From: ali Date: Fri, 6 Mar 2015 17:31:36 +0000 (+0100) Subject: Update by Ali X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/commitdiff_plain/ac806079ad3314f3c1e5fdabe0e654ce12f36935?ds=sidebyside Update by Ali --- diff --git a/CONCLUSION.tex b/CONCLUSION.tex index 8dd43ab..9a9606a 100644 --- a/CONCLUSION.tex +++ b/CONCLUSION.tex @@ -1,8 +1,11 @@ -\chapter*{Conclusion and Perspectives\markboth{Conclusion and Perspectives}{Conclusion and Perspectives}} -\label{chII} -\addcontentsline{toc}{chapter}{Conclusion and Perspectives} +%\chapter*{Conclusion and Perspectives\markboth{Conclusion and Perspectives}{Conclusion and Perspectives}} +%\label{ch7} +%\addcontentsline{toc}{chapter}{Conclusion and Perspectives} -\section*{Conclusion} +\chapter{Conclusion and Perspectives} +\label{ch07} + +\section{Conclusion} In this dissertation, we have concentrated on proposing a distributed optimization protocols so as to prolong the lifetime of wireless sensor networks. We have addressed the problem of the area coverage and the lifetime optimization in wireless sensor networks, where the ultimate goal is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area (or region) of interest. @@ -16,16 +19,27 @@ representative active nodes that will optimize the network lifetime while taking -In chapter 4, - +In chapter 4, we have proposed an optimization protocol, called Distributed Lifetime Coverage Optimization protocol (DiLCO), which optimizes the coverage and the lifetime of a wireless sensor network. DiLCO protocol is distributed in each sensor node in the subregions of the sensing field. It has been implemented in each subregion simultaneously and Independently. The proposed DiLCO protocol is a periodic protocol where each period consists of 4 phases: information exchange, leader election, decision, and sensing. The sensor nodes collaborate in each subregion to elect the leader. The leader applies activity scheduling based optimization so as to provides only one optimal set of active sensor nodes that takes the mission of sensing during this period. The performance of our DiLCO protocol has been evaluated by a series of simulations. We have presented a comparison between our proposed protocol and other two existing and known in the literature, DESK and GAF. The experimental results have validated our protocol and showed their efficiency in the optimization of the coverage and the lifetime compared to existing methods. + + +Next, we propose a method called Multiround Distributed Lifetime Coverage Optimization protocol (MuDiLCO) in chapter 5, to maintain the coverage and to improve the lifetime in wireless sensor networks. MuDiLCO protocol is an extension of the DiLCO protocol introduced in chapter 4. In MuDiLCO, the protocol has implemented activity scheduling based optimization in order provides a multiple set of active sensor nodes for several rounds in the sensing phase. We have introduced an improved coverage optimization model that make a multiround optimization, whilst it was a single round optimization in DiLCO. We have conducted several sets of simulations comparing the proposed MuDiLCO protocol for different number of rounds as well as with other existing coverage methods like DESK and GAF. + +In chapter 6, We have proposed an approach called Perimeter-based Coverage Optimization protocol (PeCO) in order to optimize the lifetime coverage, so that it provides activity scheduling which ensures sensing coverage as long as possible. PeCO protocol is distributed among sensor nodes in each subregion. The novelty of our approach lies essentially in the formulation of a new mathematical optimization model based on the perimeter coverage level to schedule sensors’ activities. The leader provides one schedule during the current period by executing the new integer program during the decision phase. The extensive simulation experiments have demonstrated that PeCO can offer longer lifetime coverage for WSNs in comparison with some other protocols. + +Finally, we outline some interesting issues that we will consider in our perspectives which are discussed in more detail next. + + - +\section{Perspectives} +In this dissertation, we have focused on the lifetime area coverage optimization problem and we are interested only in energy-efficient, distributed and parallel protocol. Various scenarios might need to be taken into consideration such as fault-tolerance, k-coverage, $\alpha$-coverage, adjustable sensor’s sensing range network, heterogeneous network, mobility, etc. In the future, we will concentrate on the following work: +In chapter 4, We have studied the impact of the number of subregions chosen to subdivide the area of interest, considering different network sizes. The optimal number of subregions will be investigated in the future. We also plan to study and propose a coverage protocol, which computes all active sensor schedules in one time, using optimization methods such as swarms optimization or evolutionary algorithms. The round will still consist of 4 phases, but the decision phase will compute the schedules for several sensing phases which, aggregated together, define a kind of meta-sensing phase. The computation of all cover sets in one time is far more difficult, but will reduce the communication overhead. +In chapter 5, we plan to design and propose a heterogeneous integrated optimization protocol in WSNs. This protocol integrates three energy-efficient (coverage, routing and data aggregation) protocols so as to extend the network lifetime in WSNs. The sensing, routing, and aggregation jobs are also challenges in WSNs. This integrated optimization protocol will be executed by each cluster head in the wireless sensor network. The cluster head will be selected in a distributed way and based on local information. -\section*{Perspectives} +In chapter 6, 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. diff --git a/INTRODUCTION.tex b/INTRODUCTION.tex index 32dcae3..995fefa 100755 --- a/INTRODUCTION.tex +++ b/INTRODUCTION.tex @@ -41,7 +41,7 @@ The coverage problem in WSNs is becoming more and more important for many applic \item We devise a framework to schedule nodes to be activated alternatively such that the network lifetime is prolonged while ensuring that a certain level of coverage is preserved. A key idea in our framework is to exploit spatial an temporal subdivision. On the one hand the area of interest if divided into several smaller subregions and on the other hand the time line is divided into periods of equal length. In each subregion the sensor nodes will cooperatively choose a leader which will schedule nodes' activities, and this grouping of sensors is similar to typical cluster architecture. We propose a new mathematical optimization model. Instead of trying to cover a set of specified points/targets as in most of the methods proposed in the literature, we formulate an integer program based on perimeter coverage of each sensor. The model involves integer variables to capture the deviations between the actual level of coverage and the required level. So that an optimal scheduling will be obtained by minimizing a weighted sum of these deviations. This contribution is demonstrated in Chapter 5. -\item We add an improved model of energy consumption to assess the efficiency of our protocols as well as we conducted extensive simulation experiments, using the discrete event simulator OMNeT++, to demonstrate the efficiency of our protocols. We compared our proposed distributed optimization protocols to two approaches found in the literature: DESK~\cite{DESK} and GAF~\cite{GAF}, simulation results based on multiple criteria (energy consumption, coverage ratio, network lifetime and so on) show that the proposed protocols can prolong efficiently the network lifetime and improve the coverage performance. +\item We add an improved model of energy consumption to assess the efficiency of our protocols as well as we conducted extensive simulation experiments, using the discrete event simulator OMNeT++, to demonstrate the efficiency of our protocols. We compared our proposed distributed optimization protocols to two approaches found in the literature: DESK~\cite{DESK} and GAF~\cite{GAF}, simulation results based on multiple criteria (energy consumption, coverage ratio, network lifetime and so on) show that the proposed protocols can prolong efficiently the network lifetime and improve the coverage performance. \end{enumerate} diff --git a/Thesis.toc b/Thesis.toc index 374c8f7..59e901c 100755 --- a/Thesis.toc +++ b/Thesis.toc @@ -99,5 +99,7 @@ \contentsline {subsubsection}{\numberline {6.4.2.4}The Network Lifetime}{112}{subsubsection.6.4.2.4} \contentsline {section}{\numberline {6.5}Conclusion}{113}{section.6.5} \contentsline {part}{III\hspace {1em}Conclusion and Perspectives}{117}{part.3} -\contentsline {chapter}{Conclusion and Perspectives}{119}{chapter*.6} -\contentsline {part}{Bibliographie}{134}{chapter*.9} +\contentsline {chapter}{\numberline {7}Conclusion and Perspectives}{119}{chapter.7} +\contentsline {section}{\numberline {7.1}Conclusion}{119}{section.7.1} +\contentsline {section}{\numberline {7.2}Perspectives}{120}{section.7.2} +\contentsline {part}{Bibliographie}{136}{chapter*.6}