X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/LiCO.git/blobdiff_plain/2b16e2d89b01a6fdda8ae3ccfe3b8fb7085c4dcb..f728a52ec5b5d3a0b90f03b90777f454b88b21be:/PeCO-EO/reponse.tex?ds=sidebyside diff --git a/PeCO-EO/reponse.tex b/PeCO-EO/reponse.tex index 9d11094..1fb3f07 100644 --- a/PeCO-EO/reponse.tex +++ b/PeCO-EO/reponse.tex @@ -73,7 +73,7 @@ methodology uses existing methods and the original contribution lies only in the application of these methods for the coverage scheduling problem.\\ \textcolor{blue}{\textbf{\textsc{Answer:} To the best of our knowledge, no - integer linear programming based on perimeter coverage has been already + integer linear programming based on perimeter coverage has ever been proposed in the literature. As specified in the paper, in Section 4, it is inspired from a model developed for brachytherapy treatment planning for optimizing dose distribution. In this model the deviation between an actual @@ -86,9 +86,9 @@ application of these methods for the coverage scheduling problem.\\ assumption made on the selection criteria for the leader seems too vague. \\ \textcolor{blue}{\textbf{\textsc{Answer:} The selection criteria for the leader - inside each subregion is explained in page~9, at the end of Section~3.3 - After information exchange among the sensor nodes in the subregion, each - node will have all the information needed to decide if it will the leader or + inside each subregion is explained page~9, at the end of Section~3.3 + After the information exchange among the sensor nodes in the subregion, each + node will have all the information needed to decide if it will be the leader or not. The decision is based on selecting the sensor node that has the larger number of one-hop neighbors. If this value is the same for many sensors, the node that has the largest remaining energy will be selected as a leader. If @@ -139,9 +139,9 @@ results showing how the algorithm performs with different alphas and betas.\\ for alpha and beta. Table 4 presents the results obtained for a WSN of 200~sensor nodes. It explains the value chosen for the simulation settings in Table~2. \\ \indent The choice of alpha and beta should be made according - to the needs of the application. Alpha should be enough large to prevent - undercoverage and so to reach the highest possible coverage ratio. Beta - should be enough large to prevent overcoverage and so to activate a minimum + to the needs of the application. Alpha should be large enough to prevent + undercoverage and thus to reach the highest possible coverage ratio. Beta + should be enough large to prevent overcoverage and thus to activate a minimum number of sensors. The values of $\alpha_{i}^{j}$ can be identical for all coverage intervals $i$ of one sensor $j$ in order to express that the perimeter of each sensor should be uniformly covered, but $\alpha_{i}^{j}$ @@ -151,9 +151,9 @@ results showing how the algorithm performs with different alphas and betas.\\ as $\alpha$ is low, some areas may be poorly covered. This explains the results obtained for $Lifetime_{50}$ with $\beta \gg \alpha$: a large number of periods with low coverage ratio. With $\alpha \gg \beta$, we favor the - coverage even if some areas may be overcovered, so high coverage ratio is + coverage even if some areas may be overcovered, so a high coverage ratio is reached, but a large number of sensors are activated to achieve this goal. - Therefore network lifetime is reduced. The choice $\alpha=0.6$ and + Therefore the network lifetime is reduced. The choice $\alpha=0.6$ and $\beta=0.4$ seems to achieve the best compromise between lifetime and coverage ratio.}}\\ @@ -192,10 +192,10 @@ may be an issue if this approach is used in an application that requires high coverage ratio. \\ \textcolor{blue}{\textbf{\textsc{Answer:} Your remark is very interesting. Indeed, - Figures 8(a) and (b) highlight this result. PeCO protocol allows to achieve + Figures 8(a) and (b) highlight this result. The PeCO protocol allows to achieve a coverage ratio greater than $50\%$ for far more periods than the others three methods, but for applications requiring a high level of coverage - (greater than $95\%$), DiLCO method is more efficient. It is explained at + (greater than $95\%$), the DiLCO method is more efficient. It is explained at the end of Section 5.2.4.}}\\ %%%%%%%%%%%%%%%%%%%%%% ENGLISH and GRAMMAR %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @@ -283,7 +283,7 @@ how should this common duration should be chosen?\\ do not have the same Quality of Service requirements. In our case, information exchange is executed every hour, but the length of the sensing period could be reduced and adapted dynamically. On the one hand, a small - sensing period would allow to be more reliable but would have higher + sensing period would allow the network to be more reliable but would have higher communication costs. On the other hand, the choice of a long duration may cause problems in case of nodes failure during the sensing period. Several explanations on these points are given throughout the paper. In @@ -320,8 +320,7 @@ and explain how the protocol is built to optimize these objectives. \\ \textcolor{blue}{\textbf{\textsc{Answer:} Right. The mixed Integer Linear Program adresses a multiobjective problem, where the goal is to minimize - overcoverage and undercoverage for each coverage interval of a sensor. As - far as we know, representing the objective function as a weighted sum of + overcoverage and undercoverage for each coverage interval of a sensor. To the best of our knowledge, representing the objective function as a weighted sum of criteria to be minimized in case of multicriteria optimization is a classical method. In Section 5, the comparison of protocols with a large variety of performance metrics allows to select the most appropriate method @@ -387,7 +386,7 @@ of nodes &&&&relaxation &B\&B tree &\\ \medskip \\ It is noteworthy that the difference of memory used with GLPK between the resolution of the IP and its LP-relaxation is very weak (not more than 0.1 -MB). The size of the branch and bound tree dos not exceed 3 nodes. This result +MB). The size of the branch and bound tree does not exceed 3 nodes. This result leads one to believe that the memory use with CPLEX\textregistered for solving the IP would be very close to that for the LP-relaxation, that is to say around 100 Kb for a subregion containing $S=10$ sensors. Moreover the IP seems to have @@ -399,7 +398,7 @@ Optimization, issn 1572-5286). \item the subdivision of the region of interest. To make the resolution of integer programming tractable by a leader sensor, we need to limit the number of nodes in each subregion (the number of variables and constraints of the - integer programming is directly depending on the number of nodes and + integer programming directly depends on the number of nodes and neigbors). It is therefore necessary to adapt the subdvision according to the number of sensors deployed in the area and their sensing range (impact on the number of coverage intervals). @@ -487,8 +486,8 @@ A discussion about memory consumption has been added in Section 5.2}} \textcolor{blue}{\textbf{\textsc{Answer:} For minimizing the objective function, $M_{i}^{j}$ and $V_{i}^{j}$ should be set to the smallest possible value - such that the inequalities are satisfied. It is explained in the answer 4 - for the reviewer 1. But, at optimality, constraints are not necessary + such that the inequalities are satisfied. It is explained in answer 4 + for reviewer 1. But, at optimality, constraints are not necessary satisfied with equality. For instance, if a sensor $j$ is overcovered, there exists at least one of its coverage interval (say $i$) for which the number of active sensors (denoted by $l^{i}$) covering this part of the perimeter