X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Sensornets15.git/blobdiff_plain/c6211cbac5722fd0bc5afa0836434bc4c13b9b79..c85ec6ad6701f39ea2cf8f521d146f09d45f7cfe:/Example.tex?ds=sidebyside diff --git a/Example.tex b/Example.tex index b461e0d..a7e85c3 100644 --- a/Example.tex +++ b/Example.tex @@ -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}. @@ -145,28 +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 @@ -199,9 +207,7 @@ the sensor nodes in the network. % What is the link between the previous work and this paragraph about DiLCO ? -{\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.} + Yang et al.~\cite{yang2014energy} investigated full area coverage problem under the probabilistic sensing model in the sensor networks. They have studied the @@ -214,13 +220,9 @@ 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 @@ -410,7 +412,7 @@ $X_{13}=( p_x + R_s * (0), p_y + R_s * (\frac{-\sqrt{2}}{2})) $. \fi -\subsection{The main idea} +\subsection{Main idea} \label{main_idea} \noindent We start by applying a divide-and-conquer algorithm to partition the @@ -426,7 +428,7 @@ executed simultaneously in each subregion. As shown in Figure~\ref{fig2}, the proposed DiLCO protocol is a periodic protocol where each period is decomposed into 4~phases: Information Exchange, -Leader Election , Decision, and Sensing. For each period there will be exactly +Leader Election, Decision, and Sensing. For each period there will be exactly one cover set in charge of the sensing task. A periodic scheduling is interesting because it enhances the robustness of the network against node failures. First, a node that has not enough energy to complete a period, or @@ -565,7 +567,7 @@ sensor in the subregion and then describe it in more detail. \fi \end{algorithm} \iffalse -The DiLCO protocol work in rounds and executed at each sensor node in the network , each sensor node can still sense data while being in +The DiLCO protocol work in rounds and executed at each sensor node in the network, each sensor node can still sense data while being in LISTENING mode. Thus, by entering the LISTENING mode at the beginning of each round, sensor nodes still executing sensing task while participating in the leader election and decision phases. More specifically, The DiLCO protocol algorithm works as follow: Initially, the sensor node check it's remaining energy in order to participate in the current round. Each sensor node determines it's position and it's subregion based Embedded GPS or Location Discovery Algorithm. After that, All the sensors collect position coordinates, current remaining energy, sensor node id, and the number of its one-hop live neighbors during the information exchange. It stores this information into a list L. @@ -581,7 +583,7 @@ objective is to find a maximum number of disjoint cover sets. To accomplish this goal, the authors proposed an integer program which forces undercoverage and overcoverage of targets to become minimal at the same time. They use binary variables $x_{jl}$ to indicate if sensor $j$ belongs to cover set $l$. In our -model, we consider binary variable $X_{j}$ which determine the activation of +model, we consider that the binary variable $X_{j}$ determines the activation of sensor $j$ in the sensing phase. We also consider primary points as targets. The set of primary points is denoted by $P$ and the set of sensors by $J$. @@ -638,7 +640,7 @@ U_{p} = \left \{ %\label{c1} %\sum_{t \in T} X_{j,t} \leq \frac{RE_j}{e_t} &\forall j \in J \\ %\label{c2} -\Theta_{p}\in \mathbb{N} , &\forall p \in P\\ +\Theta_{p}\in \mathbb{N}, &\forall p \in P\\ U_{p} \in \{0,1\}, &\forall p \in P \\ X_{j} \in \{0,1\}, &\forall j \in J \end{array} @@ -773,7 +775,7 @@ symmetric communication costs), and we set their respective size to 112 and is equal to 0.2575 mW. 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 +interval $[500-700]$. If its 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 @@ -782,7 +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)] @@ -840,7 +842,7 @@ Where: $A_r^t$ is the number of active sensors in the subregion $r$ during round 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 +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 @@ -1073,9 +1075,10 @@ difficult, but will reduce the communication overhead. \fi \section*{\uppercase{Acknowledgements}} -\noindent As a Ph.D. student, Ali Kadhum IDREES would like to gratefully +\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. +Campus France for the received support. This paper is also partially funded by +the Labex ACTION program (contract ANR-11-LABX-01-01). %\vfill \bibliographystyle{apalike}