From: ali Date: Fri, 20 Mar 2015 16:57:31 +0000 (+0100) Subject: Update by Ali X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/commitdiff_plain/6cacb63d8ed60c9822de6be85af0c827783658df?ds=sidebyside Update by Ali --- diff --git a/CHAPITRE_04.tex b/CHAPITRE_04.tex index ad69a0b..c06ac21 100755 --- a/CHAPITRE_04.tex +++ b/CHAPITRE_04.tex @@ -8,7 +8,7 @@ \label{ch4} - +\iffalse \section{Summary} \label{ch4:sec:01} In this chapter, a Distributed Lifetime Coverage Optimization protocol (DiLCO) to maintain @@ -26,6 +26,23 @@ carried out by a leader node, which solves an integer program. Compared with some existing protocols, simulation results show that the proposed protocol can prolong the network lifetime and improve the coverage performance effectively. +\fi + +\section{Introduction} +\label{ch4:sec:01} +Energy efficiency is a crucial issue in wireless sensor networks since the sensory consumption, in order to maximize the network lifetime, represents the major difficulty when designing WSNs. As a consequence, one of the scientific research challenges in WSNs, which has been addressed by a large amount of literature +during the last few years, is the design of energy efficient approaches for coverage and connectivity~\cite{ref94}. Coverage reflects how well a sensor field is monitored. On the one hand, we want to monitor the area of interest in the most efficient way~\cite{ref95}. On the other hand, we want to use as little energy as possible. Sensor nodes are battery-powered with no means of recharging or replacing, usually due to environmental (hostile or +unpractical environments) or cost reasons. Therefore, it is desired that the WSNs are deployed with high densities so as to exploit the overlapping sensing regions of some sensor nodes to save energy by turning off some of them during the sensing phase to prolong the network lifetime. + +In this chapter, we design a protocol that focuses on the area coverage problem with the objective of maximizing the network lifetime. Our proposition, the Distributed Lifetime Coverage Optimization (DiLCO) protocol, maintains the coverage and improves the lifetime in WSNs. The area of interest is first +divided into subregions using a divide-and-conquer algorithm and an activity scheduling for sensor nodes is then planned by the elected leader in each subregion. In fact, the nodes in a subregion can be seen as a cluster where each node sends sensing data to the cluster head or the sink node. Furthermore, the activities in a subregion/cluster can continue even if another cluster stops due +to too many node failures. Our DiLCO protocol considers periods, where a period starts with a discovery phase to exchange information between sensors of the same subregion, in order to choose in a suitable manner a sensor node (the leader) to carry out the coverage strategy. In each subregion, the activation of the sensors for the sensing phase of the current period is obtained by solving +an integer program. The resulting activation vector is broadcast by a leader to every node of its subregion. + +The remainder of this chapter is organized as follows. The next section is devoted to the DiLCO protocol description. Section \ref{ch4:sec:03} gives the primary points based coverage problem formulation which is used to schedule the activation of sensors. Section \ref{ch4:sec:04} shows the simulation +results obtained using the discrete event simulator OMNeT++ \cite{ref158}. They fully demonstrate the usefulness of the proposed approach. Finally, we give concluding remarks in section \ref{ch4:sec:05}. + + \section{Description of the DiLCO Protocol} \label{ch4:sec:02} diff --git a/CHAPITRE_05.tex b/CHAPITRE_05.tex index a49a28a..ba1dc5a 100755 --- a/CHAPITRE_05.tex +++ b/CHAPITRE_05.tex @@ -7,6 +7,8 @@ \chapter{Multiround Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks} \label{ch5} +\iffalse + \section{Summary} \label{ch5:sec:01} Coverage and lifetime are two paramount problems in Wireless Sensor Networks (WSNs). In this paper, a method called Multiround Distributed Lifetime Coverage @@ -17,6 +19,47 @@ lifetime of WSN. The decision process is carried out by a leader node, which during the rounds of the sensing phase. Compared with some existing protocols, simulation results based on multiple criteria (energy consumption, coverage ratio, and so on) show that the proposed protocol can prolong efficiently the network lifetime and improve the coverage performance. +\fi + +\section{Introduction} +\label{ch5:sec:01} + +\indent The fast developments of low-cost sensor devices and wireless +communications have allowed the emergence of WSNs. A WSN includes a large number +of small, limited-power sensors that can sense, process, and transmit data over +a wireless communication. They communicate with each other by using multi-hop +wireless communications and cooperate together to monitor the area of interest, +so that each measured data can be reported to a monitoring center called sink +for further analysis~\cite{ref222}. There are several fields of application +covering a wide spectrum for a WSN, including health, home, environmental, +military, and industrial applications~\cite{ref19}. + +On the one hand sensor nodes run on batteries with limited capacities, and it is +often costly or simply impossible to replace and/or recharge batteries, +especially in remote and hostile environments. Obviously, to achieve a long life +of the network it is important to conserve battery power. Therefore, lifetime +optimization is one of the most critical issues in wireless sensor networks. On +the other hand we must guarantee coverage over the area of interest. To fulfill +these two objectives, the main idea is to take advantage of overlapping sensing +regions to turn-off redundant sensor nodes and thus save energy. In this paper, +we concentrate on the area coverage problem, with the objective of maximizing +the network lifetime by using an optimized multiround scheduling. + +We study the problem of designing an energy-efficient optimization algorithm that divides the sensors in a WSN into multiple cover sets such that the area of interest is monitored as long as possible. Providing multiple cover sets can be used to improve the energy efficiency of WSNs. Therefore, in order to increase the longevity of the WSN and conserve the energy, it can be useful to provide multiple cover sets in one time after that schedule them for multiple rounds, so that the battery life of a sensor is not wasted due to the repeated execution of the coverage optimization algorithm, as well as the information exchange and leader election. + +The MuDiLCO protocol (for Multiround Distributed Lifetime Coverage Optimization protocol) presented in this chapter is an extension of the approach introduced in chapter 4. Simulation results have shown that it was more interesting to divide the area into several subregions, given the computation complexity. Compared to our protocol in chapter 4, in this one we study the possibility of dividing the sensing phase into multiple rounds. In fact, in this chapter we make a multiround optimization, while it was a single round optimization in our protocol in chapter 4. + +The remainder of the chapter continues with section \ref{ch5:sec:02} where a detail of MuDiLCO Protocol is presented. The next section describes the Primary Points based Multiround Coverage Problem formulation which is used to schedule the activation of sensors in T cover sets. Section \ref{ch5:sec:04} shows the simulation +results. The chapter ends with a conclusion and some suggestions for further work. + + + + + + + + + \section{MuDiLCO Protocol Description} \label{ch5:sec:02} \noindent In this section, we introduce the MuDiLCO protocol which is distributed on each subregion in the area of interest. It is based on two energy-efficient @@ -100,8 +143,9 @@ The energy consumption and some other constraints can easily be taken into -\subsection{Primary Points based Multiround Coverage Problem Formulation} -%\label{ch5:sec:02:02} +\section{Primary Points based Multiround Coverage Problem Formulation} +\label{ch5:sec:03} + According to our algorithm~\ref{alg:MuDiLCO}, the integer program is based on the model proposed by \cite{ref156} with some modifications, where the objective is @@ -221,10 +265,10 @@ large compared to $W_{\theta}$. \section{Experimental Study and Analysis} -\label{ch5:sec:03} +\label{ch5:sec:04} \subsection{Simulation Setup} -\label{ch5:sec:03:01} +\label{ch5:sec:04:01} We conducted a series of simulations to evaluate the efficiency and the relevance of our approach, using the discrete event simulator OMNeT++ \cite{ref158}. The simulation parameters are summarized in Table~\ref{table3}. Each experiment for a network is run over 25~different random topologies and the results presented hereafter are the average of these @@ -293,7 +337,7 @@ We used the modeling language and the optimization solver which are mentioned in %The initial energy of each node is randomly set in the interval $[500;700]$. A sensor node will not participate in the next round if its remaining energy is less than $E_{R}=36~\mbox{Joules}$, the minimum energy needed for the node to stay alive during one round. This value has been computed by multiplying the energy consumed in active state (9.72 mW) by the time in second for one round (3600 seconds). According to the interval of initial energy, a sensor may be alive during at most 20 rounds. \subsection{Metrics} -\label{ch5:sec:03:02} +\label{ch5:sec:04:02} To evaluate our approach we consider the following performance metrics: \begin{enumerate}[i] @@ -359,7 +403,7 @@ indicate the energy consumed by the whole network in round $t$. \subsection{Results Analysis and Comparison } -\label{ch5:sec:03:02} +\label{ch5:sec:04:02} \begin{enumerate}[(i)] @@ -516,7 +560,7 @@ energy consumption, since network lifetime and energy consumption are directly \section{Conclusion} -\label{ch5:sec:04} +\label{ch5:sec:05} We have addressed the problem of the coverage and of the lifetime optimization in wireless sensor networks. This is a key issue as sensor nodes have limited resources in terms of memory, energy, and computational power. To cope with this problem, the field of sensing is divided into smaller subregions using the concept of divide-and-conquer method, and then we propose a protocol which optimizes coverage and lifetime performances in each subregion. Our protocol, called MuDiLCO (Multiround Distributed Lifetime Coverage Optimization) combines two efficient techniques: network leader election and sensor activity scheduling. diff --git a/Thesis.toc b/Thesis.toc index 33c9ace..656ebdb 100755 --- a/Thesis.toc +++ b/Thesis.toc @@ -54,35 +54,35 @@ \contentsline {section}{\numberline {3.4}Conclusion}{61}{section.3.4} \contentsline {part}{II\hspace {1em}Contributions}{63}{part.2} \contentsline {chapter}{\numberline {4}Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}{65}{chapter.4} -\contentsline {section}{\numberline {4.1}Summary}{65}{section.4.1} -\contentsline {section}{\numberline {4.2}Description of the DiLCO Protocol}{65}{section.4.2} -\contentsline {subsection}{\numberline {4.2.1}Assumptions and Network Model}{65}{subsection.4.2.1} -\contentsline {subsection}{\numberline {4.2.2}Primary Point Coverage Model}{66}{subsection.4.2.2} -\contentsline {subsection}{\numberline {4.2.3}Main Idea}{67}{subsection.4.2.3} -\contentsline {subsubsection}{\numberline {4.2.3.1}Information Exchange Phase}{68}{subsubsection.4.2.3.1} +\contentsline {section}{\numberline {4.1}Introduction}{65}{section.4.1} +\contentsline {section}{\numberline {4.2}Description of the DiLCO Protocol}{66}{section.4.2} +\contentsline {subsection}{\numberline {4.2.1}Assumptions and Network Model}{66}{subsection.4.2.1} +\contentsline {subsection}{\numberline {4.2.2}Primary Point Coverage Model}{67}{subsection.4.2.2} +\contentsline {subsection}{\numberline {4.2.3}Main Idea}{68}{subsection.4.2.3} +\contentsline {subsubsection}{\numberline {4.2.3.1}Information Exchange Phase}{69}{subsubsection.4.2.3.1} \contentsline {subsubsection}{\numberline {4.2.3.2}Leader Election Phase}{69}{subsubsection.4.2.3.2} \contentsline {subsubsection}{\numberline {4.2.3.3}Decision phase}{69}{subsubsection.4.2.3.3} \contentsline {subsubsection}{\numberline {4.2.3.4}Sensing phase}{69}{subsubsection.4.2.3.4} \contentsline {section}{\numberline {4.3}Primary Points based Coverage Problem Formulation}{70}{section.4.3} -\contentsline {section}{\numberline {4.4}Simulation Results and Analysis}{71}{section.4.4} -\contentsline {subsection}{\numberline {4.4.1}Simulation Framework}{71}{subsection.4.4.1} +\contentsline {section}{\numberline {4.4}Simulation Results and Analysis}{72}{section.4.4} +\contentsline {subsection}{\numberline {4.4.1}Simulation Framework}{72}{subsection.4.4.1} \contentsline {subsection}{\numberline {4.4.2}Modeling Language and Optimization Solver}{72}{subsection.4.4.2} \contentsline {subsection}{\numberline {4.4.3}Energy Consumption Model}{72}{subsection.4.4.3} \contentsline {subsection}{\numberline {4.4.4}Performance Metrics}{73}{subsection.4.4.4} \contentsline {subsection}{\numberline {4.4.5}Performance Analysis for Different Subregions}{74}{subsection.4.4.5} -\contentsline {subsection}{\numberline {4.4.6}Performance Analysis for Primary Point Models}{79}{subsection.4.4.6} -\contentsline {subsection}{\numberline {4.4.7}Performance Comparison with other Approaches}{86}{subsection.4.4.7} +\contentsline {subsection}{\numberline {4.4.6}Performance Analysis for Primary Point Models}{80}{subsection.4.4.6} +\contentsline {subsection}{\numberline {4.4.7}Performance Comparison with other Approaches}{85}{subsection.4.4.7} \contentsline {section}{\numberline {4.5}Conclusion}{91}{section.4.5} \contentsline {chapter}{\numberline {5}Multiround Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}{93}{chapter.5} -\contentsline {section}{\numberline {5.1}Summary}{93}{section.5.1} -\contentsline {section}{\numberline {5.2}MuDiLCO Protocol Description}{93}{section.5.2} +\contentsline {section}{\numberline {5.1}Introduction}{93}{section.5.1} +\contentsline {section}{\numberline {5.2}MuDiLCO Protocol Description}{94}{section.5.2} \contentsline {subsection}{\numberline {5.2.1}Background Idea and Algorithm}{94}{subsection.5.2.1} -\contentsline {subsection}{\numberline {5.2.2}Primary Points based Multiround Coverage Problem Formulation}{95}{subsection.5.2.2} -\contentsline {section}{\numberline {5.3}Experimental Study and Analysis}{97}{section.5.3} -\contentsline {subsection}{\numberline {5.3.1}Simulation Setup}{97}{subsection.5.3.1} -\contentsline {subsection}{\numberline {5.3.2}Metrics}{98}{subsection.5.3.2} -\contentsline {subsection}{\numberline {5.3.3}Results Analysis and Comparison }{99}{subsection.5.3.3} -\contentsline {section}{\numberline {5.4}Conclusion}{103}{section.5.4} +\contentsline {section}{\numberline {5.3}Primary Points based Multiround Coverage Problem Formulation}{95}{section.5.3} +\contentsline {section}{\numberline {5.4}Experimental Study and Analysis}{97}{section.5.4} +\contentsline {subsection}{\numberline {5.4.1}Simulation Setup}{97}{subsection.5.4.1} +\contentsline {subsection}{\numberline {5.4.2}Metrics}{98}{subsection.5.4.2} +\contentsline {subsection}{\numberline {5.4.3}Results Analysis and Comparison }{99}{subsection.5.4.3} +\contentsline {section}{\numberline {5.5}Conclusion}{104}{section.5.5} \contentsline {chapter}{\numberline {6}Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks}{107}{chapter.6} \contentsline {section}{\numberline {6.1}Summary}{107}{section.6.1} \contentsline {section}{\numberline {6.2}The PeCO Protocol Description}{107}{section.6.2} diff --git a/bib.bib b/bib.bib index 0d22ad5..8864ed2 100755 --- a/bib.bib +++ b/bib.bib @@ -2077,4 +2077,11 @@ ISSN={2153-0025},} pages={1--6}, year={2010}, organization={IEEE} -} \ No newline at end of file +} + +@book{ref222, + author = {S. Misra and I. Woungang and S. C. Misra}, + title = {Guide to Wireless Sensor Networks}, + publisher = {Springer-Verlag London Limited}, + year = {2009}, +} \ No newline at end of file