From: ali Date: Fri, 17 Oct 2014 21:57:38 +0000 (+0200) Subject: Update on the Figure of energy consumption by Ali X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Sensornets15.git/commitdiff_plain/908d92d35c6d3bbfa53a7a317dbecd6ebbd01bbe?ds=sidebyside;hp=-c Update on the Figure of energy consumption by Ali Merge branch 'master' of ssh://bilbo.iut-bm.univ-fcomte.fr/Sensornets15 Conflicts: Example.tex --- 908d92d35c6d3bbfa53a7a317dbecd6ebbd01bbe diff --combined Example.tex index 2558b19,d865963..bd29ee6 --- a/Example.tex +++ b/Example.tex @@@ -138,6 -138,8 +138,6 @@@ Vu \cite{chin2007}, Padmatvathy et al. Various approaches, including centralized, or distributed algorithms, have been proposed to extend the network lifetime. -%For instance, in order to hide the occurrence of faults, or the sudden unavailability of -%sensor nodes, some distributed algorithms have been developed in~\cite{Gallais06,Tian02,Ye03,Zhang05,HeinzelmanCB02}. In distributed algorithms~\cite{yangnovel,ChinhVu,qu2013distributed}, information is disseminated throughout the network and sensors decide cooperatively by communicating with their neighbors which of them will remain in @@@ -168,6 -170,8 +168,11 @@@ used~\cite{castano2013column,rossi2012e \cite{pedraza2006} where the objective is to maximize the number of cover sets.} ++<<<<<<< HEAD ++======= + + ++>>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba \section{\uppercase{Description of the DiLCO protocol}} \label{sec:The DiLCO Protocol Description} @@@ -177,6 -181,7 +182,10 @@@ on each subregion in the area of in techniques: network leader election and sensor activity scheduling for coverage preservation and energy conservation, applied periodically to efficiently maximize the lifetime in the network. ++<<<<<<< HEAD ++======= + ++>>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba \subsection{Assumptions and models} @@@ -207,6 -212,7 +216,6 @@@ less accurate according to the number o \subsection{Main idea} \label{main_idea} - \noindent We start by applying a divide-and-conquer algorithm to partition the area of interest into smaller areas called subregions and then our protocol is executed simultaneously in each subregion. @@@ -271,10 -277,12 +280,15 @@@ to each sensor in the same subregion t not. Alternately, if the sensor is not the leader, it will wait for the Active-Sleep packet to know its state for the coming sensing phase. ++<<<<<<< HEAD ++======= + + + ++>>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba \begin{algorithm}[h!] - % \KwIn{all the parameters related to information exchange} - % \KwOut{$winer-node$ (: the id of the winner sensor node, which is the leader of current round)} + \BlankLine %\emph{Initialize the sensor node and determine it's position and subregion} \; @@@ -298,7 -306,7 +312,7 @@@ \Else{ \emph{$s_j.status$ = LISTENING}\; \emph{Wait $ActiveSleep()$ packet from the Leader}\; - % \emph{After receiving Packet, Retrieve the schedule and the $T$ rounds}\; + \emph{Update $RE_j $}\; } % } @@@ -518,7 -526,7 +532,7 @@@ value corresponds to the energy nee multiplying the energy consumed in active state (9.72 mW) by the time in seconds for one period (3,600 seconds), and adding the energy for the pre-sensing phases. According to the interval of initial energy, a sensor may be active during at - most 20 rounds. + most 20 periods. In the simulations, we introduce the following performance metrics to evaluate the efficiency of our approach: @@@ -548,6 -556,12 +562,15 @@@ where $n$ is the number of covered 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. ++<<<<<<< HEAD ++======= + %The accuracy of this method depends on the distance between grids. In our + %simulations, the sensing field has been divided into 50 by 25 grid points, which means + %there are $51 \times 26~ = ~ 1326$ points in total. + % Therefore, for our simulations, the error in the coverage calculation is less than ~ 1 $\% $. + + ++>>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba \item {{\bf Energy Consumption}:} energy consumption (EC) can be seen as the total amount of energy consumed by the sensors during $Lifetime_{95}$ or @@@ -570,6 -584,9 +593,12 @@@ refers to the energy needed by all the during a period. Finally, $E^a_{m}$ and $E^s_{m}$ indicate the energy consumed by the whole network in the sensing phase (active and sleeping nodes). ++<<<<<<< HEAD ++======= + + + ++>>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba \end{itemize} %\end{enumerate} @@@ -621,6 -638,7 +650,10 @@@ nodes, and thus enables the extension o \label{fig3} \end{figure} ++<<<<<<< HEAD ++======= + ++>>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba \subsubsection{Energy consumption} Based on the results shown in Figure~\ref{fig3}, we focus on the DiLCO-16 and @@@ -637,7 -655,7 +670,7 @@@ used for the different performance metr \begin{figure}[h!] \centering \includegraphics[scale=0.45]{R/EC.pdf} -\caption{Energy consumption} +\caption{Energy consumption per period} \label{fig95} \end{figure} @@@ -647,6 -665,10 +680,6 @@@ DiLCO-32/95 consume less energy tha similar level of area coverage. This observation reflects the larger number of nodes set active by DESK and GAF. - -%In fact, a distributed method on the subregions greatly reduces the number of communications and the time of listening so thanks to the partitioning of the initial network into several independent subnetworks. -%As shown in Figures~\ref{fig95} and ~\ref{fig50} , DiLCO-2 consumes more energy than the other versions of DiLCO, especially for large sizes of network. This is easy to understand since the bigger the number of sensors involved in the integer program, the larger the time computation to solve the optimization problem as well as the higher energy consumed during the communication. - \subsubsection{Execution time} Another interesting point to investigate is the evolution of the execution time @@@ -680,6 -702,7 +713,10 @@@ prevents it to ensure a good covera subregions. Thus, the optimal number of subregions can be seen as a trade-off between execution time and coverage performance. ++<<<<<<< HEAD ++======= + ++>>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba \subsubsection{Network lifetime} In the next figure, the network lifetime is illustrated. Obviously, the lifetime @@@ -703,6 -726,8 +740,11 @@@ DESK and GAF for the lifetime of the n the larger level of coverage ($95\%$) in the case of our protocol, the subdivision in $16$~subregions seems to be the most appropriate. ++<<<<<<< HEAD ++======= + + ++>>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba \section{\uppercase{Conclusion and future work}} \label{sec:Conclusion and Future Works} @@@ -729,6 -754,8 +771,11 @@@ there is an optimal number of subregio context a subdivision in $16$~subregions seems to be the most relevant. The optimal number of subregions will be investigated in the future. ++<<<<<<< HEAD ++======= + + ++>>>>>>> ec736a6c4605ef475156098f1b75d72120a294ba \section*{\uppercase{Acknowledgements}} \noindent As a Ph.D. student, Ali Kadhum IDREES would like to gratefully