X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/blobdiff_plain/1e354db9744c3493287fe10e62f12f5851bcf3ed..763e6024a4df2631498b5b9e77063abdf69dc9bc:/bare_conf.tex diff --git a/bare_conf.tex b/bare_conf.tex index 373f8b5..c1003b8 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.