X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/blobdiff_plain/0ffd0e6c8834d66f5f7c08ad1752ad0879b8d791..04c09785f4cb24f21708a0e95672ce7bcc4290bf:/bare_conf.tex?ds=sidebyside diff --git a/bare_conf.tex b/bare_conf.tex index 959f692..1a9602b 100755 --- a/bare_conf.tex +++ b/bare_conf.tex @@ -1,6 +1,3 @@ - - - \documentclass[conference]{IEEEtran} \ifCLASSINFOpdf @@ -33,6 +30,10 @@ \usepackage{caption} \usepackage{multicol} +\usepackage{graphicx,epstopdf} +\epstopdfsetup{suffix=} +\DeclareGraphicsExtensions{.ps} +\DeclareGraphicsRule{.ps}{pdf}{.pdf}{`ps2pdf -dEPSCrop -dNOSAFER #1 \noexpand\OutputFile} \begin{document} @@ -87,13 +88,13 @@ several domains ranging from health care applications to military applications. A sensor network is composed of a large number of tiny sensing devices deployed in a region of interest. Each device has processing and wireless communication capabilities, which enable it to -sense its environment, to compute, to store information and to deliver +sense its environment, to compute, to store information, and to deliver report messages to a base station. %These sensor nodes run on batteries with limited capacities. To achieve a long life of the network, it is important to conserve battery power. Therefore, lifetime optimisation is one of the most critical issues in wireless sensor networks. One of the main design issues in Wireless Sensor Networks (WSNs) is to prolong the network lifetime, while achieving acceptable quality of service for applications. Indeed, sensor nodes have limited resources -in terms of memory, energy and computational power. +in terms of memory, energy, and computational power. Since sensor nodes have limited battery life and without being able to replace batteries, especially in remote and hostile environments, it @@ -129,8 +130,7 @@ reviews the related work in the field. Section~\ref{pd} is devoted to the scheduling strategy for energy-efficient coverage. Section~\ref{cp} gives the coverage model formulation which is used to schedule the activation of sensors. Section~\ref{exp} shows the -simulation results obtained using the discrete event simulator on -OMNET++ \cite{varga}. They fully demonstrate the usefulness of the +simulation results obtained using the discrete event simulator OMNeT++ \cite{varga}. They fully demonstrate the usefulness of the proposed approach. Finally, we give concluding remarks and some suggestions for future works in Section~\ref{sec:conclusion}. @@ -191,7 +191,7 @@ transmit information on an event in the area that it monitors. {\bf Activity scheduling} -Activitiy scheduling is to schedule the activation and deactivation of +Activity scheduling is to schedule the activation and deactivation of sensor nodes. The basic objective is to decide which sensors are in what states (active or sleeping mode) and for how long, so that the application coverage requirement can be guaranteed and the network @@ -333,7 +333,7 @@ lifetime increases compared with related work~\cite{Cardei:2005:IWS:1160086.1160098}. In~\cite{berman04}, the authors have formulated the lifetime problem and suggested another (LP) technique to solve this problem. A centralized solution based on the Garg-K\"{o}nemann -algorithm~\cite{garg98}, probably near +algorithm~\cite{garg98}, provably near the optimal solution, is also proposed. {\bf Our contribution} @@ -355,7 +355,7 @@ sections. decision is a good compromise between these two conflicting objectives. -\item {\bf Which node should make such a decision?} As mentioned in +\item {\bf Which node should make such a decision?} As mentioned in \cite{pc10}, both centralized and distributed algorithms have their own advantages and disadvantages. Centralized coverage algorithms have the advantage of requiring very low processing power from the @@ -365,10 +365,10 @@ sections. that there is a threshold in terms of network size to switch from a localized to a centralized algorithm. Indeed the exchange of messages in large networks may consume a considerable amount of - energy in a localized approach compared to a centralized one. Our + energy in a centralized approach compared to a distributed one. Our work does not consider only one leader to compute and to broadcast - the scheduling decision to all the sensors. When the network size - increases, the network is divided into many subregions and the + the scheduling decision to all the sensors. When the network size + increases, the network is divided into many subregions and the decision is made by a leader in each subregion. \end{itemize} @@ -418,7 +418,7 @@ each phase in more details. \subsection{Information exchange phase} Each sensor node $j$ sends its position, remaining energy $RE_j$, and -the number of local neighbors $NBR_j$ to all wireless sensor nodes in +the number of local neighbours $NBR_j$ to all wireless sensor nodes in its subregion by using an INFO packet and then listens to the packets sent from other nodes. After that, each node will have information about all the sensor nodes in the subregion. In our model, the @@ -437,7 +437,7 @@ independently for each round. All the sensor nodes cooperate to select WSNL. The nodes in the same subregion will select the leader based on the received information from all other nodes in the same subregion. The selection criteria in order of priority are: larger -number of neighbors, larger remaining energy, and then in case of +number of neighbours, larger remaining energy, and then in case of equality, larger index. \subsection{Decision phase} @@ -623,7 +623,7 @@ X_{j} \in \{0,1\}, &\forall j \in J sensing in the round (1 if yes and 0 if not); \item $\Theta_{p}$ : {\it overcoverage}, the number of sensors minus one that are covering the primary point $p$; -\item $U_{p}$ : {\it undercoverage}, indicates whether or not the principal point +\item $U_{p}$ : {\it undercoverage}, indicates whether or not the primary point $p$ is being covered (1 if not covered and 0 if covered). \end{itemize} @@ -692,7 +692,7 @@ active node will consume 12~joules during the sensing phase, while a sleeping node will use 0.002 joules. Each sensor node will not participate in the next round if its remaining energy is less than 12 joules. In all experiments the parameters are set as follows: -$R_s=5m$, $w_{\Theta}=1$, and $w_{U}=|P^2|$. +$R_s=5~m$, $w_{\Theta}=1$, and $w_{U}=|P^2|$. We evaluate the efficiency of our approach by using some performance metrics such as: coverage ratio, number of active nodes ratio, energy @@ -732,7 +732,7 @@ subregion. \parskip 0pt \begin{figure}[h!] \centering -\includegraphics[scale=0.55]{TheCoverageRatio150.eps} %\\~ ~ ~(a) +\includegraphics[scale=0.5]{TheCoverageRatio150g.eps} %\\~ ~ ~(a) \caption{The impact of the number of rounds on the coverage ratio for 150 deployed nodes} \label{fig3} \end{figure} @@ -742,7 +742,7 @@ subregion. 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. This point is assessed through the Active Sensors -Ratio, which is defined as follows: +Ratio (ASR), which is defined as follows: \begin{equation*} \scriptsize \mbox{ASR}(\%) = \frac{\mbox{Number of active sensors @@ -754,7 +754,7 @@ for 150 deployed nodes. \begin{figure}[h!] \centering -\includegraphics[scale=0.55]{TheActiveSensorRatio150.eps} %\\~ ~ ~(a) +\includegraphics[scale=0.5]{TheActiveSensorRatio150g.eps} %\\~ ~ ~(a) \caption{The impact of the number of rounds on the active sensors ratio for 150 deployed nodes } \label{fig4} \end{figure} @@ -772,7 +772,7 @@ lifetime of the network. \subsection{The impact of the number of rounds on the energy saving ratio} In this experiment, we consider a performance metric linked to energy. -This metric, called Energy Saving Ratio, is defined by: +This metric, called Energy Saving Ratio (ESR), is defined by: \begin{equation*} \scriptsize \mbox{ESR}(\%) = \frac{\mbox{Number of alive sensors during this round}} @@ -787,7 +787,7 @@ for all three approaches and for 150 deployed nodes. %\centering % \begin{multicols}{6} \centering -\includegraphics[scale=0.55]{TheEnergySavingRatio150.eps} %\\~ ~ ~(a) +\includegraphics[scale=0.5]{TheEnergySavingRatio150g.eps} %\\~ ~ ~(a) \caption{The impact of the number of rounds on the energy saving ratio for 150 deployed nodes} \label{fig5} \end{figure} @@ -802,11 +802,11 @@ number of rounds increases the two leaders' strategy becomes the most performing one, since it takes longer to have the two subregion networks simultaneously disconnected. -\subsection{The number of stopped simulation runs} +\subsection{The percentage of stopped simulation runs} -We will now study the number of simulations which stopped due to +We will now study the percentage of simulations which stopped due to network disconnections per round for each of the three approaches. -Figure~\ref{fig6} illustrates the average number of stopped simulation +Figure~\ref{fig6} illustrates the percentage of stopped simulation runs per round for 150 deployed nodes. It can be observed that the simple heuristic is the approach which stops first because the nodes are randomly chosen. Among the two proposed strategies, the @@ -818,8 +818,8 @@ optimization participates in extending the network lifetime. \begin{figure}[h!] \centering -\includegraphics[scale=0.55]{TheNumberofStoppedSimulationRuns150.eps} -\caption{The number of stopped simulation runs compared to the number of rounds for 150 deployed nodes } +\includegraphics[scale=0.5]{TheNumberofStoppedSimulationRuns150g.eps} +\caption{The percentage of stopped simulation runs compared to the number of rounds for 150 deployed nodes } \label{fig6} \end{figure} @@ -835,7 +835,7 @@ which is obtained for 10~simulation runs, is then divided by the average number of rounds to define a metric allowing a fair comparison between networks having different densities. -Figure~\ref{fig7} illustrates the Energy Consumption for the different +Figure~\ref{fig7} illustrates the energy consumption for the different network sizes and the three approaches. The results show that the strategy with two leaders is the most competitive from the energy consumption point of view. A centralized method, like the strategy @@ -850,7 +850,7 @@ communications have a small impact on the network lifetime. \begin{figure}[h!] \centering -\includegraphics[scale=0.55]{TheEnergyConsumption.eps} +\includegraphics[scale=0.5]{TheEnergyConsumptiong.eps} \caption{The energy consumption} \label{fig7} \end{figure} @@ -925,7 +925,7 @@ with two leaders and the simple heuristic is illustrated. %\centering % \begin{multicols}{6} \centering -\includegraphics[scale=0.5]{TheNetworkLifetime.eps} %\\~ ~ ~(a) +\includegraphics[scale=0.5]{TheNetworkLifetimeg.eps} %\\~ ~ ~(a) \caption{The network lifetime } \label{fig8} \end{figure} @@ -944,7 +944,7 @@ 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} +\section{Conclusion and future works} \label{sec:conclusion} In this paper, we have addressed the problem of the coverage and the lifetime @@ -973,14 +973,13 @@ single global optimization problem by partitioning it in many smaller problems, one per subregion, that can be solved more easily. In future work, we plan to study and propose a coverage protocol which -computes all active sensor schedules in a single round, using +computes all active sensor schedules in one time, using optimization methods such as swarms optimization or evolutionary -algorithms. This single round will still consists of 4 phases, but the +algorithms. The round will still consist of 4 phases, but the decision phase will compute the schedules for several sensing phases which, aggregated together, define a kind of meta-sensing phase. -The computation of all cover sets in one round is far more +The computation of all cover sets in one time is far more difficult, but will reduce the communication overhead. - % use section* for acknowledgement %\section*{Acknowledgment}