From: couturie Date: Mon, 21 Sep 2015 05:55:01 +0000 (+0200) Subject: english correction X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/JournalMultiPeriods.git/commitdiff_plain/153bdb7b8dc4b2d64354aa33053bade4c93e86f4?hp=--cc english correction --- 153bdb7b8dc4b2d64354aa33053bade4c93e86f4 diff --git a/article.tex b/article.tex index 1c13bc7..cbb393c 100644 --- a/article.tex +++ b/article.tex @@ -208,7 +208,7 @@ network. Note that centralized algorithms have the advantage of requiring very low processing power from the sensor nodes, which usually have limited processing capabilities. The main drawback of this kind of approach is its higher cost in communications, since the node that will make the decision needs -information from all the sensor nodes. \textcolor{blue} {Exact or heuristics +information from all the sensor nodes. \textcolor{blue} {Exact or heuristic approaches are designed to provide cover sets. %(Moreover, centralized approaches usually %suffer from the scalability problem, making them less competitive as the network @@ -627,12 +627,11 @@ $X_{25}=( p_x + R_s * (\frac{1}{2}), p_y + R_s * (\frac{-\sqrt{3}}{2})) $. %smaller areas, called subregions, and then our MuDiLCO protocol will be %implemented in each subregion in a distributed way. -\textcolor{blue}{The WSN area of interest is, in a first step, divided into - regular homogeneous subregions using a divide-and-conquer algorithm. In a - second step our protocol will be executed in a distributed way in each +\textcolor{blue}{The WSN area of interest is, at first, divided into + regular homogeneous subregions using a divide-and-conquer algorithm. Then, our protocol will be executed in a distributed way in each subregion simultaneously to schedule nodes' activities for one sensing period. Sensor nodes are assumed to be deployed almost uniformly and with high - density over the region. The regular subdivision is made such that the number + density over the region. The regular subdivision is made so that the number of hops between any pairs of sensors inside a subregion is less than or equal to 3.} @@ -663,10 +662,10 @@ batteries running out of energy), because it works in periods. node will not participate to this phase, and, on the other hand, if the node failure occurs after the decision, the sensing task of the network will be temporarily affected: only during the period of sensing until a new period - starts. \textcolor{blue}{The duration of the rounds are predefined - parameters. Round duration should be long enough to hide the system control + starts. \textcolor{blue}{The duration of the rounds is a predefined + parameter. Round duration should be long enough to hide the system control overhead and short enough to minimize the negative effects in case of node - failure.} + failures.} %%RC so if there are at least one failure per period, the coverage is bad... %%MS if we want to be reliable against many node failures we need to have an @@ -1174,13 +1173,13 @@ $W_{U}$ & $|P|^2$ \\ sizes when $T=7$, using the following respective values (in second): 0.03 for 150~nodes, 0.06 for 200~nodes, and 0.08 for 250~nodes. % Table \ref{tl} shows time limit values. - These time limit threshold have been set empirically. The basic idea consists + These time limit thresholds have been set empirically. The basic idea consists in considering the average execution time to solve the integer programs to - optimality, then by dividing this average time by three to set the threshold - value. After that, this threshold value is increased if necessary such that + optimality, then in dividing this average time by three to set the threshold + value. After that, this threshold value is increased if necessary so that the solver is able to deliver a feasible solution within the time limit. In fact, selecting the optimal values for the time limits will be investigated in - future.} + the future.} %In Table \ref{tl}, "NO" indicates that the problem has been solved to optimality without time limit.} %\begin{table}[ht]