From: Michel Salomon Date: Fri, 13 Feb 2015 13:55:29 +0000 (+0100) Subject: Michel - Minor corrections X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Sensornets15.git/commitdiff_plain/34d0f50338c2813bf9cc5b22535e90523d3a5926 Michel - Minor corrections --- diff --git a/Example.tex b/Example.tex index 41f84a1..3f5d44c 100644 --- a/Example.tex +++ b/Example.tex @@ -41,27 +41,27 @@ Optimization, Scheduling.} region) of interest to be monitored, while simultaneously preventing as much as possible a network failure due to battery-depleted nodes. In this paper we propose a protocol, called Distributed Lifetime Coverage Optimization protocol - (DiLCO), which maintains the coverage and improves the lifetime of a wireless + (DiLCO), which maintains the coverage and improves the lifetime of a wireless sensor network. First, we partition the area of interest into subregions using a classical divide-and-conquer method. Our DiLCO protocol is then distributed - on the sensor nodes in each subregion in a second step. To fulfill our - objective, the proposed protocol combines two effective techniques: a leader + on the sensor nodes in each subregion in a second step. To fulfill our + objective, the proposed protocol combines two effective techniques: a leader election in each subregion, followed by an optimization-based node activity - scheduling performed by each elected leader. This two-step process takes + scheduling performed by each elected leader. This two-step process takes place periodically, in order to choose a small set of nodes remaining active for sensing during a time slot. Each set is built to ensure coverage at a low - energy cost, allowing to optimize the network lifetime. - %More precisely, a - %period consists of four phases: (i)~Information Exchange, (ii)~Leader - %Election, (iii)~Decision, and (iv)~Sensing. - The decision process, which + energy cost, allowing to optimize the network lifetime. %More precisely, a + %period consists of four phases: (i)~Information Exchange, (ii)~Leader + %Election, (iii)~Decision, and (iv)~Sensing. The decision process, which results in an activity scheduling vector, is carried out by a leader node - through the solving of an integer program. - {\color{red} Simulations are conducted using the discret event simulator OMNET++. - We refer to the characterictics of a Medusa II sensor for the energy consumption and the time computation. - In comparison with two other existing methods, our approach is able to increase the WSN lifetime and provides - improved coverage performance. }} - + through the solving of an integer program. +% MODIF - BEGIN + Simulations are conducted using the discret event simulator + OMNET++. We refer to the characterictics of a Medusa II sensor for + the energy consumption and the computation time. In comparison with + two other existing methods, our approach is able to increase the WSN + lifetime and provides improved coverage performance. } +% MODIF - END \onecolumn \maketitle \normalsize \vfill @@ -101,11 +101,17 @@ the sensors for the sensing phase of the current period is obtained by solving an integer program. The resulting activation vector is broadcast by a leader to every node of its subregion. -{\color{red} Our previous paper ~\cite{idrees2014coverage} relies almost exclusively on the framework of the DiLCO approach and the coverage problem formulation. -In this paper we strengthen our simulations by taking into account the characteristics of a Medusa II sensor ~\cite{raghunathan2002energy} to measure the energy consumption and the computation time. -We have implemented two other existing approaches (a distributed one DESK ~\cite{ChinhVu} and a centralized one GAF ~\cite{xu2001geography}) in order to compare their performances with our approach. -We also focus on performance analysis based on the number of subregions. } - +% MODIF - BEGIN +Our previous paper ~\cite{idrees2014coverage} relies almost exclusively on the +framework of the DiLCO approach and the coverage problem formulation. In this +paper we made more realistic simulations by taking into account the +characteristics of a Medusa II sensor ~\cite{raghunathan2002energy} to measure +the energy consumption and the computation time. We have implemented two other +existing approaches (a distributed one, DESK ~\cite{ChinhVu}, and a centralized +one called GAF ~\cite{xu2001geography}) in order to compare their performances +with our approach. We also focus on performance analysis based on the number of +subregions. } +% MODIF - END The remainder of the paper continues with Section~\ref{sec:Literature Review} where a review of some related works is presented. The next section describes @@ -333,10 +339,10 @@ Active-Sleep packet to know its state for the coming sensing phase. \section{\uppercase{Coverage problem formulation}} \label{cp} -{\color{red} +% MODIF - BEGIN We formulate the coverage optimization problem with an integer program. The objective function consists in minimizing the undercoverage and the overcoverage of the area as suggested in \cite{pedraza2006}. -The area coverage problem is transformed to the coverage of a fraction of points called primary points. +The area coverage problem is expressed as the coverage of a fraction of points called primary points. Details on the choice and the number of primary points can be found in \cite{idrees2014coverage}. The set of primary points is denoted by $P$ and the set of sensors by $J$. As we consider a boolean disk coverage model, we use the boolean indicator $\alpha_{jp}$ which is equal to 1 if the primary point $p$ is in the sensing range of the sensor $j$. The binary variable $X_j$ represents the activation or not of the sensor $j$. So we can express the number of active sensors that cover the primary point $p$ by $\sum_{j \in J} \alpha_{jp} * X_{j}$. We deduce the overcoverage denoted by $\Theta_p$ of the primary point $p$ : \begin{equation} @@ -360,11 +366,11 @@ U_{p} = \left \{ \end{array} \right. \label{eq14} \end{equation} -There is, of course, a relationship between the three variables $X_j$, $\Theta_p$ and $U_p$ which can be formulated as follows : +There is, of course, a relationship between the three variables $X_j$, $\Theta_p$, and $U_p$ which can be formulated as follows : \begin{equation} \sum_{j \in J} \alpha_{jp} X_{j} - \Theta_{p}+ U_{p} =1, \forall p \in P \end{equation} -If the point $p$ is not covered, $U_p=1$, $\sum_{j \in J} \alpha_{jp} X_{j}=0$ and $\Theta_{p}=0$ by defintion, so the equality is satisfied. +If the point $p$ is not covered, $U_p=1$, $\sum_{j \in J} \alpha_{jp} X_{j}=0$ and $\Theta_{p}=0$ by definition, so the equality is satisfied. On the contrary, if the point $p$ is covered, $U_p=0$, and $\Theta_{p}=\left( \sum_{j \in J} \alpha_{jp} X_{j} \right)- 1$. \noindent Our coverage optimization problem can then be formulated as follows: \begin{equation} \label{eq:ip2r} @@ -385,7 +391,7 @@ X_{j} \in \{0,1\}, &\forall j \in J The objective function is a weighted sum of overcoverage and undercoverage. The goal is to limit the overcoverage in order to activate a minimal number of sensors while simultaneously preventing undercoverage. Both weights $w_\theta$ and $w_U$ must be carefully chosen in order to guarantee that the maximum number of points are covered during each period. -} +% MODIF - END diff --git a/ahswn.tex b/ahswn.tex index e2ef23c..d66df5d 100644 --- a/ahswn.tex +++ b/ahswn.tex @@ -18,7 +18,7 @@ \institute{FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comt\'e, France} -\def\received{Received 21 October 2014} +\def\received{Received 23 October 2014} \maketitle @@ -437,19 +437,20 @@ experimental results which are relevant. 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. -We chose as energy consumption model the one proposed proposed \linebreak -by~\cite{ChinhVu} and based on ~\cite{raghunathan2002energy} with slight -modifications. The energy consumed by the communications is added and the part -relative to a variable sensing range is removed. We also assume that the nodes -have the characteristics of the Medusa II sensor node platform -\cite{raghunathan2002energy}. A sensor node typically consists of four units: a -MicroController Unit, an Atmels AVR ATmega103L in case of Medusa II, to perform -the computations; a communication (radio) unit able to send and receive -messages; a sensing unit to collect data; a power supply which provides the -energy consumed by node. Except the battery, all the other unit can be switched -off to save energy according to the node status. Table~\ref{table4} summarizes -the energy consumed (in milliWatt per second) by a node for each of its possible -status. +We chose as energy consumption model the one proposed +by~\cite{ChinhVu} and based on ~\cite{raghunathan2002energy} with +slight modifications. The energy consumed by the communications is +added and the part relative to a variable sensing range is removed. We +also assume that the nodes have the characteristics of the Medusa II +sensor node platform \cite{raghunathan2002energy}. A sensor node +typically consists of four units: a MicroController Unit, an Atmels +AVR ATmega103L in case of Medusa II, to perform the computations; a +communication (radio) unit able to send and receive messages; a +sensing unit to collect data; a power supply which provides the energy +consumed by node. Except the battery, all the other unit can be +switched off to save energy according to the node status. +Table~\ref{table4} summarizes the energy consumed (in milliWatt per +second) by a node for each of its possible status. \begin{table} \caption{Energy consumption model.} @@ -527,7 +528,7 @@ the efficiency of our approach: where $n$ is the number of covered grid points by active sensors of every subregions during the current sensing phase and $N$ is the total number of grid points in the sensing field. In our simulations, we have a layout of $N = 51 - \times 26 = 1326$ grid points. + \times 26 = 1,326$ grid points. \item {{\bf Energy Consumption}:} energy consumption (EC) can be seen as the total amount of energy consumed by the sensors during $Lifetime_{95}$ @@ -591,7 +592,7 @@ nodes, and thus enables the extension of the lifetime. \begin{center} \scalebox{0.5}{\includegraphics{R/CR.pdf}} \end{center} -\caption{Coverage ratio} +\caption{Coverage ratio.} \label{fig3} \end{figure}