X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Sensornets15.git/blobdiff_plain/ad0f1593c3a19abcc1abeed173f08282b0067936..8289f9fca3236b8d7083fd54b8e33d8dcd1220a2:/Example.tex?ds=inline diff --git a/Example.tex b/Example.tex index 94a8536..ac05f6c 100644 --- a/Example.tex +++ b/Example.tex @@ -27,7 +27,7 @@ \title{Distributed Lifetime Coverage Optimization Protocol \\in Wireless Sensor Networks} \author{\authorname{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier} -\affiliation{FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comte, Belfort, France} +\affiliation{FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comt\'e, Belfort, France} %\affiliation{\sup{2}Department of Computing, Main University, MySecondTown, MyCountry} \email{ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr} %\email{\{f\_author, s\_author\}@ips.xyz.edu, t\_author@dc.mu.edu} @@ -113,29 +113,30 @@ problem and distinguish our DiLCO protocol from the works presented in the literature. The most discussed coverage problems in literature -can be classified into three types \cite{li2013survey}: area coverage (where -every point inside an area is to be monitored), target coverage (where the main -objective is to cover only a finite number of discrete points called targets), -and barrier coverage (to prevent intruders from entering into the region of -interest). +can be classified into three types \cite{li2013survey}: area coverage \cite{Misra} where +every point inside an area is to be monitored, target coverage \cite{yang2014novel} where the main +objective is to cover only a finite number of discrete points called targets, +and barrier coverage \cite{Kumar:2005}\cite{kim2013maximum} to prevent intruders from entering into the region of interest. In \cite{Deng2012} authors transform the area coverage problem to the target coverage problem taking into account the intersection points among disks of sensors nodes or between disk of sensor nodes and boundaries. {\it In DiLCO protocol, the area coverage, i.e. the coverage of every point in the sensing region, is transformed to the coverage of a fraction of points called primary points. } + 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 +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. +and the set of active sensor nodes is decided at the beginning of each period \cite{ling2009energy}. Active node selection is determined based on the problem requirements (e.g. area -monitoring, connectivity, power efficiency). Different methods have been -proposed in literature. -{\it DiLCO protocol works in periods, where each period contains a preliminary +monitoring, connectivity, power efficiency). For instance, Jaggi et al. \cite{jaggi2006} +address the problem of maximizing network lifetime by dividing sensors into the maximum number of disjoint subsets such that each subset can ensure both coverage and connectivity. A greedy algorithm is applied once to solve this problem and the computed sets are activated in succession to achieve the desired network lifetime. +Vu \cite{chin2007}, Padmatvathy et al. \cite{pc10}, propose algorithms working in a periodic fashion where a cover set is computed at the beginning of each period. +{\it Motivated by these works, DiLCO 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 approaches, including centralized, distributed, and localized +Various approaches, including centralized, or distributed algorithms, have been proposed to extend the network lifetime. %For instance, in order to hide the occurrence of faults, or the sudden unavailability of %sensor nodes, some distributed algorithms have been developed in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02}. @@ -145,23 +146,35 @@ cooperatively by communicating with their neighbors which of them will remain in sleep mode for a certain period of time. The centralized algorithms~\cite{cardei2005improving,zorbas2010solving,pujari2011high} always provide nearly or close to optimal solution since the algorithm has global view -of the whole network, but such a method has the disadvantage of requiring high +of the whole network. But such a method has the disadvantage of requiring high communication costs, since the node (located at the base station) making the -decision needs information from all the sensor nodes in the area. +decision needs information from all the sensor nodes in the area and the amount of information can be huge. +{\it In order to be suitable for large-scale network, in the DiLCO protocol, the area coverage is divided into several smaller + subregions, and in each of one, a node called the leader is in charge for + selecting the active sensors for the current period.} -A large variety of coverage scheduling algorithms have been proposed. Many of +A large variety of coverage scheduling algorithms have been developed. Many of the existing algorithms, dealing with the maximization of the number of cover sets, are heuristics. These heuristics involve the construction of a cover set by including in priority the sensor nodes which cover critical targets, that is -to say targets that are covered by the smallest number of sensors. Other -approaches are based on mathematical programming formulations and dedicated +to say targets that are covered by the smallest number of sensors \cite{berman04,zorbas2010solving}. Other +approaches are based on mathematical programming formulations~\cite{cardei2005energy,5714480,pujari2011high,Yang2014} and dedicated techniques (solving with a branch-and-bound algorithms available in optimization solver). The problem is formulated as an optimization problem (maximization of the lifetime or number of cover sets) under target coverage and energy constraints. Column generation techniques, well-known and widely practiced techniques for solving linear programs with too many variables, have been also -used~\cite{castano2013column,rossi2012exact,deschinkel2012column}. +used~\cite{castano2013column,rossi2012exact,deschinkel2012column}. {\it In DiLCO protocol, each leader, in each subregion, solves an integer + program with a double objective consisting in minimizing the overcoverage and + limiting the undercoverage. This program is inspired from the work of + \cite{pedraza2006} where the objective is to maximize the number of cover + sets.} +% ***** Part which must be rewritten - Start + +% Start of Ali's papers catalog => there's no link between them or with our work +% (use of subregions; optimization based method; etc.) +\iffalse Diongue and Thiare~\cite{diongue2013alarm} proposed an energy aware sleep scheduling algorithm for lifetime maximization in wireless sensor networks (ALARM). The proposed approach permits to schedule redundant nodes according to @@ -192,9 +205,9 @@ algorithm in order to produce the set of active nodes which take the mission of sensing during the current epoch. After that, the produced schedule is sent to the sensor nodes in the network. -{\it In DiLCO protocol, the area coverage is divided into several smaller - subregions, and in each of which, a node called the leader is on charge for - selecting the active sensors for the current period.} +% What is the link between the previous work and this paragraph about DiLCO ? + + Yang et al.~\cite{yang2014energy} investigated full area coverage problem under the probabilistic sensing model in the sensor networks. They have studied the @@ -207,13 +220,11 @@ extend the network lifetime. The work proposed by \cite{qu2013distributed} considers the coverage problem in WSNs where each sensor has variable sensing radius. The final objective is to maximize the network coverage lifetime in WSNs. +\fi +% Same remark, no link with the two previous citations... -{\it In DiLCO protocol, each leader, in each subregion, solves an integer - program with a double objective consisting in minimizing the overcoverage and - limiting the undercoverage. This program is inspired from the work of - \cite{pedraza2006} where the objective is to maximize the number of cover - sets.} +% ***** Part which must be rewritten - End \iffalse @@ -773,7 +784,7 @@ 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 follow80ing performance metrics to evaluate +In the simulations, we introduce the following performance metrics to evaluate the efficiency of our approach: %\begin{enumerate}[i)] @@ -1024,12 +1035,12 @@ the performance of our approach, we compared it with two other approaches using many performance metrics like coverage ratio or network lifetime. We have also study the impact of the number of subregions chosen to subdivide the area of interest, considering different network sizes. The experiments show that -increasing the number of subregions allows to improves the lifetime. The more -there are subregions, the more the network is robust against random -disconnection resulting from dead nodes. However, for a given sensing field and -network size there is an optimal number of subregions. Therefore, in case of -our simulation context a subdivision in $16$~subregions seems to be the most -relevant. The optimal number of subregions will be investigated in the future. +increasing the number of subregions improves the lifetime. The more there are +subregions, the more the network is robust against random disconnection +resulting from dead nodes. However, for a given sensing field and network size +there is an optimal number of subregions. Therefore, in case of our simulation +context a subdivision in $16$~subregions seems to be the most relevant. The +optimal number of subregions will be investigated in the future. \iffalse \noindent In this paper, we have addressed the problem of the coverage and the lifetime