\today
\end{flushright}%
-\vspace{-0.5cm}\hspace{-2cm}FEMTO-ST Institute, UMR 6714
+\vspace{-0.5cm}\hspace{-2cm}FEMTO-ST Institute, UMR 6714 CNRS
-\hspace{-2cm}University of Franche-Comt\'e
+\hspace{-2cm}University Bourgogne Franche-Comt\'e
\hspace{-2cm}IUT Belfort-Montb\'eliard, BP 527, 90016 Belfort Cedex, France.
\begin{center}
Detailed changes and addressed issues in the revision of the article
-"Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks"\\
+``Perimeter-based Coverage Optimization \\
+to Improve Lifetime in Wireless Sensor Networks''\\
-by Ali Kadhum Idrees, Karine Deschinkel Michel Salomon, Rapahel Couturier
+by Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Raph\"ael Couturier
+
+\medskip
-\bigskip
\end{center}
Dear Editor and Reviewers,
-First of all, we would like to thank you very much for your kind help to improve our article named:
-"Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks". We highly appreciate the detailed valuable comments of the reviewers on our article. The suggestions are quite helpful for us and we incorporate them in the revised article. We are happy to submit to you a revised version that considers most of your remarks and suggestions of improvement to improve the quality of our article.
-
-As below, we would like to clarify some of the points raised by the reviewers and we hope the reviewers and the editors will be satisfied with our responses to the comments and the revision for the original manuscript. \\
+First of all, we would like to thank you very much for your kind help to improve
+our article named: ``Perimeter-based Coverage Optimization to Improve Lifetime
+in Wireless Sensor Networks''. We highly appreciate the detailed valuable
+comments of the reviewers on our article. The suggestions are quite helpful for
+us and we incorporate them in the revised article. We are happy to submit to you
+a revised version that considers most of your remarks and suggestions to improve
+the quality of our article.
+As below, we would like to clarify some of the points raised by the reviewers
+and we hope the reviewers and the editors will be satisfied by our responses to
+the comments and the revision for the original manuscript.
%Journal: Engineering Optimization
%Reviewer's Comment to the Author Manuscript id GENO-2015-0094
%Title: \bf "Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks"
%Authors: Ali Kadhum Idrees, Karine Deschinkela, Michel Salomon and Raphael Couturier
-
\section*{Response to Reviewer No. 1 Comments}
-This paper proposes a scheduling technique for WSN to maximize coverage and network lifetime. The novelty of this paper is the integration of an existing perimeter coverage measure with an existing integer linear programming model. Here are few comments:\\
-
-\noindent {\bf 1.} The paper makes use of the existing integer optimization model to govern the state of each sensor node within the WSN to maximize coverage and network lifetime. This formulation of the coverage problem is different from the literature in the sense that they use the perimeter coverage measures to optimize coverage as opposed to the targets/points coverage. The 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 proposed in the literature. As specified in the paper, in section 4, it is inspired from one model developed for brachytherapy treatment planning for optimizing dose distribution. In this model the deviation between an actual dose distribution and a required dose distribution in each organ is minimized. In WSN the deviations between the actual level of coverage and the required level are minimized. Outside this parallel between these two applications the mathematical formulation is completly different. }}\\
+This paper proposes a scheduling technique for WSN to maximize coverage and
+network lifetime. The novelty of this paper is the integration of an existing
+perimeter coverage measure with an existing integer linear programming
+model. Here are few comments:\\
+
+\noindent {\bf 1.} The paper makes use of the existing integer optimization
+model to govern the state of each sensor node within the WSN to maximize
+coverage and network lifetime. This formulation of the coverage problem is
+different from the literature in the sense that they use the perimeter coverage
+measures to optimize coverage as opposed to the targets/points coverage. The
+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
+ 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
+ dose distribution and a required dose distribution in each organ is
+ minimized. In WSN the deviations between the actual level of coverage and
+ the required level are minimized. Outside this parallel between these two
+ applications the mathematical formulation is completely different.}}\\
+
+\noindent {\bf 2.} The theory seems mathematically sound. However, the
+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 subsection~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
+ 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
+ there exists sensors with the same number of neighbors and the same value
+ for the remaining energy, the sensor node that has the largest index will be
+ finally selected as a leader. }}\\
-
-\noindent {\bf 2.} The theory seems mathematically sound. However, the 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 8, lines 50-51. After information exchange among the sensor nodes in the subregion, each node will have all required information to decide if it is a leader or not. The decision is based on selecting the sensor node that has a 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 there exists sensors with the same number of neighbors and the same value for the remaining energy, the sensor node that has the largest index will be selected as a leader. }}\\
%{\bf In fact, we gave a high priority to the number of neighbors to reduce the communication energy consumption - PAS CLAIR }}.\\
-
-\noindent {\bf 3.} The communication and information sharing required to cooperate and make these
-decisions was not discussed. \\
-
-\textcolor{blue}{\textbf{\textsc{Answer:} The communication and information sharing required to cooperate and make these decisions was discussed in page 8, lines 48-49. Position coordinates, remaining energy, sensor node ID and number of one-hop neighbors are exchanged.}}\\
-
-
-
-\noindent {\bf 4.} The definitions of the undercoverage and overcoverage variables are not clear. I suggest
-adding some information about these values, since without it, you cannot understand how M and V are computed for the optimization problem. \\
-
-\textcolor{blue}{\textbf{\textsc{Answer:} The perimeter of each sensor may be cut in parts called coverage intervals (CI). The level of coverage of one CI is defined as the number of active sensors neighbours covering this part of the perimeter. If a given level of coverage $l$ is required for one sensor, the sensor is said to be undercovered (respectively overcovered) if the level of coverage of one of its CI is less (respectively greater) than $l$. In other terms, we define undercoverage and overcoverage through the use of variables $M_{i}^{j}$ and $V_{i}^{j}$ for one sensor $j$ and its coverage interval $i$. If the sensor $j$ is undercovered, there exists at least one of its CI (say $i$) for which the number of active sensors (denoted by $l^{i}$) covering this part of the perimeter is less than $l$ and in this case : $M_{i}^{j}=l-l^{i}$, $V_{i}^{j}=0$. In the contrary, if the sensor $j$ is overcovered, there exists at least one of its CI (say $i$) for which the number of active sensors (denoted by $l^{i}$) covering this part of the perimeter is greater than $l$ and in this case : $M_{i}^{j}=0$, $V_{i}^{j}=l^{i}-l$. This explanation has been added at the end of section~4. }}\\
-
-
-
-\noindent {\bf 5.} Can you mathematically justify how you chose the values of alpha and beta? This is not
-very clear. I would suggest possibly adding more results showing how the algorithm performs with different alphas and betas. \\
-
-\textcolor{blue}{\textbf{\textsc{Answer:} 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 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 $\alpha_{i}^{j}$ values can be differenciated between sensors to force some regions to be better covered than others. The choice of $\beta \gg \alpha$ prevents the overcoverage, and so limit the activation of a large number of sensors, but as $\alpha$ is low, some areas may be poorly covered. This explains the results obtained for {\it Lifetime50} 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 reached, but a large number of sensors are activated to achieve this goal. Therefore network lifetime is reduced. The choice $\alpha=0.6$ and $\beta=0.4$ seems to achieve the best compromise between lifetime and coverage ratio. This discussion about the impact of the values of alpha and beta on the protocol performance is added as subsection 5.2.5. }}\\
-
-
-
-\noindent {\bf 6.} The authors have performed a thorough review of existing coverage methodologies.
-However, the clarity in the literature review is a little off. Some of the descriptions of the method
-s used are very vague and do not bring out their key contributions. Some references are not consistent and I suggest using the journals template to adjust them for overall consistency. \\
-
-\textcolor{blue}{\textbf{\textsc{Answer:} References have been carefully checked and seem to be consistent with the journal template. In section "related works" we refer to papers dealing with coverage and lifetime in WSN. Each paragraph of this section discusses the literature related to a particular aspect of the problem : 1.Type of coverage, 2.Type of scheme, 3.Centralized versus Distributed, 4. Optimization method. At the end of each paragraph we position our method.}}\\
-
-
-
-\noindent {\bf 7.} The methodology is implemented in OMNeT++ (network simulator) and tested against 2 existing algorithms and a previously developed method by the authors. The simulation results are thorough and show that the proposed method improves the coverage and network lifetime compared with the 3 existing methods. The results are similar to previous work done by their team. \\
-
-\textcolor{blue}{\textbf{\textsc{Answer:} Although the study conducted in this paper reuses the same protocol presented in our previous work, we focus in this paper on the mathematical optimization model developed to schedule nodes activities. We deliberately chose to keep the same performance indicators to compare the results obtained with this new formulation with other existing algorithms. }}\\
-
-
-\noindent {\bf 8.} Since this paper is attacking the coverage problem, I would like to see more information on the amount of coverage the algorithm is achieving. It seems that there is a tradeoff in this algorithm that allows the network to increase its lifetime but does not improve the coverage ratio. This 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 interesting. Indeed, figures 8(a) and (b) highlight this result. PeCO methods allows to achieve a coverage ratio greater than $50\%$ for many more periods than the others three methods, but for applications requiring an high level of coverage (greater than $95\%$), DilCO method is more efficient. It is explained at the end of section 5.2.4. }}\\
+\noindent {\bf 3.} The communication and information sharing required to
+cooperate and make these decisions was not discussed.\\
+
+\textcolor{blue}{\textbf{\textsc{Answer:} The communication and information
+ sharing required to cooperate and make these decisions is discussed at the
+ end of page 8. Position coordinates, remaining energy, sensor node ID and
+ number of one-hop neighbors are exchanged.}}\\
+
+\noindent {\bf 4.} The definitions of the undercoverage and overcoverage
+variables are not clear. I suggest adding some information about these values,
+since without it, you cannot understand how M and V are computed for the
+optimization problem.\\
+
+\textcolor{blue}{\textbf{\textsc{Answer:} The perimeter of each sensor may be
+ cut in parts called coverage intervals (CI). The level of coverage of one CI
+ is defined as the number of active sensors neighbors covering this part of
+ the perimeter. If a given level of coverage $l$ is required for one sensor,
+ the sensor is said to be undercovered (respectively overcovered) if the
+ level of coverage of one of its CI is less (respectively greater) than
+ $l$. In other terms, we define undercoverage and overcoverage through the
+ use of variables $M_{i}^{j}$ and $V_{i}^{j}$ for one sensor $j$ and its
+ coverage interval $i$. If the sensor $j$ is undercovered, there exists at
+ least one of its CI (say $i$) for which the number of active sensors
+ (denoted by $l^{i}$) covering this part of the perimeter is less than $l$
+ and in this case : $M_{i}^{j}=l-l^{i}$, $V_{i}^{j}=0$. In the contrary, if
+ the sensor $j$ is overcovered, there exists at least one of its CI (say $i$)
+ for which the number of active sensors (denoted by $l^{i}$) covering this
+ part of the perimeter is greater than $l$ and in this case : $M_{i}^{j}=0$,
+ $V_{i}^{j}=l^{i}-l$. This explanation has been added in the penultimate
+ paragraph of section~4.}}\\
+
+\noindent {\bf 5.} Can you mathematically justify how you chose the values of
+alpha and beta? This is not very clear. I would suggest possibly adding more
+results showing how the algorithm performs with different alphas and betas.\\
+
+\textcolor{blue}{\textbf{\textsc{Answer:} To discuss this point, we added
+ subsection 5.2.5 in which we study the protocol performance, considering
+ $Lifetime_{50}$ and $Lifetime_{95}$ metrics, for different couples of values
+ 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
+ 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 $\alpha_{i}^{j}$
+ values can be differentiated between sensors to force some regions to be
+ better covered than others. The choice of $\beta \gg \alpha$ prevents the
+ overcoverage, and so limit the activation of a large number of sensors, but
+ 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
+ reached, but a large number of sensors are activated to achieve this goal.
+ Therefore network lifetime is reduced. The choice $\alpha=0.6$ and
+ $\beta=0.4$ seems to achieve the best compromise between lifetime and
+ coverage ratio.}}\\
+
+\noindent {\bf 6.} The authors have performed a thorough review of existing
+coverage methodologies. However, the clarity in the literature review is a
+little off. Some of the descriptions of the method s used are very vague and do
+not bring out their key contributions. Some references are not consistent and I
+suggest using the journals template to adjust them for overall consistency.\\
+
+\textcolor{blue}{\textbf{\textsc{Answer:} References have been carefully checked
+ and seem to be consistent with the journal template. In section~2, ``Related
+ literature'', we refer to papers dealing with coverage and lifetime in
+ WSN. Each paragraph of this section discusses the literature related to a
+ particular aspect of the problem : 1. types of coverage, 2. types of scheme,
+ 3. centralized versus distributed protocols, 4. optimization method. At the
+ end of each paragraph we position our approach.}}\\
+
+\noindent {\bf 7.} The methodology is implemented in OMNeT++ (network simulator)
+and tested against 2 existing algorithms and a previously developed method by
+the authors. The simulation results are thorough and show that the proposed
+method improves the coverage and network lifetime compared with the 3 existing
+methods. The results are similar to previous work done by their team.\\
+
+\textcolor{blue}{\textbf{\textsc{Answer:} Although the study conducted in this
+ paper reuses the same protocol presented in our previous work, we focus in
+ this paper on the mathematical optimization model developed to schedule
+ nodes activities. We deliberately chose to keep the same performance
+ indicators to compare the results obtained with this new formulation with
+ other existing algorithms.}}\\
+
+\noindent {\bf 8.} Since this paper is attacking the coverage problem, I would
+like to see more information on the amount of coverage the algorithm is
+achieving. It seems that there is a tradeoff in this algorithm that allows the
+network to increase its lifetime but does not improve the coverage ratio. This
+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 interesting. Indeed,
+ Figures 8(a) and (b) highlight this result. 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
+ the end of section 5.2.4.}}\\
%%%%%%%%%%%%%%%%%%%%%% ENGLISH and GRAMMAR %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\noindent\textcolor{black}{\textbf{\Large English and Grammar:}} \\
-
-\noindent {\ding{90} The first paragraph of every section is not indented. } \\
-
-\textcolor{blue}{\textbf{\textsc{Answer:} Right, fixed. The first paragraph of every section is indented in the new version. }}\\
+\noindent\textcolor{black}{\textbf{\Large English and Grammar:}}\\
+\noindent {\ding{90} The first paragraph of every section is not indented.}\\
-\noindent {\ding{90} You seem to be writing in the first person. I suggest rewriting sentences that include “we” “our” or “I” in the third person. (There are too many instances to list them all. They are easily found using the find tool.) } \\
+\textcolor{blue}{\textbf{\textsc{Answer:} Right, fixed. The first paragraph of
+ every section is indented in the new version. }}\\
-\textcolor{blue}{\textbf{\textsc{Answer:} It is very common to find sentences with "we" and "our" in scientific papers to explain the work made by the authors. Nevertheless we agree with the reviewer and we reformulated some sentences in the paper to avoid too many uses of the first person. }}\\
+\noindent {\ding{90} You seem to be writing in the first person. I suggest
+ rewriting sentences that include “we” “our” or “I” in the third person. (There
+ are too many instances to list them all. They are easily found using the find
+ tool.) } \\
+\textcolor{blue}{\textbf{\textsc{Answer:} It is very common to find sentences
+ with "we" and "our" in scientific papers to explain the work made by the
+ authors. Nevertheless we agree with the reviewer and we reformulated some
+ sentences in the paper to avoid too many uses of the first person. }}\\
-\noindent {\ding{90} Run-on sentence: Page 2 lines 43-48} \\
-
-\textcolor{blue}{\textbf{\textsc{Answer:} We rewrote this sentence in two separated sentences. }}\\
-
+\noindent {\ding{90} Run-on sentence: Page 2 lines 43-48} \\
+\textcolor{blue}{\textbf{\textsc{Answer:} We rewrote this sentence in two
+ separated sentences. }}\\
\noindent {\ding{90} Add an “and” after the comma on page 3 line 34.} \\
-\textcolor{blue}{\textbf{\textsc{Answer:} Right, fixed }}\\
-
+\textcolor{blue}{\textbf{\textsc{Answer:} Right, fixed }}\\
-\noindent {\ding{90} “model as” instead of “Than” on page 10 line 12.} \\
+\noindent {\ding{90} “model as” instead of “Than” on page 10 line 12.} \\
\textcolor{blue}{\textbf{\textsc{Answer:} Right, fixed }}\\
-
\noindent {\ding{90} “no longer” instead of “no more” on page 10 line 31.} \\
\textcolor{blue}{\textbf{\textsc{Answer:} Right, fixed }}\\
-
\noindent {\ding{90} “in the active state” add the on page 10 line 34. } \\
\textcolor{blue}{\textbf{\textsc{Answer:} Right, fixed }}\\
+\noindent { \ding{90} Lots of English and grammar mistakes. I recommend
+ rereading the paper line by line and adjusting the sentences that do not make
+ sense.} \\
+\textcolor{blue}{\textbf{\textsc{Answer:} The English of the paper has been
+ carefully revised and the readability improved. The new version has been
+ checked by an English teacher.}}\\
-\noindent { \ding{90} Lots of English and grammar mistakes. I recommend rereading the paper line by line and adjusting the sentences that do not make sense.} \\
-
-\textcolor{blue}{\textbf{\textsc{Answer:} The paper has been carefully reread, and the readability of the paper (English) has been improved. }}\\
-
-
-
+% TO BE CONTINUED
\section*{Response to Reviewer No. 2 Comments}
The paper entitled "Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks", by Ali Kadhum Idrees, Karine Deschinkela, Michel Salomon and Raphael Couturier proposes a new protocol for Wireless Sensor Networks called PeCO (Perimeter-based Coverage Optimization protocol) that aims at optimizing the use of energy by conjointly exploiting a spatial and temporal subdivision. The protocol is based on solving a Mixed Integer Linear Program at each leader node, and at each iteration of the protocol. The results obtained by PeCO are compared with three other competitors.\\
