X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/LiCO.git/blobdiff_plain/8d0a87bd6a095ec213dc2af5220c6e4904f7e4c0..45fa22e580e6f445e350a75d0f862584459d158c:/PeCO-EO/reponse.tex?ds=sidebyside

diff --git a/PeCO-EO/reponse.tex b/PeCO-EO/reponse.tex
index b00dd1d..45c1d69 100644
--- a/PeCO-EO/reponse.tex
+++ b/PeCO-EO/reponse.tex
@@ -19,137 +19,234 @@
 \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-MontbÃ©liard, BP 527, 90016 Belfort Cedex, France.
+\hspace{-2cm}IUT Belfort-Montb\'eliard, BP 527, 90016 Belfort Cedex, France.
 
 \bigskip
 
 \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 : $M_{i}^{j}=0$, $V_{i}^{j}=l^{i}-l$.                       }}\\
-
-
-
-\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 priviligie 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:}                               }}\\
-
-
-
-\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 {\ding{90} You seem to be writing in the first person. I suggest rewriting sentences that include âweâ âourâ or âIâ in the third person. (There are too many instances to list them all. They are easily found using the find tool.)  }  \\
+\noindent\textcolor{black}{\textbf{\Large English and Grammar:}}\\
 
-\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} 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 {\ding{90}  Run-on sentence: Page 2 lines 43-48}  \\
+\noindent  {\ding{90} You  seem to  be writing  in the  first person.  I suggest
+  rewriting sentences that include âweâ âourâ or âIâ 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:} We rewrote this sentence in two separated sentences.     }}\\
+\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} Add an âandâ 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}  âmodel asâ instead of âThanâ on page 10 line 12.}  \\
+\noindent {\ding{90} âmodel asâ instead of âThanâ on page 10 line 12.}  \\
 
 \textcolor{blue}{\textbf{\textsc{Answer:} Right, fixed }}\\
 
-
 \noindent {\ding{90} âno longerâ instead of âno moreâ on page 10 line 31.}  \\
 
 \textcolor{blue}{\textbf{\textsc{Answer:} Right, fixed }}\\
 
-
 \noindent {\ding{90} âin the active stateâ 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:} ?????? relecture par Ingrid     }}.\\
-
-
-
+% 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.\\
@@ -159,13 +256,13 @@ The paper entitled "Perimeter-based Coverage Optimization to Improve Lifetime in
 
 \noindent {\bf 1.} The protocol framework is not described in details. In particular, the spatial and temporal subdivision (page 2, line 11) that is at the core of PeCO, is not described nor justified in much detail. How to implement an efficient spatial subdivision? On page 10, line 11, the number of subdivisions is said to be equal to 16, but the clustering algorithm used is not mentioned. Is this number dependent of the size of the sensing area? Of the number of sensors? Of the sensing range? The proposed protocol cannot be adopted by practitioners if such an important step is not documented. Temporal subdivision suffers from the same lack of description and justification: why should time intervals have the same duration? If they have the same duration, how should this common duration should be chosen?    \\
 
-\textcolor{blue}{\textbf{\textsc{Answer:} Spatial and temporal choices of subdivision are not the topics of the paper. In the study, we assume that the deployment of sensors is almost uniformly over the region. So we only need to fix a regular division of the region into subregions to make the problem tractable. The subdivision is made such that the number of hops between any pairs of sensors inside a subregion is less than or equal to 3. Concerning the choice of the sensing period duration, it is correlated with the types of applications, with the amount of initial energy in sensors batteries and also with the duration of the exchange phase. All applications do not have the same requirements of Quality of Service. Here 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 communication costs. On the other hand the choice of a long duration may cause problems in case of nodes failure during the sensing period.}}\\
+\textcolor{blue}{\textbf{\textsc{Answer:} Spatial and temporal choices of subdivision are not the topics of the paper. In the study, we assume that the deployment of sensors is almost uniformly over the region. So we only need to fix a regular division of the region into subregions to make the problem tractable. The subdivision is made such that the number of hops between any pairs of sensors inside a subregion is less than or equal to 3. Concerning the choice of the sensing period duration, it is correlated with the types of applications, with the amount of initial energy in sensors batteries and also with the duration of the exchange phase. All applications do not have the same requirements of Quality of Service. Here 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 communication costs. On the other hand the choice of a long duration may cause problems in case of nodes failure during the sensing period. Explanation has been given throughout the paper. In particular the sensing duration is discussed in section 5.1.}}\\
 
 
 \noindent {\bf 2.}Page 9, Section 4, is the Perimeter-based coverage problem NP-hard? This question is important for justifying the use of a Mixed Integer Linear Programming model.   \\
 
 \textcolor{blue}{\textbf{\textsc{Answer:} The perimeter scheduling coverage problem is NP-hard in general, it  has been proved 
- in the paper entitled "Perimeter Coverage Scheduling in Wireless Sensor Networks Using Sensors with a Single Continuous Cover Range" from  Ka-Shun Hung and King-Shan Lui (EURASIP Journal on Wireless Communications and Networking 2010, 2010:926075  doi:10.1155/2010/926075). In this paper, authors study the coverage of the perimeter of a large object requiring to be monitored. In our study, the large object to be monitored is the sensor itself (or more precisely its sensing area).  	                                    }}\\
+ in the paper entitled "Perimeter Coverage Scheduling in Wireless Sensor Networks Using Sensors with a Single Continuous Cover Range" from  Ka-Shun Hung and King-Shan Lui (EURASIP Journal on Wireless Communications and Networking 2010, 2010:926075  doi:10.1155/2010/926075). In this paper, authors study the coverage of the perimeter of a large object requiring to be monitored. In our study, the large object to be monitored is the sensor itself (or more precisely its sensing area). This point has been highlighted at the beginning of section 4.  	                                    }}\\
 
 
 \noindent {\bf 3.} Page 9, the major problem with the present paper is, in my opinion, the objective function of the Mixed Integer Linear Program (2). It is not described in the paper, and looks like an attempt to address a multiobjective problem (like minimizing overcoverage and undercoverage). However, using a weighted sum is well known not to be an efficient way to address biobjective problems. The introduction of various performance metrics in Section 5.1 also suggests that the authors have not decided exactly which objective function to use, and compare their protocols against competitors without mentioning the exact purpose of each of them. If the performance metrics list given in Section 5.1 is exhaustive, then the authors should mention at the beginning of the paper what are the aims of the protocol, and explain how the protocol is built to optimize these objectives.  \\
@@ -175,7 +272,7 @@ The paper entitled "Perimeter-based Coverage Optimization to Improve Lifetime in
 
 \noindent {\bf 4.}Page 11 Section 5.2, the sensor nodes are said to be based on Atmels AVR ATmega103L microcontroller. If I am not mistaken, these devices have 128 KBytes of memory, and I didn't find any clue that they can run an operating system like Linux. This point is of primary importance for the proposed protocol, since GLPK (a C API) is supposed to be executed by the cluster leader. In addition to that, GLPK requires a non negligible amount of memory to run properly, and the Atmels AVR ATmega103L microcontroller might be insufficient for that purpose. The authors are urged to provide references of previous works showing that these technical constraints are not preventing their protocol to be implemented on the aforementioned microcontroller. Then, on page 13, in Section "5.2.3 Energy Consumption", the estimation of $E_p^{com}$ for the considered microcontroller seems quite challenging and should be carefully documented. Indeed, this is a key point in providing a fair comparison of PeCO with its competitors.    \\
 
-\textcolor{blue}{\textbf{\textsc{Answer:}
+\textcolor{blue}{\textbf{\textsc{Answer 1:}
 To implement PeCO on real sensors nodes with limited memories capacities, we can act on :
 \begin{itemize}
 \item the solver : GLPK is memory consuming for the resolution of integer programming (IP) compared with other commercial solvers like CPLEX\textregistered. Commercial solvers generally outperform open source solvers  (See the report : "Analysis of commercial and free and open source
@@ -194,18 +291,22 @@ where $S$ denotes the number of sensors in the subregion, $I$ the average number
 Total number & S & I & GLPK IP & GLPK LP & nodes&CPLEX\\
 of nodes &&&&relaxation &B\&B tree &\\
 \hline
-100 & 6.25& 5&0.2 Mb & 0.2 Mb &1 &  64 Kb\\
+100 & 6.25& 5&0.2 MB & 0.2 Mb &1 &  64 kB\\
 \hline
-200 & 12.5& 11&1.7 Mb & 1.6 Mb &1 & 281 Kb\\
+200 & 12.5& 11&1.7 MB & 1.6 Mb &1 & 281 kB\\
 \hline
-300 &18.5 & 17&3.6 Mb & 3.5 Mb & 3 &644 Kb\\
+300 &18.5 & 17&3.6 MB & 3.5 Mb & 3 &644 kB\\
 \hline
 \end{tabular}
 \\
-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 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 some specifities that encourage us to develop our own solver (coefficents matrix is very sparse) or to use an existing heuristic to find good approximate solutions (Reference : "A feasibility pump heuristic for general mixed-integer problems", Livio Bertacco and Matteo Fischetti and Andrea Lodi, Discrete Optimization, issn 1572-5286).
+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 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 some specifities that encourage us to develop our own solver (coefficents matrix is very sparse) or to use an existing heuristic to find good approximate solutions (Reference : "A feasibility pump heuristic for general mixed-integer problems", Livio Bertacco and Matteo Fischetti and Andrea Lodi, Discrete 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 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).  
-\end{itemize}}}\\
-
+\end{itemize}
+A discussion about memory consumption has been added in section 5.2}}
+\bigskip
+\textcolor{blue}{\textbf{\textsc{Answer 2:}
+In section 5.2 we give a table with the power consumption values which are used to compute the energy consumption. These ones are based on the energy model of (Vu et al. 2006).
+}}
 
 
 
