X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/9b375b5cf26913ef7db49a28e9ba77dfca00711d..1742a9c217544a81beabede3104d4b88690996f2:/CHAPITRE_03.tex

diff --git a/CHAPITRE_03.tex b/CHAPITRE_03.tex
index c75aa5d..fda74c3 100644
--- a/CHAPITRE_03.tex
+++ b/CHAPITRE_03.tex
@@ -14,9 +14,10 @@
 Performance evaluation and optimization solvers are important tools and they have received a great interest by many researchers around the world. In the last few years, several intensive researches have been done about the WSNs, and for a wide range of real-world applications. Therefore, the performance evaluation of algorithms and protocols becomes challenging at various stages of design, development, and implementation. In order to perform an efficient deployment, it is desirable to analyze  the performance of the newly designed algorithms and protocols in WSNs.  Performance evaluation tools are becoming precious means for evaluating the efficiency of algorithms and protocols in WSNs. 
 On the other side, the main challenges in the design of WSNs handle new hard and complex theoretical problems in optimization. These optimization problems are related to several topics in WSNs such as coverage, topology control, scheduling, routing, mobility, etc. 
 %So, the optimization is very important in WSNs because the limited resources of the sensor nodes. 
-For this reason, several problems are modeled by an optimization problem for optimizing the network lifetime and satisfying the application requirements. 
+For this reason, several problems are modeled by an optimization problem for instance to optimize the network lifetime while satisfying the application requirements. 
 %Therefore, in order to get the optimal solutions for these mathematical optimization problems, the optimization solver is the best candidate tool to solve them. The optimization solver takes mathematical optimization problem descriptions in a certain file format and calculates their optimal solution. 
-While optimization solvers dedicated to specific resolution methods are required linear programming.
+Optimization solvers dedicated to specific resolution methods (meta-heuristics, linear programming, etc) are required.
+Many important real-world problems have formulated as integer programming problems. In this dissertation, we use the linear programming because we used integer programs to optimize the coverage and the lifetime in WSNs.
  
 \section{Evaluation Tools}
 Several proposed works in WSNs require evaluating the power depletion efficiently and accurately for network lifetime prediction. On the other hand, the wrong energy evaluation leads to waste of energy because the sensor nodes might be rendered useless long time before draining their energy. Furthermore, the sensor nodes might die in advance of the expected lifetime. However, evaluation experiments on actually deployed WSN suffer some constraints because of the large number of sensor nodes deployed in hostile and inaccessible environments. Moreover, the analytical (or theoretical) models might be unrealistic for real world  systems. 
@@ -99,16 +100,16 @@ The simulation models are released from interdependency that usually found in an
 
 \item \textbf{TOSSIM:}  
  
-TOSSIM~\cite{ref205,ref207,ref208} is a discrete event simulator for TinyOS sensor networks, where the TinyOS application can be compiled on the TOSSIM framework, which executes on a computer rather than on the mote. This allow the users to test, debug, and analyze theirs algorithms in a controlled and repeatable environment. The users can check up their codes using debuggers and other development tools for executing them on the computer. TOSSIM is regarded as an emulator rather than a simulator because of its ability to simulate both software and hardware of the mote. TOSSIM is specially-designed for TinyOS applications run on Berkeley MICA Motes. TOSSIM has to develop four requirements:  scalability, completeness, fidelity, and bridging. It should manage a large number of sensor nodes with different configurations to be scalable. For completeness, it has to capture behavior and interactions of a system at a different of levels.  The simulator should capture behavior of a network with accurate timing of interactions on a mote and among motes for fidelity. The bridging requirement is satisfied due to executing the simulated code directly in a real mote. Two programming interfaces are supported by TOSSIM: Python and C++.  The C++ interface transforms the code easily from one form to another. Python allows interaction with an executing simulation dynamically, like a powerful debugger.  TOSSIM provides a high fidelity and scalable simulation of a complete TinyOS sensor network. It visualizes and interacts with executing simulations using GUI tool and TinyViz. The users can develop new visualizations and interfaces for TinyViz using simple plug-in model.  The simulator's effectiveness for analyzing low-level protocols is decreased due to inaccuracies of probabilistic bit error model. Moreover, TOSSIM is only supported by MICA motes platform.
+TOSSIM~\cite{ref205,ref207,ref208} is a discrete event simulator for TinyOS sensor networks, where the TinyOS application can be compiled on the TOSSIM framework, which executes on a computer rather than on the mote. This allow the users to test, debug, and analyze theirs algorithms in a controlled and repeatable environment. The users can check up their codes using debuggers and other development tools for executing them on the computer. TOSSIM is regarded as an emulator rather than a simulator because of its ability to simulate both software and hardware of the mote. TOSSIM is specially-designed for TinyOS applications run on Berkeley MICA Motes. TOSSIM should be developed to include four requirements:  scalability, completeness, fidelity, and bridging. It should manage a large number of sensor nodes with different configurations to be scalable. For completeness, it has to capture behavior and interactions of a system at a different of levels.  The simulator should capture behavior of a network with accurate timing of interactions on a mote and among motes for fidelity. The bridging requirement is satisfied due to executing the simulated code directly in a real mote. Two programming interfaces are supported by TOSSIM: Python and C++.  The C++ interface transforms the code easily from one form to another. Python allows interaction with an executing simulation dynamically, like a powerful debugger.  TOSSIM provides a high fidelity and scalable simulation of a complete TinyOS sensor network. It visualizes and interacts with executing simulations using GUI tool and TinyViz. The users can develop new visualizations and interfaces for TinyViz using simple plug-in model.  The simulator's effectiveness for analyzing low-level protocols is decreased due to inaccuracies of probabilistic bit error model. Moreover, TOSSIM is only supported by MICA motes platform.
 
 
 \item \textbf{GTSNetS:}  
 
 GTSNetS (Georgia Tech Sensor Network Simulator)~\cite{ref209,ref210} is an open-source, C++, large scale, event-driven simulation tool to evaluate the applications, algorithms, and protocols. It is capable of  evaluating  the impact of various architectural choices and designs on the lifetime and performance of a particular sensor network. GTSNetS is constructed on the top of the Georgia Tech Network Simulator (GTNetS), where it uses and expands all the design choices of the existing GTNetS simulator. The main feature of GTSNetS simulator is to support several thousand nodes.
-It is organized efficiently in modular way, to support large-scale WSNs. It is designed to be easy to use by the users in order to simulate a certain sensor network. Several choices are provided by GTSNetS to users to select from different alternatives such as network protocols, energy models, applications, and tracing options. Furthermore, the existing models of the simulator can simply extended or replaced according to user need. The network lifetime can be tracked by GTSNetS and the energy consumption of each unit can be evaluated. Therefore, the users can study the impact of different architectural choices on lifetime and energy consumption.  The mobility is inherited from GTNetS simulator. Therefore, it provides a specification of mobile sensor nodes, moving sensed objects, as well as a mobile base station.
+It is organized efficiently in modular way to support large-scale WSNs. It is designed to be easy to use by the users in order to simulate a certain sensor network. Several choices are provided by GTSNetS to users to select from different alternatives such as network protocols, energy models, applications, and tracing options. Furthermore, the existing models of the simulator can simply extended or replaced according to user need. The network lifetime can be tracked by GTSNetS and the energy consumption of each unit can be evaluated. Therefore, the users can study the impact of different architectural choices on lifetime and energy consumption.  The mobility is inherited from GTNetS simulator. Therefore, it provides a specification of mobile sensor nodes, moving sensed objects, as well as a mobile base station.
 GTSNetS provides graphical user interface and extensive packet tracing. 
 %The stopped updating and maintaining the project since Oct, 2008 represents the main disadvantage of this simulator.
-The project, the tool is no longer maintained since october 2008.
+The tool is no longer maintained since October 2008.
 
 \end{enumerate}
 
@@ -165,8 +166,6 @@ In this section, we investigated some simulation tools for WSNs. A large number
 \end{table}
 
 
-
-
 \section{Optimization Solvers}
 
 Several optimization solvers exist, which are able to solve the linear optimization problems. Linear programming~\cite{ref211} is a technique for determining the maximum or minimum of a linear function of non-negative variables subject to constraints expressed as linear equalities or inequalities. Linear Programming is a special case of mathematical programming (mathematical optimization).
@@ -225,7 +224,7 @@ The Gurobi Optimizer~\cite{ref219,ref220,ref211} is a commercial optimization so
 \end{enumerate}
 
 
-B. Meindl and M. Templ~\cite{ref212} studied the efficiency of above optimization solvers. They used the attacker problems in order to achieve the performance comparison of GLPK, lp$\_$solve, CLP, GUROBI, and CPLEX optimization solvers. They considered a total of 200 problem instances for this study, 100 of these problem instances are based on problems with two dimensions, and 100 problem instances are three-dimensional.
+B. Meindl and M. Templ~\cite{ref212} studied the efficiency of above optimization solvers. They used a set of instances of a difficult optimization problems called the attacker problems in order to achieve the performance comparison of GLPK, lp$\_$solve, CLP, GUROBI, and CPLEX optimization solvers. They considered a total of 200 problem instances for this study, 100 of these problem instances are based on problems with two dimensions, and 100 problem instances are three-dimensional.
 Tables~\ref{my-label1}, \ref{my-label2}, and \ref{my-label3} compares the running times of the five linear program solvers to find solutions to the 200 two-dimensional, 200 three-dimensional, and all 400 problem instances.  In order to solve the attackerâs problem for a given problem instance, it is needed to both minimize and maximize any given problem. Therefore, a total of 400  problem instances had  been solved when only 200 problem instances have been generated.
 
 
@@ -277,14 +276,11 @@ The illustrated results in tables~\ref{my-label1}, \ref{my-label2}, and \ref{my-
 \item It is easy to use the GLPK  solver and it is possible to call it's routines within the simulator.
 \item The GLPK comes with a stand-alone solver, a callable library, and the modeling language GMPL. The GMPL is compatible with AMPL and is extremely easy to learn.
  \item  Modeling language and solver can be used independently.
-\item GUI is available for Windows, Mac OS X, and Linux. 
+\item GUI is available for Windows, Mac OS X, and Linux. Java, Python, and Matlab interfaces are available.
 \item Database support and formatted text output.
-\item Java, Python, and Matlab interfaces are available.
 \item Exact simplex algorithm and branch-and-bound method are integrated with GLPK.
 
 \end{enumerate}
 
-
-
 \section{Conclusion}
-\indent In this chapter, an overview of the evaluation tools for wireless sensor networks and optimization solvers have been presented. The testbed for wireless sensor network and some major types have been demonstrated. We have found that most researchers in the field of WSNs use the simulators to evaluate theirs works because they are free, easy to use, more flexible, and scalable for a large WSNs. The simulation tools and several types of wireless sensor network simulators are described. The comparison among some types of network simulators show that OMNeT++ simulator a good candidate to be used as performance evaluation tool so as to evaluate the efficiency of our protocols in this dissertation. This chapter highlights the optimization problem in WSNs and the most popular free and commercial linear optimization solvers. The performance of the commercial optimization solvers outperforms the free optimization solvers. GLPK has been chosen as a good candidate to solve the proposed optimization problems in this dissertation because it is free, easy to use, and better than some other free optimization solvers.
\ No newline at end of file
+\indent In this chapter, an overview of the evaluation tools for wireless sensor networks and optimization solvers have been presented. The testbed for wireless sensor network and some major types have been demonstrated. We have found that most researchers in the field of WSNs use the simulators to evaluate theirs works because they are free, easy to use, more flexible, and scalable for a large WSNs. The simulation tools and several types of wireless sensor network simulators are described. The comparison among some types of network simulators show that OMNeT++ simulator is a good candidate to be used as performance evaluation tool so as to evaluate the efficiency of our protocols in this dissertation. This chapter highlights the optimization problem in WSNs and the most popular free and commercial linear optimization solvers. The performance of the commercial optimization solvers outperforms the free optimization solvers. GLPK has been chosen as a good candidate to solve the proposed optimization problems in this dissertation because it is free, easy to use, and better than some other free optimization solvers.
\ No newline at end of file
