications Letters,~Vol.~XX, No.~Y, January 2015}% +{Shell \MakeLowercase{\textit{et al.}}: Bare Demo of IEEEtran.cls for Journals} + +\maketitle + +\begin{abstract} +The most important problem in a Wireless Sensor Network (WSN) is to optimize the +use of its limited energy provision, so that it can fulfill its monitoring task +as long as possible. Among known available approaches that can be used to +improve power management, lifetime coverage optimization provides activity +scheduling which ensures sensing coverage while minimizing the energy cost. In +this paper, we propose such an approach called Lifetime Coverage Optimization +protocol (LiCO). It is a hybrid of centralized and distributed methods: the +region of interest is first subdivided into subregions and our protocol is then +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 +leader selected after cooperation between nodes within the same subregion. The +novelty of our 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 protocols. +\end{abstract} + +% Note that keywords are not normally used for peerreview papers. +\begin{IEEEkeywords} +Wireless Sensor Networks, Area Coverage, Network lifetime, Optimization, Scheduling. +\end{IEEEkeywords} + +\IEEEpeerreviewmaketitle + +\section{Introduction} +\label{sec:introduction} + +\noindent The continuous progress in Micro Electro-Mechanical Systems (MEMS) and +wireless communication hardware has given rise to the opportunity to use large +networks of tiny sensors, called Wireless Sensor Networks +(WSN)~\cite{akyildiz2002wireless,puccinelli2005wireless}, to fulfill monitoring +tasks. A WSN consists of small low-powered sensors working together by +communicating with one another through multi-hop radio communications. Each node +can send the data it collects in its environment, thanks to its sensor, to the +user by means of sink nodes. The features of a WSN made it suitable for a wide +range of application in areas such as business, environment, health, industry, +military, and so on~\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 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 +a limited energy provision and it may be unsuitable or impossible to replace or +recharge it in most applications. Therefore it is necessary to deploy WSN with +high density in order to increase the reliability and to exploit node redundancy +thanks to energy-efficient activity scheduling approaches. Indeed, the overlap +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 +energy-efficient mechanisms have been suggested to retain energy and extend the +lifetime of the WSNs~\cite{rault2014energy}. + +%The sensor system ought to have a lifetime long enough to satisfy the application necessities. The lifetime coverage maximization is one of the fundamental requirements of any area coverage protocol in WSN implementation~\cite{nayak2010wireless}. In order to increase the reliability and prevent the possession of coverage holes (some parts are not covered in the area of interest) in the WSN, it is necessary to deploy the WSN with high density so as to increase the reliability and to exploit redundancy by using energy-efficient activity scheduling approaches. + +%From a certain standpoint, the high coverage ratio is required by many applications such as military and health-care. Therefore, a suitable number of sensors are being chosen so as to cover the area of interest, is the first challenge. Meanwhile, the sensor nodes have a limited capabilities in terms of memory, processing, communication, and battery power being the most important and critical one. So, the main question is: how to extend the lifetime coverage of WSN as long time as possible?. There are many energy-efficient mechanisms have been suggested to retain energy and extend the lifetime of the WSNs~\cite{rault2014energy}. + +%\uppercase{\textbf{Our contributions.}} + +This paper makes the following contributions. +\begin{enumerate} +\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 and + 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 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{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 +the literature. + +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, 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 {\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.} + +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 sensor 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 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 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 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. + +%The problem of k-coverage in WSNs was addressed~\cite{ammari2012centralized}. It mathematically formulated and the spacial sensor density for full k-coverage determined, where the relation between the communication range and the sensing range constructed by this work to retain the k-coverage and connectivity in WSN. After that, a four configuration protocols have proposed for treating the k-coverage in WSNs. + +%In~\cite{rebai2014branch}, the problem of full grid coverage is formulated using two integer linear programming models: the first, a model that takes into account only the overall coverage constraint; the second, both the connectivity and the full grid coverage constraints have taken into consideration. This work did not take into account the energy constraint. + +%Li et al.~\cite{li2011transforming} presented a framework to convert any complete coverage problem to a partial coverage one with any coverage ratio by means of executing a complete coverage algorithm to find a full coverage sets with virtual radii and transforming the coverage sets to a partial coverage sets by adjusting sensing radii. The properties of the original algorithms can be maintained by this framework and the transformation process has a low execution time. + +%The authors in~\cite{liu2014generalized} explained that in some applications of WSNs such as structural health monitoring (SHM) and volcano monitoring, the traditional coverage model which is a geographic area defined for individual sensors is not always valid. For this reason, they define a generalized coverage model, which is not need to have the coverage area of individual nodes, but only based on a function to determine whether a set of +%sensor nodes is capable of satisfy the requested monitoring task for a certain area. They have proposed two approaches to divide the deployed nodes into suitable cover sets, which can be used to prolong the network lifetime. + +%The work in~\cite{wang2010preserving} addressed the target area coverage problem by proposing a geometric-based activity scheduling scheme, named GAS, to fully cover the target area in WSNs. The authors deals with small area (target area coverage), which can be monitored by a single sensor instead of area coverage, which focuses on a large area that should be monitored by many sensors cooperatively. They explained that GAS is capable to monitor the target area by using a few sensors as possible and it can produce as many cover sets as possible. + +%Cho et al.~\cite{cho2007distributed} proposed a distributed node scheduling protocol, which can retain sensing coverage needed by applications +%and increase network lifetime via putting in sleep mode some redundant nodes. In this work, the effective sensing area (ESA) concept of a sensor node is used, which refers to the sensing area that is not overlapping with another sensor's sensing area. A sensor node and by compute it's ESA can be determine whether it will be active or sleep. The suggested work permits to sensor nodes to be in sleep mode opportunistically whilst fulfill the needed sensing coverage. + +%In~\cite{quang2008algorithm}, the authors defined a maximum sensing coverage region problem (MSCR) in WSNs and then proposed an algorithm to solve it. The +%maximum observed area fully covered by a minimum active sensors. In this work, the major property is to getting rid from the redundant sensors in high-density WSNs and putting them in sleep mode, and choosing a smaller number of active sensors so as to be sure that the full area is k-covered, and all events appeared in that area can be precisely and timely detected. This algorithm minimized the total energy consumption and increased the lifetime. + +%A novel method to divide the sensors in the WSN, called node coverage grouping (NCG) suggested~\cite{lin2010partitioning}. The sensors in the connectivity group are within sensing range of each other, and the data collected by them in the same group are supposed to be similar. They are proved that dividing n sensors via NCG into connectivity groups is a NP-hard problem. So, a heuristic algorithm of NCG with time complexity of $O(n^3)$ is proposed. +%For some applications, such as monitoring an ecosystem with extremely diversified environment, It might be premature assumption that sensors near to each other sense similar data. + +%In~\cite{zaidi2009minimum}, the problem of minimum cost coverage in which full coverage is performed by using the minimum number of sensors for an arbitrary geometric shape region is addressed. a geometric solution to the minimum cost coverage problem under a deterministic deployment is proposed. The probabilistic coverage solution which provides a relationship between the probability of coverage and the number of randomly deployed sensors in an arbitrarily-shaped region is suggested. The authors are clarified that with a random deployment about seven times more nodes are required to supply full coverage. + +%A graph theoretical framework for connectivity-based coverage with configurable coverage granularity was proposed~\cite{dong2012distributed}. A new coverage criterion and scheduling approach is proposed based on cycle partition. This method is capable of build a sparse coverage set in distributed way by means of only connectivity information. This work considers only the communication range of the sensor is smaller two times the sensing range of sensor. + +%Liu et al.~\cite{liu2010energy} formulated maximum disjoint sets problem for retaining coverage and connectivity in WSN. Two algorithms are proposed for solving this problem, heuristic algorithm and network flow algorithm. This work did not take into account the sensor node failure, which is an unpredictable event because the two solutions are full centralized algorithms. + +%The work that presented in~\cite{aslanyan2013optimal} solved the coverage and connectivity problem in sensor networks in +%an integrated way. The network lifetime is divided in a fixed number of rounds. A coverage bitmap of sensors of the domain has been generated in each round and based on this bitmap, it has been decided which sensors +%stay active or turn it to sleep. They checked the connection of the graph via laplacian of adjancy graph of active sensors in each round. the generation of coverage bitmap by using Minkowski technique, the network is able to providing the desired ratio of coverage. They have been defined the connected coverage problem as an optimization problem and a centralized genetic algorithm is used to find the solution. + +%Several algorithms to retain the coverage and maximize the network lifetime were proposed in~\cite{cardei2006energy,wang2011coverage}. + +%\uppercase{\textbf{shortcomings}}. In spite of many energy-efficient protocols for maintaining the coverage and improving the network lifetime in WSNs were proposed, non of them ensure the coverage for the sensing field with optimal minimum number of active sensor nodes, and for a long time as possible. For example, in a full centralized algorithms, an optimal solutions can be given by using optimization approaches, but in the same time, a high energy is consumed for the execution time of the algorithm and the communications among the sensors in the sensing field, so, the full centralized approaches are not good candidate to use it especially in large WSNs. Whilst, a full distributed algorithms can not give optimal solutions because this algorithms use only local information of the neighboring sensors, but in the same time, the energy consumption during the communications and executing the algorithm is highly lower. Whatever the case, this would result in a shorter lifetime coverage in WSNs. + +%\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 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} +\label{CI} + +\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 (perimeter covered by at least $k$ sensors). +%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{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 + \begin{tabular}{@{}cr@{}} + \includegraphics[width=75mm]{pcm.jpg} & \raisebox{3.25cm}{(a)} + \\ \includegraphics[width=75mm]{twosensors.jpg} & \raisebox{2.75cm}{(b)} + \end{tabular} + \caption{(a) Perimeter coverage of sensor node 0 and (b) finding the arc of + $u$'s perimeter covered by $v$.} + \label{pcm2sensors} +\end{figure} + +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~$v$ 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=137.5mm]{expcm.pdf} +\caption{Maximum coverage levels for perimeter of sensor node $0$.} +\label{expcm} +\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. + +\iffalse + +\begin{figure}[ht!] +\centering +\includegraphics[width=90mm]{ex2pcm.jpg} +\caption{Coverage intervals and contributing sensors for sensor node 0.} +\label{ex2pcm} +\end{figure} + +\fi + + \begin{table}[h] + \caption{Coverage intervals and contributing sensors for sensor node 0.} +\begin{tabular}{|c|c|c|c|c|c|c|c|c|} +\hline +\begin{tabular}[c]{@{}c@{}}Left \\ point \\ angle~$\alpha$ \end{tabular} & \begin{tabular}[c]{@{}c@{}}Interval \\ left \\ point\end{tabular} & \begin{tabular}[c]{@{}c@{}}Interval \\ right \\ point\end{tabular} & \begin{tabular}[c]{@{}c@{}}Maximum \\ coverage\\ level\end{tabular} & \multicolumn{5}{c|}{\begin{tabular}[c]{@{}c@{}}Set of sensors\\ involved \\ in coverage interval\end{tabular}} \\ \hline +0.0291 & 1L & 2L & 4 & 0 & 1 & 3 & 4 & \\ \hline +0.104 & 2L & 3R & 5 & 0 & 1 & 3 & 4 & 2 \\ \hline +0.3168 & 3R & 4R & 4 & 0 & 1 & 4 & 2 & \\ \hline +0.6752 & 4R & 1R & 3 & 0 & 1 & 2 & & \\ \hline +1.8127 & 1R & 5L & 2 & 0 & 2 & & & \\ \hline +1.9228 & 5L & 6L & 3 & 0 & 2 & 5 & & \\ \hline +2.3959 & 6L & 2R & 4 & 0 & 2 & 5 & 6 & \\ \hline +2.4258 & 2R & 7L & 3 & 0 & 5 & 6 & & \\ \hline +2.7868 & 7L & 8L & 4 & 0 & 5 & 6 & 7 & \\ \hline +2.8358 & 8L & 5R & 5 & 0 & 5 & 6 & 7 & 8 \\ \hline +2.9184 & 5R & 7R & 4 & 0 & 6 & 7 & 8 & \\ \hline +3.3301 & 7R & 9R & 3 & 0 & 6 & 8 & & \\ \hline +3.9464 & 9R & 6R & 4 & 0 & 6 & 8 & 9 & \\ \hline +4.767 & 6R & 3L & 3 & 0 & 8 & 9 & & \\ \hline +4.8425 & 3L & 8R & 4 & 0 & 3 & 8 & 9 & \\ \hline +4.9072 & 8R & 4L & 3 & 0 & 3 & 9 & & \\ \hline +5.3804 & 4L & 9R & 4 & 0 & 3 & 4 & 9 & \\ \hline +5.9157 & 9R & 1L & 3 & 0 & 3 & 4 & & \\ \hline +\end{tabular} + +\label{my-label} +\end{table} + + +%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. 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=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 $-2R$. +%\begin{figure}[ht!] +%\centering +%\includegraphics[width=75mm]{ex5pcm.jpg} +%\caption{Coverage intervals and contributing sensors for sensor node 0 having a part of its sensing range outside the border.} +%\label{ex5pcm} +%\end{figure} + +\subsection{The Main Idea} + +\noindent The WSN area of interest is, in a first step, divided into regular +homogeneous subregions using a divide-and-conquer algorithm. In a second step +our protocol will be executed in a distributed way in each subregion +simultaneously to schedule nodes' activities for one sensing period. + +As shown in figure~\ref{fig2}, node activity scheduling is produced by our +protocol in a periodic manner. Each period is divided into 4 stages: Information +(INFO) Exchange, Leader Election, Decision (the result of an optimization +problem), and Sensing. For each period there is exactly one set cover +responsible for the sensing task. Protocols based on a periodic scheme, like +LiCO, are more robust against an unexpected node failure. On the one hand, if +node failure is discovered before taking the decision, the corresponding sensor +node will not be considered by the optimization algorithm, and, on the other +hand, if the sensor failure happens after the decision, the sensing task of the +network will be temporarily affected: only during the period of sensing until a +new period starts, since a new set cover will take charge of the sensing task in +the next period. The energy consumption and some other constraints can easily be +taken into account since the sensors can update and then exchange their +information (including their residual energy) at the beginning of each period. +However, the pre-sensing phases (INFO Exchange, Leader Election, and Decision) +are energy consuming, even for nodes that will not join the set cover to monitor +the area. + +\begin{figure}[t!] +\centering +\includegraphics[width=80mm]{Model.pdf} +\caption{LiCO protocol.} +\label{fig2} +\end{figure} + +We define two types of packets to be used by LiCO protocol: +%\begin{enumerate}[(a)] +\begin{itemize} +\item INFO packet: sent by each sensor node to all the nodes inside a same + subregion for information exchange. +\item ActiveSleep packet: sent by the leader to all the nodes in its subregion + to transmit to them their respective status (stay Active or go Sleep) during + sensing phase. +\end{itemize} +%\end{enumerate} + +Five status are possible for a sensor node in the network: +%\begin{enumerate}[(a)] +\begin{itemize} +\item LISTENING: waits for a decision (to be active or not); +\item COMPUTATION: executes the optimization algorithm as leader to + determine the activities scheduling; +\item ACTIVE: node is sensing; +\item SLEEP: node is turned off; +\item COMMUNICATION: transmits or receives packets. +\end{itemize} +%\end{enumerate} +%Below, we describe each phase in more details. + +\subsection{LiCO Protocol Algorithm} + +\noindent The pseudocode implementing the protocol on a node is given below. +More precisely, Algorithm~\ref{alg:LiCO} gives a brief description of the +protocol applied by a sensor node $s_k$ where $k$ is the node index in the WSN. + +\begin{algorithm}[h!] + % \KwIn{all the parameters related to information exchange} +% \KwOut{$winer-node$ (: the id of the winner sensor node, which is the leader of current round)} + \BlankLine + %\emph{Initialize the sensor node and determine it's position and subregion} \; + + \If{ $RE_k \geq E_{th}$ }{ + \emph{$s_k.status$ = COMMUNICATION}\; + \emph{Send $INFO()$ packet to other nodes in subregion}\; + \emph{Wait $INFO()$ packet from other nodes in subregion}\; + \emph{Update K.CurrentSize}\; + \emph{LeaderID = Leader election}\; + \If{$ s_k.ID = LeaderID $}{ + \emph{$s_k.status$ = COMPUTATION}\; + + \If{$ s_k.ID $ is Not previously selected as a Leader }{ + \emph{ Execute the perimeter coverage model}\; + % \emph{ Determine the segment points using perimeter coverage model}\; + } + + \If{$ (s_k.ID $ is the same Previous Leader) And (K.CurrentSize = K.PreviousSize)}{ + + \emph{ Use the same previous cover set for current sensing stage}\; + } + \Else{ + \emph{Update $a^j_{ik}$; prepare data for IP~Algorithm}\; + \emph{$\left\{\left(X_{1},\dots,X_{l},\dots,X_{K}\right)\right\}$ = Execute Integer Program Algorithm($K$)}\; + \emph{K.PreviousSize = K.CurrentSize}\; + } + + \emph{$s_k.status$ = COMMUNICATION}\; + \emph{Send $ActiveSleep()$ to each node $l$ in subregion} \; + \emph{Update $RE_k $}\; + } + \Else{ + \emph{$s_k.status$ = LISTENING}\; + \emph{Wait $ActiveSleep()$ packet from the Leader}\; + \emph{Update $RE_k $}\; + } + } + \Else { Exclude $s_k$ from entering in the current sensing stage} +\caption{LiCO($s_k$)} +\label{alg:LiCO} +\end{algorithm} + +In this algorithm, K.CurrentSize and K.PreviousSize refer to the current size +and the previous size of the subnetwork in the subregion respectively. That +means the number of sensor nodes which are still alive. Initially, the sensor +node checks its remaining energy $RE_k$, which must be greater than a threshold +$E_{th}$ in order to participate in the current period. Each sensor node +determines its position and its subregion using an embedded GPS or a location +discovery algorithm. After that, all the sensors collect position coordinates, +remaining energy, sensor node ID, and the number of their one-hop live neighbors +during the information exchange. The sensors inside a same region cooperate to +elect a leader. The selection criteria for the leader, in order of priority, +are: larger number of neighbors, larger remaining energy, and then in case of +equality, larger index. Once chosen, the leader collects information to +formulate and solve the integer program which allows to construct the set of +active sensors in the sensing stage. + +%After the cooperation among the sensor nodes in the same subregion, the leader will be elected in distributed way, where each sensor node and based on it's information decide who is the leader. The selection criteria for the leader in order of priority are: larger number of neighbors, larger remaining energy, and then in case of equality, larger index. Thereafter, if the sensor node is leader, it will execute the perimeter-coverage model for each sensor in the subregion in order to determine the segment points which would be used in the next stage by the optimization algorithm of the LiCO protocol. Every sensor node is selected as a leader, it is executed the perimeter coverage model only one time during it's life in the network. + +% The leader has the responsibility of applying the integer program algorithm (see section~\ref{cp}), which provides a set of sensors planned to be active in the sensing stage. As leader, it will send an Active-Sleep packet to each sensor in the same subregion to inform it if it has to be active or not. On the contrary, if the sensor is not the leader, it will wait for the Active-Sleep packet to know its state for the sensing stage. + +\section{Lifetime Coverage problem formulation} +\label{cp} + +\noindent In this section, the coverage model is mathematically formulated. We +start with a description of the notations that will be used throughout the +section. + +First, we have the following sets: +\begin{itemize} +\item $S$ represents the set of WSN sensor nodes; +\item $A \subseteq S $ is the subset of alive sensors; +\item $I_j$ designates the set of coverage intervals (CI) obtained for + sensor~$j$. +\end{itemize} +$I_j$ refers to the set of coverage intervals which have been defined according +to the method introduced in subsection~\ref{CI}. For a coverage interval $i$, +let $a^j_{ik}$ denote the indicator function of whether sensor~$k$ is involved +in coverage interval~$i$ of sensor~$j$, that is: +\begin{equation} +a^j_{ik} = \left \{ +\begin{array}{lll} + 1 & \mbox{if sensor $k$ is involved in the } \\ + & \mbox{coverage interval $i$ of sensor $j$}, \\ + 0 & \mbox{otherwise.}\\ +\end{array} \right. +%\label{eq12} +\notag +\end{equation} +Note that $a^k_{ik}=1$ by definition of the interval. +%, where the objective is to find the maximum number of non-disjoint sets of sensor nodes such that each set cover can assure the coverage for the whole region so as to extend the network lifetime in WSN. Our model uses the PCL~\cite{huang2005coverage} in order to optimize the lifetime coverage in each subregion. +%We defined some parameters, which are related to our optimization model. In our model, we consider binary variables $X_{k}$, which determine the activation of sensor $k$ in the sensing round $k$. . +Second, we define several binary and integer variables. Hence, each binary +variable $X_{k}$ determines the activation of sensor $k$ in the sensing phase +($X_k=1$ if the sensor $k$ is active or 0 otherwise). $M^j_i$ is an integer +variable which measures the undercoverage for the coverage interval $i$ +corresponding to sensor~$j$. In the same way, the overcoverage for the same +coverage interval is given by the variable $V^j_i$. + +If we decide to sustain a level of coverage equal to $l$ all along the perimeter +of sensor $j$, we have to ensure that at least $l$ sensors involved in each +coverage interval $i \in I_j$ of sensor $j$ are active. According to the +previous notations, the number of active sensors in the coverage interval $i$ of +sensor $j$ is given by $\sum_{k \in A} a^j_{ik} X_k$. To extend the network +lifetime, the objective is to activate a minimal number of sensors in each +period to ensure the desired coverage level. As the number of alive sensors +decreases, it becomes impossible to reach the desired level of coverage for all +coverage intervals. Therefore we use variables $M^j_i$ and $V^j_i$ as a measure +of the deviation between the desired number of active sensors in a coverage +interval and the effective number. And we try to minimize these deviations, +first to force the activation of a minimal number of sensors to ensure the +desired coverage level, and if the desired level cannot be completely satisfied, +to reach a coverage level as close as possible to the desired one. + +%A system of linear constraints is imposed to attempt to keep the coverage level in each coverage interval to within specified PCL. Since it is physically impossible to satisfy all constraints simultaneously, each constraint uses a variable to either record when the coverage level is achieved, or to record the deviation from the desired coverage level. These additional variables are embedded into an objective function to be minimized. + +%\noindent In this paper, let us define some parameters, which are used in our protocol. +%the set of segment points is denoted by $I$, the set of all sensors in the network by $J$, and the set of alive sensors within $J$ by $K$. + + +%\noindent \begin{equation} +%X_{k} = \left \{ +%\begin{array}{l l} + % 1& \mbox{if sensor $k$ is active,} \\ +% 0 & \mbox{otherwise.}\\ +%\end{array} \right. +%\label{eq11} +%\notag +%\end{equation} + +%\noindent $M^j_i (undercoverage): $ integer value $\in \mathbb{N}$ for segment point $i$ of sensor $j$. + +%\noindent $V^j_i (overcoverage): $ integer value $\in \mathbb{N}$ for segment point $i$ of sensor $j$. + +Our coverage optimization problem can then be mathematically expressed as follows: +%Objective: +\begin{equation} %\label{eq:ip2r} +\left \{ +\begin{array}{ll} +\min \sum_{j \in S} \sum_{i \in I_j} (\alpha^j_i ~ M^j_i + \beta^j_i ~ V^j_i )&\\ +\textrm{subject to :}&\\ +\sum_{k \in A} ( a^j_{ik} ~ X_{k}) + M^j_i \geq l \quad \forall i \in I_j, \forall j \in S\\ +%\label{c1} +\sum_{k \in A} ( a^j_{ik} ~ X_{k}) - V^j_i \leq l \quad \forall i \in I_j, \forall j \in S\\ +% \label{c2} +% \Theta_{p}\in \mathbb{N}, &\forall p \in P\\ +% U_{p} \in \{0,1\}, &\forall p \in P\\ +X_{k} \in \{0,1\}, \forall k \in A +\end{array} +\right. +\notag +\end{equation} +$\alpha^j_i$ and $\beta^j_i$ are nonnegative weights selected according to the +relative importance of satisfying the associated level of coverage. For example, +weights associated with coverage intervals of a specified part of a region may +be given a relatively larger magnitude than weights associated with another +region. This kind of integer program is inspired from the model developed for +brachytherapy treatment planning for optimizing dose distribution +\cite{0031-9155-44-1-012}. The integer program must be solved by the leader in +each subregion at the beginning of each sensing phase, whenever the environment +has changed (new leader, death of some sensors). Note that the number of +constraints in the model is constant (constraints of coverage expressed for all +sensors), whereas the number of variables $X_k$ decreases over periods, since we +consider only alive sensors (sensors with enough energy to be alive during one +sensing phase) in the model. + +\section{Performance Evaluation and Analysis} +\label{sec:Simulation Results and Analysis} +%\noindent \subsection{Simulation Framework} + +\subsection{Simulation Settings} +%\label{sub1} + +The WSN area of interest is supposed to be divided into 16~regular subregions +and we use the same energy consumption than in our previous work~\cite{Idrees2}. +Table~\ref{table3} gives the chosen parameters settings. + +\begin{table}[ht] +\caption{Relevant parameters for network initialization.} +% title of Table +\centering +% used for centering table +\begin{tabular}{c|c} +% centered columns (4 columns) +\hline +Parameter & Value \\ [0.5ex] + +\hline +% inserts single horizontal line +Sensing field & $(50 \times 25)~m^2 $ \\ + +WSN size & 100, 150, 200, 250, and 300~nodes \\ +%\hline +Initial energy & in range 500-700~Joules \\ +%\hline +Sensing period & duration of 60 minutes \\ +$E_{th}$ & 36~Joules\\ +$R_s$ & 5~m \\ +%\hline +$\alpha^j_i$ & 0.6 \\ +% [1ex] adds vertical space +%\hline +$\beta^j_i$ & 0.4 +%inserts single line +\end{tabular} +\label{table3} +% is used to refer this table in the text +\end{table} +To obtain experimental results which are relevant, simulations with five +different node densities going from 100 to 300~nodes were performed considering +each time 25~randomly generated networks. The nodes are deployed on a field of +interest of $(50 \times 25)~m^2 $ in such a way that they cover the field with a +high coverage ratio. Each node has an initial energy level, in Joules, which is +randomly drawn in the interval $[500-700]$. If it's energy provision reaches a +value below the threshold $E_{th}=36$~Joules, the minimum energy needed for a +node to stay active during one period, it will no more participate in the +coverage task. This value corresponds to the energy needed by the sensing phase, +obtained by multiplying the energy consumed in active state (9.72 mW) with the +time in seconds for one period (3600 seconds), and adding the energy for the +pre-sensing phases. According to the interval of initial energy, a sensor may +be active during at most 20 periods. + +The values of $\alpha^j_i$ and $\beta^j_i$ have been chosen to ensure a good +network coverage and a longer WSN lifetime. We have given a higher priority for +the undercoverage (by setting the $\alpha^j_i$ with a larger value than +$\beta^j_i$) so as to prevent the non-coverage for the interval~$i$ of the +sensor~$j$. On the other hand, we have given a little bit lower value for +$\beta^j_i$ so as to minimize the number of active sensor nodes which contribute +in covering the interval. + +We introduce the following performance metrics to evaluate the efficiency of our +approach. + +%\begin{enumerate}[i)] +\begin{itemize} +\item {\bf Network Lifetime}: the lifetime is defined as the time elapsed until + the coverage ratio falls below a fixed threshold. $Lifetime_{95}$ and + $Lifetime_{50}$ denote, respectively, the amount of time during which is + guaranteed a level of coverage greater than $95\%$ and $50\%$. The WSN can + fulfill the expected monitoring task until all its nodes have depleted their + energy or if the network is no more connected. This last condition is crucial + because without network connectivity a sensor may not be able to send to a + base station an event it has sensed. +\item {\bf Coverage Ratio (CR)} : it measures how well the WSN is able to + observe the area of interest. In our case, we discretized the sensor field as + a regular grid, which yields the following equation: + \begin{equation*} + \scriptsize + \mbox{CR}(\%) = \frac{\mbox{$n$}}{\mbox{$N$}} \times 100 + \end{equation*} + where $n$ is the number of covered grid points by active sensors of every + subregions during the current sensing phase and $N$ is total number of grid + points in the sensing field. In our simulations we have set a layout of + $N~=~51~\times~26~=~1326$~grid points. +\item {\bf Active Sensors Ratio (ASR)}: a major objective of our protocol is to + activate nodes as few as possible, in order to minimize the communication + overhead and maximize the WSN lifetime. The active sensors ratio is defined as + follows: + \begin{equation*} + \scriptsize + \mbox{ASR}(\%) = \frac{\sum\limits_{r=1}^R \mbox{$|A_r^p|$}}{\mbox{$|S|$}} \times 100 + \end{equation*} + where $|A_r^p|$ is the number of active sensors in the subregion $r$ in the + current sensing period~$p$, $|S|$ is the number of sensors in the network, and + $R$ is the number of subregions. +\item {\bf Energy Consumption (EC)}: energy consumption can be seen as the total + energy consumed by the sensors during $Lifetime_{95}$ or $Lifetime_{50}$, + divided by the number of periods. The value of EC is computed according to + this formula: + \begin{equation*} + \scriptsize + \mbox{EC} = \frac{\sum\limits_{p=1}^{P} \left( E^{\mbox{com}}_p+E^{\mbox{list}}_p+E^{\mbox{comp}}_p + + E^{a}_p+E^{s}_p \right)}{P}, + \end{equation*} + where $P$ corresponds to the number of periods. The total energy consumed by + the sensors comes through taking into consideration four main energy + factors. The first one, denoted $E^{\scriptsize \mbox{com}}_p$, represents the + energy consumption spent by all the nodes for wireless communications during + period $p$. $E^{\scriptsize \mbox{list}}_p$, the next factor, corresponds to + the energy consumed by the sensors in LISTENING status before receiving the + decision to go active or sleep in period $p$. $E^{\scriptsize \mbox{comp}}_p$ + refers to the energy needed by all the leader nodes to solve the integer + program during a period. Finally, $E^a_{p}$ and $E^s_{p}$ indicate the energy + consumed by the WSN during the sensing phase (active and sleeping nodes). +\end{itemize} +%\end{enumerate} + +\subsection{Simulation Results} + +In order to assess and analyze the performance of our protocol we have +implemented LiCO protocol in OMNeT++~\cite{varga} simulator. Besides LiCO, two +other protocols, described in the next paragraph, will be evaluated for +comparison purposes. The simulations were run on a laptop DELL with an Intel +Core~i3~2370~M (2.4~GHz) processor (2 cores) whose MIPS (Million Instructions +Per Second) rate is equal to 35330. To be consistent with the use of a sensor +node based on Atmels AVR ATmega103L microcontroller (6~MHz) having a MIPS rate +equal to 6, the original execution time on the laptop is multiplied by 2944.2 +$\left(\frac{35330}{2} \times \frac{1}{6} \right)$. The modeling language for +Mathematical Programming (AMPL)~\cite{AMPL} is employed to generate the integer +program instance in a standard format, which is then read and solved by the +optimization solver GLPK (GNU linear Programming Kit available in the public +domain) \cite{glpk} through a Branch-and-Bound method. + +As said previously, the LiCO is compared with three other approaches. The first +one, called DESK, is a fully distributed coverage algorithm proposed by +\cite{ChinhVu}. The second one, called GAF~\cite{xu2001geography}, consists in +dividing the monitoring area into fixed squares. Then, during the decision +phase, in each square, one sensor is chosen to remain active during the sensing +phase. The last one, the DiLCO protocol~\cite{Idrees2}, is an improved version +of a research work we presented in~\cite{idrees2014coverage}. Let us notice that +LiCO and DiLCO protocols are based on the same framework. In particular, the +choice for the simulations of a partitioning in 16~subregions was chosen because +it corresponds to the configuration producing the better results for DiLCO. The +protocols are distinguished from one another by the formulation of the integer +program providing the set of sensors which have to be activated in each sensing +phase. DiLCO protocol tries to satisfy the coverage of a set of primary points, +whereas LiCO protocol objectif is to reach a desired level of coverage for each +sensor perimeter. In our experimentations, we chose a level of coverage equal to +one ($l=1$). + +\subsubsection{\bf Coverage Ratio} + +Figure~\ref{fig333} shows the average coverage ratio for 200 deployed nodes +obtained with the four protocols. DESK, GAF, and DiLCO provide a little better +coverage ratio with respectively 99.99\%, 99.91\%, and 99.02\%, against 98.76\% +produced by LiCO for the first periods. This is due to the fact that at the +beginning DiLCO protocol puts in sleep status more redundant sensors (which +slightly decreases the coverage ratio), while the three other protocols activate +more sensor nodes. Later, when the number of periods is beyond~70, it clearly +appears that LiCO provides a better coverage ratio and keeps a coverage ratio +greater than 50\% for longer periods (15 more compared to DiLCO, 40 more +compared to DESK). The energy saved by LiCO in the early periods allows later a +substantial increase of the coverage performance. + +\parskip 0pt +\begin{figure}[h!] +\centering + \includegraphics[scale=0.5] {R/CR.eps} +\caption{Coverage ratio for 200 deployed nodes.} +\label{fig333} +\end{figure} + +%When the number of periods increases, coverage ratio produced by DESK and GAF protocols decreases. This is due to dead nodes. However, DiLCO protocol maintains almost a good coverage from the round 31 to the round 63 and it is close to LiCO protocol. The coverage ratio of LiCO protocol is better than other approaches from the period 64. + +%because the optimization algorithm used by LiCO has been optimized the lifetime coverage based on the perimeter coverage model, so it provided acceptable coverage for a larger number of periods and prolonging the network lifetime based on the perimeter of the sensor nodes in each subregion of WSN. Although some nodes are dead, sensor activity scheduling based optimization of LiCO selected another nodes to ensure the coverage of the area of interest. i.e. DiLCO-16 showed a good coverage in the beginning then LiCO, when the number of periods increases, the coverage ratio decreases due to died sensor nodes. Meanwhile, thanks to sensor activity scheduling based new optimization model, which is used by LiCO protocol to ensure a longer lifetime coverage in comparison with other approaches. + + +\subsubsection{\bf Active Sensors Ratio} + +Having the less active sensor nodes in each period is essential to minimize the +energy consumption and so maximize the network lifetime. Figure~\ref{fig444} +shows the average active nodes ratio for 200 deployed nodes. We observe that +DESK and GAF have 30.36 \% and 34.96 \% active nodes for the first fourteen +rounds and DiLCO and LiCO protocols compete perfectly with only 17.92 \% and +20.16 \% active nodes during the same time interval. As the number of periods +increases, LiCO protocol has a lower number of active nodes in comparison with +the three other approaches, while keeping a greater coverage ratio as shown in +figure \ref{fig333}. + +\begin{figure}[h!] +\centering +\includegraphics[scale=0.5]{R/ASR.eps} +\caption{Active sensors ratio for 200 deployed nodes.} +\label{fig444} +\end{figure} + +\subsubsection{\bf Energy Consumption} + +We study the effect of the energy consumed by the WSN during the communication, +computation, listening, active, and sleep status for different network densities +and compare it for the four approaches. Figures~\ref{fig3EC}(a) and (b) +illustrate the energy consumption for different network sizes and for +$Lifetime95$ and $Lifetime50$. The results show that our LiCO protocol is the +most competitive from the energy consumption point of view. As shown in both +figures, LiCO consumes much less energy than the three other methods. One might +think that the resolution of the integer program is too costly in energy, but +the results show that it is very beneficial to lose a bit of time in the +selection of sensors to activate. Indeed the optimization program allows to +reduce significantly the number of active sensors and so the energy consumption +while keeping a good coverage level. + +\begin{figure}[h!] + \centering + \begin{tabular}{@{}cr@{}} + \includegraphics[scale=0.475]{R/EC95.eps} & \raisebox{2.75cm}{(a)} \\ + \includegraphics[scale=0.475]{R/EC50.eps} & \raisebox{2.75cm}{(b)} + \end{tabular} + \caption{Energy consumption per period for (a)~$Lifetime_{95}$ and (b)~$Lifetime_{50}$.} + \label{fig3EC} +\end{figure} + +%The optimization algorithm, which used by LiCO protocol, was improved the lifetime coverage efficiently based on the perimeter coverage model. + + %The other approaches have a high energy consumption due to activating a larger number of sensors. In fact, a distributed method on the subregions greatly reduces the number of communications and the time of listening so thanks to the partitioning of the initial network into several independent subnetworks. + + +%\subsubsection{Execution Time} + +\subsubsection{\bf Network Lifetime} + +We observe the superiority of LiCO and DiLCO protocols in comparison against the +two other approaches in prolonging the network lifetime. In +figures~\ref{fig3LT}(a) and (b), $Lifetime95$ and $Lifetime50$ are shown for +different network sizes. As highlighted by these figures, the lifetime +increases with the size of the network, and it is clearly the larger for DiLCO +and LiCO protocols. For instance, for a network of 300~sensors and coverage +ratio greater than 50\%, we can see on figure~\ref{fig3LT}(b) that the lifetime +is about two times longer with LiCO compared to DESK protocol. The performance +difference is more obvious in figure~\ref{fig3LT}(b) than in +figure~\ref{fig3LT}(a) because the gain induced by our protocols increases with +the time, and the lifetime with a coverage of 50\% is far more longer than with +95\%. + +\begin{figure}[h!] + \centering + \begin{tabular}{@{}cr@{}} + \includegraphics[scale=0.475]{R/LT95.eps} & \raisebox{2.75cm}{(a)} \\ + \includegraphics[scale=0.475]{R/LT50.eps} & \raisebox{2.75cm}{(b)} + \end{tabular} + \caption{Network Lifetime for (a)~$Lifetime_{95}$ \\ + and (b)~$Lifetime_{50}$.} + \label{fig3LT} +\end{figure} + +%By choosing the best suited nodes, for each period, by optimizing the coverage and lifetime of the network to cover the area of interest and by letting the other ones sleep in order to be used later in next rounds, LiCO protocol efficiently prolonged the network lifetime especially for a coverage ratio greater than $50 \%$, whilst it stayed very near to DiLCO-16 protocol for $95 \%$. + +Figure~\ref{figLTALL} compares the lifetime coverage of our protocols for +different coverage ratios. We denote by Protocol/50, Protocol/80, Protocol/85, +Protocol/90, and Protocol/95 the amount of time during which the network can +satisfy an area coverage greater than $50\%$, $80\%$, $85\%$, $90\%$, and $95\%$ +respectively, where Protocol is DiLCO or LiCO. Indeed there are applications +that do not require a 100\% coverage of the area to be monitored. LiCO might be +an interesting method since it achieves a good balance between a high level +coverage ratio and network lifetime. LiCO always outperforms DiLCO for the three +lower coverage ratios, moreover the improvements grow with the network +size. DiLCO is better for coverage ratios near 100\%, but in that case LiCO is +not so bad for the smallest network sizes. + +\begin{figure}[h!] +\centering \includegraphics[scale=0.5]{R/LTa.eps} +\caption{Network lifetime for different coverage ratios.} +\label{figLTALL} +\end{figure} + +%Comparison shows that LiCO protocol, which are used distributed optimization over the subregions, is the more relevance one for most coverage ratios and WSN sizes because it is robust to network disconnection during the network lifetime as well as it consume less energy in comparison with other approaches. LiCO protocol gave acceptable coverage ratio for a larger number of periods using new optimization algorithm that based on a perimeter coverage model. It also means that distributing the algorithm in each node and subdividing the sensing field into many subregions, which are managed independently and simultaneously, is the most relevant way to maximize the lifetime of a network. + + +\section{Conclusion and Future Works} +\label{sec:Conclusion and Future Works} + +In this paper we have studied the problem of lifetime coverage optimization in +WSNs. We designed a new protocol, called Lifetime Coverage Optimization, which +schedules nodes' activities (wake up and sleep stages) with the objective of +maintaining a good coverage ratio while maximizing the network lifetime. This +protocol is applied in a distributed way in regular subregions obtained after +partitioning the area of interest in a preliminary step. It works in periods and +is based on the resolution of an integer program to select the subset of sensors +operating in active status for each period. Our work is original in so far as it +proposes for the first time an integer program scheduling the activation of +sensors based on their perimeter coverage level, instead of using a set of +targets/points to be covered. + +%To cope with this problem, the area of interest is divided into a smaller subregions using divide-and-conquer method, and then a LiCO protocol for optimizing the lifetime coverage in each subregion. LiCO protocol combines two efficient techniques: network +%leader election, which executes the perimeter coverage model (only one time), the optimization algorithm, and sending the schedule produced by the optimization algorithm to other nodes in the subregion ; the second, sensor activity scheduling based optimization in which a new lifetime coverage optimization model is proposed. The main challenges include how to select the most efficient leader in each subregion and the best schedule of sensor nodes that will optimize the network lifetime coverage +%in the subregion. +%The network lifetime coverage in each subregion is divided into +%periods, each period consists of four stages: (i) Information Exchange, +%(ii) Leader Election, (iii) a Decision based new optimization model in order to +%select the nodes remaining active for the last stage, and (iv) Sensing. +We carried out several simulations to evaluate the proposed protocol. The +simulation results show that LiCO is more energy-efficient than other +approaches, with respect to lifetime, coverage ratio, active sensors ratio, and +energy consumption. +%Indeed, when dealing with large and dense WSNs, a distributed optimization approach on the subregions of WSN like the one we are proposed allows to reduce the difficulty of a single global optimization problem by partitioning it in many smaller problems, one per subregion, that can be solved more easily. We have identified different research directions that arise out of the work presented here. +We plan to extend our framework such that the schedules are planned for multiple +sensing periods. +%in order to compute all active sensor schedules in only one step for many periods; +We also want to improve our integer program to take into account heterogeneous +sensors from both energy and node characteristics point of views. +%the third, we are investigating new optimization model based on the sensing range so as to maximize the lifetime coverage in WSN; +Finally, it would be interesting to implement our protocol using a +sensor-testbed to evaluate it in real world applications. + +\section*{Acknowledgments} + +\noindent As a Ph.D. student, Ali Kadhum IDREES would like to gratefully +acknowledge the University of Babylon - IRAQ for financial support and Campus +France for the received support. This work has also been supported by the Labex +ACTION. + +\ifCLASSOPTIONcaptionsoff + \newpage +\fi + + +\bibliographystyle{IEEEtran} +%\bibliographystyle{IEEEbiographynophoto} +\bibliography{LiCO_Journal} + + + +%\begin{IEEEbiographynophoto}{Jane Doe} + + + + +\end{document} + + diff --git a/PeCO/ACM-Reference-Format-Journals.bst b/PeCO/ACM-Reference-Format-Journals.bst new file mode 100644 index 0000000..54089b5 --- /dev/null +++ b/PeCO/ACM-Reference-Format-Journals.bst @@ -0,0 +1,3349 @@ +%%% -*-BibTeX-*- +%%% ==================================================================== +%%% @BibTeX-style-file{ +%%% author = "Nelson H. F. Beebe, Boris Veytsman and Gerald Murray", +%%% version = "1.00", +%%% date = "18 January 2012", +%%% time = "11:48 EST", +%%% filename = "ACM-Reference-Format-Journals.bst", +%%% address = "University of Utah +%%% Department of Mathematics, 110 LCB +%%% 155 S 1400 E RM 233 +%%% Salt Lake City, UT 84112-0090 +%%% USA", +%%% telephone = "+1 801 581 5254", +%%% FAX = "+1 801 581 4148", +%%% URL = "http://www.math.utah.edu/~beebe", +%%% checksum = "available here: http://www.acm.org/publications/latex_style/CRC-journals.txt" +%%% email = "beebe@math.utah.edu, beebe@acm.org, +%%% beebe@computer.org, borisv@lk.net, murray@hq.acm.org", +%%% codetable = "ISO/ASCII", +%%% keywords = "ACM Transactions bibliography style; BibTeX", +%%% license = "public domain", +%%% supported = "yes", +%%% abstract = "", +%%% docstring = "The checksum field, above, is produced by WinMD5Free (v1.20) +%%% available from http://www.winmd5.com/?rid=winmd5," +%%% } +%%% ==================================================================== + +% "ACM Transactions" BibTeX style, ACM-Reference-Format-Journals.bst +% for BibTeX version 0.99c, LaTeX version 3.141 +% ACM extensions with code cleaned up, extended, and debugged 10--15-Nov-2008 +% Revised 17-APRIL-2008 (Nelson) +% Revised 13-MARCH-2011 (Boris/Gerry) +% Revised 23-MARCH-2011 (Boris/Gerry) +% Revised 27-MARCH-2011 (Boris/Gerry) +% Revised 15-APRIL-2011 (Boris/Gerry) +% Revised 27-SEPTEMBER-2011 (Boris) +% +% +% History (by Nelson) +% +% Based on 'acmtrans' (for ACM Journals) +% Date: 28th April 2008 +% +% 1. Avoid 'repeating' the 'month' values. +% 2. Avoid incorrectly italicizing the volume number. +% 3. Avoid non italicizing certain titles (book, inproceedings etc). +% 4. NO series if there is NO volume. +% 5. Sorting of reference with first surname. +% 6. Article no added for Inproceedings. +% +% Date: 07th May 2008 +% +% 1. Abbreviation list added +% +% Citation format: [author-last-name year] +% [author-last-name and author-last-name year] +% [author-last-name, author-last-name, and author-last-name year] +% [author-last-name et al. year] +% [author-last-name] +% author-last-name [year] +% [author-last-name and author-last-name] +% [author-last-name et al.] +% [year] or [year,year] +% year or year,year +% +% Reference list ordering: alphabetical by author or whatever passes +% for author in the absence of one. +% +% Features of the old acmtrans.bst: +% ================================= +% +% - all authors appear last name first. +% - all pages are listed xx-xx, (no pp.) and are at the end of the reference +% - publishers are identified as publisher, address +% - conferences papers (inproceedings) may give city of conference, +% date of conference, and journal that the proceedings appear in. +% - months abbreviated to max four letters (eg. Mar.) +% - volume of a series indicated after the title of the series +% - editors appear after edited title and are identified by a trailing "Eds." +% not in parentheses. Editor names are not given in small caps. +% (unless there is no author line) +% - names terminated with a period even if there is no first name. +% - editions are indicated trailing after the work, not in parentheses. +% - "et al." citations have a protected period to avoid bad spacing (jrh) +% - "address" required when publisher given +% - series (roman) and volume are in a sentence separate from (book-)title +% +% +% Features of chicago.bst: +% ======================= +% +% - full names used in citations, but abbreviated citations are available +% (see above) +% - if an entry has a "month", then the month and year are also printed +% as part of that bibitem. +% - all conjunctions use "and" instead of "\&" +% - major modification from Chicago Manual of Style (13th ed.) is that +% only the first author in a reference appears last name first- +% additional authors appear as J. Q. Public. +% - pages are listed as "pp. xx-xx" in all entry types except +% article entries. +% - book, inbook, and manual use "location: publisher" (or organization) +% for address and publisher. All other types list publishers separately. +% - "pp." are used to identify page numbers for all entry types except +% articles. +% - organization is used as a citation label if neither author nor editor +% is present (for manuals). +% - "et al." is used for long author and editor lists, or when "others" +% is used. +% +% Modifications and bug fixes from newapa.bst: +% =========================================== +% +% - added month, year to bib entries if month is present +% - fixed bug with In proceedings, added necessary comma after title +% - all conjunctions changed to "and" from "\&" +% - fixed bug with author labels in my.full.label: "et al." now is +% generated when "others" is an author name +% - major modification from Chicago Manual of Style (13th ed.) is that +% only the first author in a reference appears last name first- +% additional authors appear as J. Q. Public. +% - pages are listed as "pp. xx-xx" in all entry types except +% article entries. Unnecessary (IMHO) "()" around page numbers +% were removed, and page numbers now don't end with a period. +% - created chicago.sty for use with this bibstyle (required). +% - fixed bugs in FUNCTION {format.vol.num.pages} for missing volume, +% number, and /or pages. Renamed to format.journal.volume.number. +% - fixed bug in formatting booktitles: additional period an error if +% book has a volume. +% - fixed bug: editors usually given redundant period before next clause +% (format.editors.dot) removed. +% - added label support for organizations, if both author and editor +% are missing (from alpha.bst). If organization is too long, then +% the key field is used for abbreviated citations. +% - In proceedings or books of several volumes, no comma was written +% between the "Volume x" and the page numbers (this was intentional +% in newapa.bst). Fixed. +% - Some journals may not have volumes/numbers, only month/year (eg. +% IEEE Computer). Fixed bug in article style that assumed volume/number +% was always present. +% +% Original documentation for newapa.sty: +% ===================================== +% +% This version was made by modifying the master file made by +% Oren Patashnik (PATASHNIK@SCORE.STANFORD.EDU), and the 'named' BibTeX +% style of Peter F. Patel-Schneider. +% +% Copyright (C) 1985, all rights reserved. +% Copying of this file is authorized only if either +% (1) you make absolutely no changes to your copy, including name, or +% (2) if you do make changes, you name it something other than 'newapa.bst'. +% There are undoubtably bugs in this style. If you make bug fixes, +% improvements, etc. please let me know. My e-mail address is: +% spencer@cgrg.ohio.state.edu or 71160.3141@compuserve.com +% +% This style was made from 'plain.bst', 'named.bst', and 'apalike.bst', +% with lots of tweaking to make it look like APA style, along with tips +% from Young Ryu and Brian Reiser's modifications of 'apalike.bst'. +% +% +% Start of ACM-Reference-Format-Journals.bst +% +% Note: Many of the new bibentry 'fields' will only work with the +% 'ACM-Reference-Format-Journals.bst' file. Legacy .bib files (which will, in all probability, +% NOT contain these new fields) will _still_ work with the ACM-Reference-Format-Journals.bst. +% +% +ENTRY + { address + advisor + author + booktitle + chapter + city % jtb: added + date % jtb: added + edition + editor + howpublished + institution + journal + key + month + note + number + organization + pages + publisher + school + series + title + type + volume + year + % New keys recognized + issue % UTAH: used in, e.g., ACM SIGSAM Bulletin and ACM Communications in Computer Algebra + articleno + day % UTAH: needed for newspapers, weeklies, bi-weeklies + doi % UTAH + url % UTAH + bookpages % UTAH + numpages + lastaccessed % UTAH: used only for @Misc{...} + coden % UTAH + isbn % UTAH + isbn-13 % UTAH + issn % UTAH + lccn % UTAH + } + {} + { label.year extra.label sort.year sort.label } + +INTEGERS { output.state before.all mid.sentence after.sentence after.block } + +INTEGERS { show-isbn-10-and-13 } % initialized below in begin.bib + +INTEGERS { nameptr namesleft numnames } + +INTEGERS { multiresult } + +INTEGERS { len } + +INTEGERS { last.extra.num } + +STRINGS { s t t.org u } + +STRINGS { last.label next.extra } + +STRINGS { p1 p2 p3 page.count } + +FUNCTION { dump.stack.1 } +{ + duplicate$ "STACK[top] = [" swap$ * "]" * warning$ +} + +FUNCTION { dump.stack.2 } +{ + duplicate$ "STACK[top ] = [" swap$ * "]" * warning$ + swap$ + duplicate$ "STACK[top-1] = [" swap$ * "]" * warning$ + swap$ +} + +FUNCTION { empty.or.unknown } +{ + %% Examine the top stack entry, and push 1 if it is empty, or + %% consists only of whitespace, or is a string beginning with two + %% queries (??), and otherwise, push 0. + %% + %% This function provides a replacement for empty$, with the + %% convenient feature that unknown values marked by two leading + %% queries are treated the same as missing values, and thus, do not + %% appear in the output .bbl file, and yet, their presence in .bib + %% file(s) serves to mark values which are temporarily missing, but + %% are expected to be filled in eventually once more data is + %% obtained. The TeX User Group and BibNet bibliography archives + %% make extensive use of this practice. + %% + %% An empty string cannot serve the same purpose, because just as in + %% statistics data processing, an unknown value is not the same as an + %% empty value. + %% + %% At entry: stack = ... top:[string] + %% At exit: stack = ... top:[0 or 1] + + duplicate$ empty$ + { pop$ #1 } + { #1 #2 substring$ "??" = } + if$ +} + +FUNCTION { writeln } +{ + %% In BibTeX style files, the sequences + %% + %% ... "one" "two" output + %% ... "one" "two" output.xxx + %% + %% ship "one" to the output file, possibly following by punctuation, + %% leaving the stack with + %% + %% ... "two" + %% + %% There is thus a one-string lag in output processing that must be + %% carefully handled to avoid duplicating a string in the output + %% file. Unless otherwise noted, all output.xxx functions leave + %% just one new string on the stack, and that model should be born + %% in mind when reading or writing function code. + %% + %% BibTeX's asynchronous buffering of output from strings from the + %% stack is confusing because newline$ bypasses the buffer. It + %% would have been so much easier for newline to be a character + %% rather than a state of the output-in-progress. + %% + %% The documentation in btxhak.dvi is WRONG: it says + %% + %% newline$ Writes onto the bbl file what's accumulated in the + %% output buffer. It writes a blank line if and only + %% if the output buffer is empty. Since write$ does + %% reasonable line breaking, you should use this + %% function only when you want a blank line or an + %% explicit line break. + %% + %% write$ Pops the top (string) literal and writes it on the + %% output buffer (which will result in stuff being + %% written onto the bbl file when the buffer fills + %% up). + %% + %% Examination of the BibTeX source code shows that write$ does + %% indeed behave as claimed, but newline$ sends a newline character + %% directly to the output file, leaving the stack unchanged. The + %% first line "Writes onto ... buffer." is therefore wrong. + %% + %% The original BibTeX style files almost always use "write$ newline$" + %% in that order, so it makes sense to hide that pair in a private + %% function like this one, named after a statement in Pascal, + %% the programming language embedded in the BibTeX Web program. + + write$ % output top-of-stack string + newline$ % immediate write of newline (not via stack) +} + +FUNCTION { init.state.consts } +{ + #0 'before.all := + #1 'mid.sentence := + #2 'after.sentence := + #3 'after.block := +} + +FUNCTION { output.nonnull } +{ % Stack in: ... R S T Stack out: ... R T File out: S + 's := + output.state mid.sentence = + { + ", " * write$ + } + { + output.state after.block = + { + add.period$ writeln + "\newblock " write$ + } + { + output.state before.all = + { + write$ + } + { + add.period$ " " * write$ + } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} + +FUNCTION { output.nonnull.dot.space } +{ % Stack in: ... R S T Stack out: ... R T File out: S + 's := + output.state mid.sentence = % { ". " * write$ } + { + ". " * write$ + } + { + output.state after.block = + { + add.period$ writeln "\newblock " write$ + } + { + output.state before.all = + { + write$ + } + { + add.period$ " " * write$ + } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} + +FUNCTION { output.nonnull.remove } +{ % Stack in: ... R S T Stack out: ... R T File out: S + 's := + output.state mid.sentence = + { + " " * write$ + } + { + output.state after.block = + { + add.period$ writeln "\newblock " write$ + } + { + output.state before.all = + { + write$ + } + { + add.period$ " " * write$ + } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} + +FUNCTION { output.nonnull.removenospace } +{ % Stack in: ... R S T Stack out: ... R T File out: S + 's := + output.state mid.sentence = + { + "" * write$ + } + { + output.state after.block = + { + add.period$ writeln "\newblock " write$ + } + { + output.state before.all = + { + write$ + } + { + add.period$ " " * write$ + } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} + +FUNCTION { output } +{ % discard top token if empty, else like output.nonnull + duplicate$ empty.or.unknown + 'pop$ + 'output.nonnull + if$ +} + +FUNCTION { output.dot.space } +{ % discard top token if empty, else like output.nonnull.dot.space + duplicate$ empty.or.unknown + 'pop$ + 'output.nonnull.dot.space + if$ +} + +FUNCTION { output.removenospace } +{ % discard top token if empty, else like output.nonnull.removenospace + duplicate$ empty.or.unknown + 'pop$ + 'output.nonnull.removenospace + if$ +} + +FUNCTION { output.check } +{ % like output, but warn if key name on top-of-stack is not set + 't := + duplicate$ empty.or.unknown + { pop$ "empty " t * " in " * cite$ * warning$ } + 'output.nonnull + if$ +} + +FUNCTION { output.check.dot.space } +{ % like output.dot.space, but warn if key name on top-of-stack is not set + 't := + duplicate$ empty.or.unknown + { pop$ "empty " t * " in " * cite$ * warning$ } + 'output.nonnull.dot.space + if$ +} + +FUNCTION { fin.block } +{ % functionally, but not logically, identical to fin.entry + add.period$ + writeln +} + +FUNCTION { fin.entry } +{ + add.period$ + writeln +} + +FUNCTION { new.sentence } +{ % update sentence state, with neither output nor stack change + output.state after.block = + 'skip$ + { + output.state before.all = + 'skip$ + { after.sentence 'output.state := } + if$ + } + if$ +} + +FUNCTION { fin.sentence } +{ + add.period$ + write$ + new.sentence + "" +} + +FUNCTION { new.block } +{ + output.state before.all = + 'skip$ + { after.block 'output.state := } + if$ +} + +FUNCTION { output.coden } % UTAH +{ % output non-empty CODEN as one-line sentence (stack untouched) + coden empty.or.unknown + { } + { "\showCODEN{" coden * "}" * writeln } + if$ +} + +FUNCTION { format.articleno } +{ + articleno empty.or.unknown + { "" } + { + numpages empty.or.unknown + { "articleno field, but no numpages field, in " cite$ * warning$ } + { } + if$ + "Article " articleno * + } + if$ +} + +FUNCTION { format.year } +{ % push year string or "????" onto output stack + %% Because year is a mandatory field, we always force SOMETHING + %% to be output + year empty.or.unknown + { "????" } + { year } + if$ +} + +FUNCTION { format.day.month } +{ % push "day month " or "month " or "" onto output stack + day empty.or.unknown + { + month empty.or.unknown + { "" } + { month " " *} + if$ + } + { + month empty.or.unknown + { "" } + { day " " * month * " " *} + if$ + } + if$ +} + +FUNCTION { format.day.month.year } % UTAH +{ % if month is empty, push "" else push "(MON.)" or "(DD MON.)" + % Needed for frequent periodicals: 2008. ... New York Times C-1, C-2, C-17 (23 Oct.) + % acm-*.bst addition: prefix parenthesized date string with + % ", Article nnn " + articleno empty.or.unknown + { "" } + { ", " format.articleno * } + if$ + " (" * format.day.month * format.year * ")" * +} + +FUNCTION { output.day.month.year } % UTAH +{ % if month is empty value, do nothing; else output stack top and + % leave with new top string "(MON.)" or "(DD MON.)" + % Needed for frequent periodicals: 2008. ... New York Times C-1, C-2, C-17 (23 Oct.) + format.day.month.year + output.nonnull.remove +} + +FUNCTION { strip.doi } % UTAH +{ % Strip any Web address prefix to recover the bare DOI, leaving the + % result on the output stack, as recommended by CrossRef DOI + % documentation. + % For example, reduce "http://doi.acm.org/10.1145/1534530.1534545" to + % "10.1145/1534530.1534545". That is later typeset and displayed as + % doi:10.1145/1534530.1534545 as the LAST item in the reference list + % entry. Publisher Web sites wrap this with a suitable link to a real + % URL to resolve the DOI, and the master http://dx.doi.org/ address is + % preferred, since publisher-specific URLs can disappear in response + % to economic events. All journals are encouraged by the DOI + % authorities to use that typeset format and link procedures for + % uniformity across all publications that include DOIs in reference + % lists. + % The numeric prefix is guaranteed to start with "10.", so we use + % that as a test. + doi #1 #3 substring$ "10." = + { doi } + { + doi #1 #7 substring$ "http://" = + { + doi #8 doi text.length$ #7 - substring$ 't := % get modifiable copy of rest of DOI + + "INTERNAL STYLE-FILE ERROR" 's := + + % search for next "/" and assign its suffix to s + + { t text.length$ } + { + t #1 #1 substring$ "/" = + { + % save rest of string as true DOI (should be 10.xxxx/yyyy) + t #2 t text.length$ #1 - substring$ 's := + "" 't := % empty string t terminates the loop + } + { + % discard first character and continue loop: t <= substring(t,2,last) + t #2 t text.length$ #1 - substring$ 't := + } + if$ + } + while$ + + % check for valid DOI (should be 10.xxxx/yyyy) + s #1 #3 substring$ "10." = + { } + { "unrecognized DOI substring " s * " in DOI value [" * doi * "]" * warning$ } + if$ + + s % push the stripped DOI on the output stack + + } + { + "unrecognized DOI value [" doi * "]" * warning$ + doi % push the unrecognized original DOI on the output stack + } + if$ + } + if$ +} + +% +% Change by BV: added standard prefix to URL +% +FUNCTION { output.doi } % UTAH +{ % output non-empty DOI as one-line sentence (stack untouched) + doi empty.or.unknown + { } + { + %% NB: We want URLs at beginning of line to reduce likelihood of + %% BibTeX's nasty line wrapping after column 79, which then requires + %% manual (or automated) editing of the .bbl file to repair. + %% The \url{} macro strips percent-newlines, and is thus safe in + %% the presence of the line wrapping, but \path|...| and + %% \verb|...| do not. + "\showDOI{%" writeln + "\url{http://dx.doi.org/" strip.doi * "}}" * writeln + } + if$ +} + +FUNCTION { output.isbn } % UTAH +{ % output non-empty ISBN-10 and/or ISBN-13 as one-line sentences (stack untouched) + show-isbn-10-and-13 + { + %% show both 10- and 13-digit ISBNs + isbn empty.or.unknown + { } + { + "\showISBNx{" isbn * "}" * writeln + } + if$ + isbn-13 empty.or.unknown + { } + { + "\showISBNxiii{" isbn-13 * "}" * writeln + } + if$ + } + { + %% show 10-digit ISBNs only if 13-digit ISBNs not available + isbn-13 empty.or.unknown + { + isbn empty.or.unknown + { } + { + "\showISBNx{" isbn * "}" * writeln + } + if$ + } + { + "\showISBNxiii{" isbn-13 * "}" * writeln + } + if$ + } + if$ +} + +FUNCTION { output.issn } % UTAH +{ % output non-empty ISSN as one-line sentence (stack untouched) + issn empty.or.unknown + { } + { "\showISSN{" issn * "}" * writeln } + if$ +} + +FUNCTION { output.issue } +{ % output non-empty issue number as a one-line sentence (stack untouched) + issue empty.or.unknown + { } + { "Issue " issue * "." * writeln } + if$ +} + +FUNCTION { output.lccn } % UTAH +{ % return with stack untouched + lccn empty.or.unknown + { } + { "\showLCCN{" lccn * "}" * writeln } + if$ +} + +FUNCTION { output.note } % UTAH +{ % return with stack empty + note empty.or.unknown + { } + { "\shownote{" note add.period$ * "}" * writeln } + if$ +} + +FUNCTION { output.note.check } % UTAH +{ % return with stack empty + note empty.or.unknown + { "empty note in " cite$ * warning$ } + { "\shownote{" note add.period$ * "}" * writeln } + if$ +} + +% +% Changes by BV 2011/04/15. Do not output +% url if doi is defined +% +FUNCTION { output.url } % UTAH +{ % return with stack untouched + % output URL and associated lastaccessed fields + doi empty.or.unknown + { + url empty.or.unknown + { } + { + %% NB: We want URLs at beginning of line to reduce likelihood of + %% BibTeX's nasty line wrapping after column 79, which would require + %% manual (or automated) editing of the .bbl file to repair. However, + %% the \url{} macro handles the unwrapping job automatically. + "\showURL{%" writeln + lastaccessed empty.or.unknown + { "" } + { "Retrieved " lastaccessed * " from " * } + if$ + + %% The URL field may contain a semicolon-separated list of Web + %% addresses, and we locate and wrap each of them in \url{...}. + %% The simplistic approach of putting the entire list into the + %% macro argument is that the semicolons are typeset in a + %% typewriter font, and no space follows them. + %% + %% We therefore replace the original code + %% "\url{" * url * "}}" * writeln + %% with this character-at-a-time loop: + + "\url{" * + + url 't := % get modifiable copy of URL list + + { t text.length$ } + { + t #1 #1 substring$ ";" = + { % then split argument at separator + "};" * writeln + "\url{" + } + { % else concatenate nonblank character to argument + t #1 #1 substring$ " " = + { } + { t #1 #1 substring$ * } + if$ + } + if$ + + t #2 t text.length$ #1 - substring$ 't := + } + while$ + + "}}" * writeln + } + if$ + } + { } + if$ +} + +FUNCTION { output.year.check } +{ % warn if year empty, else output top string and leave " YEAR