X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/ec6188c6f907fa2ef54fb5b609af61cc87c22708..823922b46fe128564f6ed32de2930828d6b74368:/CHAPITRE_06.tex

diff --git a/CHAPITRE_06.tex b/CHAPITRE_06.tex
index d566e41..dc74d83 100644
--- a/CHAPITRE_06.tex
+++ b/CHAPITRE_06.tex
@@ -17,7 +17,7 @@
 In this chapter,  we propose an approach called Perimeter-based Coverage Optimization
 protocol (PeCO). 
 %The PeCO protocol merges between two energy efficient mechanisms, which are used the main advantages of the centralized and distributed approaches and avoids the most of their disadvantages. An energy-efficient activity scheduling mechanism based new optimization model is performed by each leader in the subregions. 
-The framework is similar to the one described in section \ref{ch4:sec:02:03}. But in this approach, the optimization model is based on the perimeter coverage model in order to produce the optimal cover set of active sensors, which are taken the responsibility of sensing during the current period. 
+The scheme is similar to the one described in section \ref{ch4:sec:02:03}. But in this approach, the optimization model is based on the perimeter coverage model in order to produce the optimal cover set of active sensors, which are taken the responsibility of sensing during the current period. 
 
 
 The rest of the chapter is organized as follows. The next section is devoted to the PeCO protocol description and section~\ref{ch6:sec:03} focuses on the coverage model formulation which is used  to schedule the activation  of sensor nodes.  Section~\ref{ch6:sec:04} presents simulation results and discusses the comparison with other approaches. Finally, concluding remarks   are  drawn in section~\ref{ch6:sec:05}.
@@ -35,7 +35,7 @@ we considered (in particular the perimeter coverage one), second we describe the
 
 \subsection{Assumptions and Models}
 \label{ch6:sec:02:01}
-The PeCO protocol uses the same assumptions and network model than both DiLCO and MuDiLCO protocols. All the hypotheses can be found in section \ref{ch4:sec:02:01}.
+The PeCO protocol uses the same assumptions and network model than both the DiLCO and the MuDiLCO protocols. All the hypotheses can be found in section \ref{ch4:sec:02:01}.
 The PeCO protocol  uses the  same perimeter-coverage  model as  Huang and Tseng in~\cite{ref133}. It  can be expressed as follows:  a sensor is said to be a perimeter covered if all the points on its  perimeter are covered by at least  one sensor  other than  itself.  They  proved that  a network  area is
 $k$-covered if and only if each sensor in the network is $k$-perimeter-covered (perimeter covered by at least $k$ sensors).
   
@@ -361,7 +361,7 @@ With the performance metrics, described in section \ref{ch4:sec:04:04}, we evalu
 In  order to  assess and  analyze  the  performance  of  our protocol  we  have implemented PeCO protocol in  OMNeT++~\cite{ref158} simulator.  
 %Besides PeCO, three other protocols,  described in  the next paragraph,  will  be  evaluated for comparison purposes. 
 %The simulations were run  on a laptop DELL with an Intel Core~i3~2370~M (2.4~GHz) processor (2  cores) whose MIPS  (Million Instructions Per Second) rate  is equal to 35330. To  be consistent with the use  of a sensor node based on  Atmels AVR ATmega103L microcontroller (6~MHz) having  a MIPS rate equal to 6, the original execution time  on the laptop is  multiplied by 2944.2 $\left(\frac{35330}{2} \times  \frac{1}{6} \right)$.  The modeling  language for Mathematical Programming (AMPL)~\cite{AMPL} is  employed to generate the integer program instance  in a  standard format, which  is then read  and solved  by the optimization solver  GLPK (GNU  linear Programming Kit  available in  the public domain) \cite{glpk} through a Branch-and-Bound method.
-PeCO protocol is  compared with three other approaches. DESK \cite{DESK}, GAF~\cite{GAF}, and DiLCO~\cite{Idrees2} is  an improved version of a research work we presented in~\cite{ref159}, where DiLCO protocol is described in chapter 4. Let us notice that PeCO and  DiLCO protocols are  based on the  same framework. In  particular, the choice for the simulations of a partitioning in 16~subregions was chosen because it corresponds to the configuration producing the better results for DiLCO. The protocols are distinguished from one another by the formulation  of the integer program providing the set of sensors which have to be activated in each sensing phase. DiLCO protocol tries to satisfy the coverage of a set of primary points, whereas PeCO protocol objective is to reach a desired level of coverage for each sensor perimeter. In our experimentations, we chose a level of coverage equal to one ($l=1$).
+PeCO protocol is  compared with three other approaches. DESK \cite{DESK}, GAF~\cite{GAF}, and DiLCO~\cite{Idrees2} is  an improved version of a research work we presented in~\cite{ref159}, where DiLCO protocol is described in chapter 4. Let us notice that the PeCO and  the DiLCO protocols are  based on the  same scheme. In  particular, the choice for the simulations of a partitioning in 16~subregions was chosen because it corresponds to the configuration producing the better results for DiLCO. The protocols are distinguished from one another by the formulation  of the integer program providing the set of sensors which have to be activated in each sensing phase. DiLCO protocol tries to satisfy the coverage of a set of primary points, whereas PeCO protocol objective is to reach a desired level of coverage for each sensor perimeter. In our experimentations, we chose a level of coverage equal to one ($l=1$).
 
 
 
@@ -438,7 +438,7 @@ One might think that the  resolution of the integer  program is too costly  in e
 \label{ch6:sec:04:02:04}
 
 We observe the superiority of PeCO and DiLCO protocols in comparison with the two other   approaches in  prolonging the network lifetime. In
-Figures~\ref{fig3LT}(a)  and (b),  $Lifetime95$ and  $Lifetime50$ are  shown for different  network  sizes.   As  highlighted  by  these  figures,  the  lifetime increases with the size  of the network, and it is clearly   largest for DiLCO and PeCO  protocols.  For instance,  for a  network of 300~sensors  and coverage ratio greater than 50\%, we can  see on Figure~\ref{fig3LT}(b) that the lifetime is about twice longer with  PeCO compared to DESK protocol.  The performance difference    is    more    obvious   in    Figure~\ref{fig3LT}(b) than in Figure~\ref{fig3LT}(a) because the gain induced  by our protocols increases with  time, and the lifetime with a coverage  of 50\% is far  longer than with
+Figures~\ref{fig3LT}(a)  and (b),  $Lifetime95$ and  $Lifetime50$ are  shown for different  network  sizes.   As  highlighted  by  these  figures,  the  lifetime increases with the size  of the network, and it is clearly   largest for the DiLCO and the PeCO  protocols.  For instance,  for a  network of 300~sensors  and coverage ratio greater than 50\%, we can  see on Figure~\ref{fig3LT}(b) that the lifetime is about twice longer with  the PeCO compared to the DESK protocol.  The performance difference    is    more    obvious   in    Figure~\ref{fig3LT}(b) than in Figure~\ref{fig3LT}(a) because the gain induced  by our protocols increases with  time, and the lifetime with a coverage  of 50\% is far  longer than with
 95\%. 
 
 \begin{figure} [p]
@@ -455,13 +455,7 @@ Figure~\ref{figLTALL}  compares  the  lifetime  coverage of  our  protocols  for
 different coverage  ratios. We denote by  Protocol/50, Protocol/80, Protocol/85,
 Protocol/90, and  Protocol/95 the amount  of time  during which the  network can
 satisfy an area coverage greater than $50\%$, $80\%$, $85\%$, $90\%$, and $95\%$
-respectively, where the term Protocol refers to  DiLCO  or PeCO.  Indeed there  are applications
-that do not require a 100\% coverage of  the area to be monitored. PeCO might be
-an interesting  method since  it achieves  a good balance  between a  high level
-coverage ratio and network lifetime. PeCO always outperforms DiLCO for the three
-lower  coverage  ratios,  moreover  the   improvements  grow  with  the  network
-size. DiLCO is better  for coverage ratios near 100\%, but in  that case PeCO is
-not ineffective for the smallest network sizes.
+respectively, where the term Protocol refers to  DiLCO  or PeCO.  Indeed there  are applications that do not require a 100\% coverage of  the area to be monitored. PeCO might be an interesting  method since  it achieves  a good balance  between a  high level coverage ratio and network lifetime. PeCO always outperforms DiLCO for the three lower  coverage  ratios,  moreover  the   improvements  grow  with  the  network size. DiLCO is better  for coverage ratios near 100\%, but in  that case PeCO is not ineffective for the smallest network sizes.
 
 \begin{figure} [p]
 \centering \includegraphics[scale=0.8]{Figures/ch6/R/LTa.eps}
@@ -474,24 +468,11 @@ not ineffective for the smallest network sizes.
 \section{Conclusion} 
 \label{ch6:sec:05}
 
-In this chapter, we have studied the problem of  Perimeter-based Coverage Optimization in
-WSNs. We have designed  a new protocol, called Perimeter-based  Coverage Optimization, which
-schedules nodes'  activities (wake up  and sleep  stages) with the  objective of
-maintaining a  good coverage ratio  while maximizing the network  lifetime. This
-protocol is  applied in a distributed  way in regular subregions  obtained after
-partitioning the area of interest in a preliminary step. It works in periods and
-is based on the resolution of an integer program to select the subset of sensors
-operating in active status for each period. Our work is original in so far as it
-proposes for  the first  time an  integer program  scheduling the  activation of
-sensors  based on  their perimeter  coverage level,  instead of  using a  set of
-targets/points to be covered. We  have carried out  several simulations  to  evaluate the  proposed protocol. The simulation  results  show   that  PeCO  is  more   energy-efficient  than  other approaches, with respect to lifetime,  coverage ratio, active sensors ratio, and
-energy consumption.
-
-We plan to extend our framework so that the schedules are planned for multiple
-sensing periods.
+In this chapter, we have studied the problem of  Perimeter-based Coverage Optimization in WSNs. We have designed  a new protocol, called Perimeter-based  Coverage Optimization, which schedules nodes'  activities (wake up  and sleep  stages) with the  objective of maintaining a  good coverage ratio  while maximizing the network  lifetime. This protocol is  applied in a distributed  way in regular subregions  obtained after partitioning the area of interest in a preliminary step. It works in periods and
+is based on the resolution of an integer program to select the subset of sensors operating in active status for each period. Our work is original because it proposes for  the first  time an  integer program  scheduling the  activation of sensors  based on  their perimeter  coverage level,  instead of  using a  set of targets/points to be covered. We  have carried out  several simulations  to  evaluate the  proposed protocol. The simulation  results  show   that  PeCO  is  more   energy-efficient  than  other approaches, with respect to lifetime,  coverage ratio, active sensors ratio, and energy consumption.
+
+%We plan to extend our framework so that the schedules are planned for multiple sensing periods. We also want  to improve our integer program to  take into account heterogeneous sensors  from both  energy  and node  characteristics point of views. Finally,  it   would  be   interesting  to  implement   our  protocol   using  a sensor-testbed to evaluate it in real world applications.
+
+
 %in order to compute all active sensor schedules in only one step for many periods;
-We also want  to improve our integer program to  take into account heterogeneous
-sensors  from both  energy  and node  characteristics point of views.
-%the third, we are investigating new optimization model based on the sensing range so as to maximize the lifetime coverage in WSN;
-Finally,  it   would  be   interesting  to  implement   our  protocol   using  a
-sensor-testbed to evaluate it in real world applications.
+%the third, we are investigating new optimization model based on the sensing range so as to maximize the lifetime coverage in WSN;
\ No newline at end of file
