X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/blobdiff_plain/1e354db9744c3493287fe10e62f12f5851bcf3ed..fe3d4f5c7954d683dbfbc1f26de2d724ff9889d7:/bare_conf.tex?ds=sidebyside diff --git a/bare_conf.tex b/bare_conf.tex index 373f8b5..fd7001e 100755 --- a/bare_conf.tex +++ b/bare_conf.tex @@ -40,9 +40,9 @@ % author names and affiliations % use a multiple column layout for up to three different % affiliations -\author{\IEEEauthorblockN{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Raphael Couturier } -\IEEEauthorblockA{FEMTO-ST Institute, UMR CNRS, University of Franche-Comte, Belfort, France \\ -Email:$\lbrace$ali.idness, karine.deschinkel, michel.salomon,raphael.couturier$\rbrace$@femto-st.fr} +\author{\IEEEauthorblockN{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier } +\IEEEauthorblockA{FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comte, Belfort, France \\ +Email: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr} %\email{\{ali.idness, karine.deschinkel, michel.salomon, raphael.couturier\}@univ-fcomte.fr} %\and %\IEEEauthorblockN{Homer Simpson} @@ -113,7 +113,7 @@ planned for each subregion. Our scheduling scheme considers rounds, where a round starts with a discovery phase to exchange information between sensors of the subregion, in order to choose in suitable manner a sensor node to carry out a coverage strategy. This coverage -strategy involves the resolution of an integer program which provides +strategy involves the solving of an integer program which provides the activation of the sensors for the sensing phase of the current round. @@ -128,7 +128,7 @@ 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}. -\section{\uppercase{Related work}} +\section{\uppercase{Related works}} \label{rw} \noindent This section is dedicated to the various approaches proposed in the @@ -183,7 +183,7 @@ is alive until all nodes have been drained of their energy or the sensor network becomes disconnected, and we measure the coverage ratio during the WSN lifetime. Network connectivity is important because an active sensor node without connectivity towards a base station cannot -transmit information on an event in the area that it monitor. +transmit information on an event in the area that it monitors. {\bf Activity scheduling} @@ -196,13 +196,13 @@ distributed, and localized algorithms, have been proposed for activity scheduling. In the distributed algorithms, each node in the network autonomously makes decisions on whether to turn on or turn off itself only using local neighbor information. In centralized algorithms, a -central controller (a node or base station) informs every sensor of +central controller (a node or base station) informs every sensors of the time intervals to be activated. {\bf Distributed approaches} Some distributed algorithms have been developed -in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02}. Distributed +in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02} to perform the schelduling. Distributed algorithms typically operate in rounds for predetermined duration. At the beginning of each round, a sensor exchange information with its neighbors and makes a decision to either remain turned on or to go to @@ -222,7 +222,7 @@ of the area is no longer covered. \cite{Prasad:2007:DAL:1782174.1782218} defines a model for capturing the dependencies between different cover sets and proposes localized heuristic based on this dependency. The algorithm consists of two -phases, an initial setup phase during which each sensor calculates and +phases, an initial setup phase during which each sensor computes and prioritize the covers and a sensing phase during which each sensor first decides its on/off status, and then remains on or off for the rest of the duration. Authors in \cite{chin2007} propose a novel @@ -262,10 +262,10 @@ these set covers successively. First algorithms proposed in the literature consider that the cover sets are disjoint: a sensor node appears in exactly one of the -generated cover sets. For instance Slijepcevic and Potkonjak +generated cover sets. For instance, Slijepcevic and Potkonjak \cite{Slijepcevic01powerefficient} propose an algorithm which allocates sensor nodes in mutually independent sets to monitor an area -divided into several fields. Their algorithm constructs a cover set by +divided into several fields. Their algorithm builds a cover set by including in priority the sensor nodes which cover critical fields, that is to say fields that are covered by the smallest number of sensors. The time complexity of their heuristic is $O(n^2)$ where $n$ @@ -293,7 +293,7 @@ design a heuristic to compute the final number of covers. The results show a slight performance improvement in terms of the number of produced DSC in comparison to~\cite{Slijepcevic01powerefficient}, but it incurs higher execution time due to the complexity of the mixed -integer programming resolution. %Cardei and Du +integer programming solving. %Cardei and Du \cite{Cardei:2005:IWS:1160086.1160098} propose a method to efficiently compute the maximum number of disjoint set covers such that each set can monitor all targets. They first transform the problem into a @@ -340,8 +340,8 @@ scheduling strategy. We give a brief answer to these three questions to describe our approach before going into details in the subsequent sections. \begin{itemize} -\item {\bf How must be planned the phases for information exchange, - decision and sensing over time?} Our algorithm divides the time +\item {\bf How must the phases for information exchange, + decision and sensing be planned over time?} Our algorithm divides the time line into a number of rounds. Each round contains 4 phases: Information Exchange, Leader Election, Decision, and Sensing. @@ -430,7 +430,7 @@ active mode. This step includes choosing the Wireless Sensor Node Leader (WSNL) which will be responsible of executing coverage algorithm. Each subregion in the area of interest will select its own WSNL -independently for each round. All the sensor nodes cooperates to +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 @@ -450,7 +450,7 @@ Active sensors in the round will execute their sensing task to preserve maximal coverage in the region of interest. We will assume that the cost of keeping a node awake (or sleep) for sensing task is the same for all wireless sensor nodes in the network. Each sensor -will receive an Active-Sleep packet from WSNL telling him to stay +will receive an Active-Sleep packet from WSNL informing it to stay awake or go sleep for a time equal to the period of sensing until starting a new round. @@ -615,7 +615,7 @@ X_{j} \in \{0,1\}, &\forall j \in J \right. \end{equation} \begin{itemize} -\item $X_{j}$ : indicates whether or not sensor $j$ is actively +\item $X_{j}$ : indicates whether or not the sensor $j$ is actively 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 point $p$; @@ -661,7 +661,7 @@ the maximum number of points are covered during each round. \section{\uppercase{Simulation Results}} \label{exp} -In this section, we conducted a series of simulations, to evaluate the +In this section, we conducted a series of simulations to evaluate the efficiency and relevance of our approach, using the discrete event simulator OMNeT++ \cite{varga}. We performed simulations for five different densities varying from 50 to 250~nodes. Experimental results @@ -683,7 +683,7 @@ range 24-60~joules, and each sensor node will consume 0.2 watts during the sensing period which will have a duration of 60 seconds. Thus, an active node will consume 12~joules during sensing phase, while a sleeping node will use 0.002 joules. Each sensor node will not -participate in the next round if it's remaining energy is less than 12 +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 $w_{U}=|P^2|$. @@ -711,7 +711,7 @@ number of rounds on the average coverage ratio for 150 deployed nodes for the three approaches. It can be seen that the three approaches give similar coverage ratios during the first rounds. From the 9th~round the coverage ratio decreases continuously with the simple -heuristic, while the other two strategies provide superior coverage to +heuristic, while the two other strategies provide superior coverage to $90\%$ for five more rounds. Coverage ratio decreases when the number of rounds increases due to dead nodes. Although some nodes are dead, thanks to strategy~1 or~2, other nodes are preserved to ensure the @@ -791,14 +791,14 @@ expected, the Strategy with One Leader is usually slightly better than the second strategy, because the global optimization permit to turn off more sensors. Indeed, when there are two subregions more nodes remain awake near the border shared by them. Note that again as the -number of rounds increase the two leader strategy becomes the most +number of rounds increases the two leader strategy becomes the most performing, since its takes longer to have the two subregion networks simultaneously disconnected. \subsection{The Network Lifetime} We have defined the network lifetime as the time until all nodes have -been drained of their energy or each sensor network monitoring a area +been drained of their energy or each sensor network monitoring an area becomes disconnected. In figure~\ref{fig6}, the network lifetime for different network sizes and for the three approaches is illustrated. @@ -815,10 +815,10 @@ As highlighted by figure~\ref{fig6}, the network lifetime obviously increases when the size of the network increase, with our approaches that lead to the larger lifetime improvement. By choosing for each round the well suited nodes to cover the region of interest and by -leaving sleep the other ones to be used later in next rounds, both +letting the other ones sleep in order to be used later in next rounds, both proposed strategies efficiently prolong the lifetime. Comparison shows -that the larger the sensor number, the more our strategies outperform -the heuristic. Strategy~2, which uses two leaders, is the best one +that the larger the sensor number is, the more our strategies outperform +the simple heuristic. Strategy~2, which uses two leaders, is the best one because it is robust to network disconnection in one subregion. It also means that distributing the algorithm in each node and subdividing the sensing field into many subregions, which are managed