From: Michel Salomon Date: Fri, 23 Aug 2013 10:34:20 +0000 (+0200) Subject: Minor typo corrections X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/UIC2013.git/commitdiff_plain/97c825d7b15b2a2dc1350641ca38613d63d30efa Minor typo corrections --- diff --git a/bare_conf.tex b/bare_conf.tex index 959f692..7518ab7 100755 --- a/bare_conf.tex +++ b/bare_conf.tex @@ -1,6 +1,3 @@ - - - \documentclass[conference]{IEEEtran} \ifCLASSINFOpdf @@ -87,13 +84,13 @@ 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 @@ -130,7 +127,7 @@ 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 +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 +188,7 @@ 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 @@ -355,7 +352,7 @@ 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 +362,10 @@ 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} @@ -692,7 +689,7 @@ 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 @@ -835,7 +832,7 @@ 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 @@ -944,7 +941,7 @@ 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