From 7c91ad0ef9e5b3ba9e8d84cfed55bc7692a8e359 Mon Sep 17 00:00:00 2001 From: ali Date: Wed, 1 Apr 2015 12:05:03 +0200 Subject: [PATCH] Update by Ali --- CHAPITRE_02.tex | 20 +++++++++++++++++++- entete.tex | 2 +- upmext-spimufcphdthesis.cfg | 12 ++++++------ 3 files changed, 26 insertions(+), 8 deletions(-) diff --git a/CHAPITRE_02.tex b/CHAPITRE_02.tex index 1534b6a..c3082a7 100755 --- a/CHAPITRE_02.tex +++ b/CHAPITRE_02.tex @@ -342,9 +342,27 @@ check if its $n_i$ is decreased to 0 or not. If $n_i$ of a sensor node is 0 (i.e \label{ch2:sec:05} This chapter describes some coverage proposed problems in the literature, with their assumptions and proposed solutions. The coverage problem has been considered an essential requirement for many applications in WSNs because the better coverage of an area of interest provides better sensing measurements of the physical phenomenon. Therefore, many extensive researches have been focused on that problem. These researches have aimed at designing mechanisms that efficiently manage or schedule the sensors after deployment, since sensor nodes have a limited battery life. +Many coverage algorithms for maintaining the coverage and improving the network lifetime in WSNs were proposed. On one hand, the full centralized coverage algorithms provide optimal or near optimal solution with low computation power but they deplete the battery power due to the communication overhead, as well as they are not scalable for large size networks. On the other hand, the distributed coverage algorithms provide a lower quality solution in comparison with centralized approaches and consume more power for computation but they are reliable, scalable, and provide low communication overhead in WSNs. Whatever the case, this would result in a shorter lifetime coverage in WSNs As shown in table \ref{Table0:ch2}, each of the two coverage approaches has advantages and disadvantages. Therefore, each approach can be used based on the application requirements. We conclude from this chapter that it is desirable to introduce a hybrid approach that take into account the advantages of both centralized and distributed coverage approaches. This hybrid approaches can provide a good quality coverage and prolong the network lifetime. -In spite of many energy-efficient protocols for maintaining the coverage and improving the network lifetime in WSNs were proposed, none of them ensure the coverage for the sensing field with optimal minimum number of active sensor nodes, and for a long time as possible. In full centralized algorithms, the optimal solutions can be given by using optimization approaches, but in the same time, a high energy is consumed for the execution time of the algorithm and the communications among the sensors in the sensing field. Therefore, the full centralized approaches are not a good candidate to be used especially in large WSNs. Whilst, a fully distributed algorithms can not give optimal solutions because these algorithms use only local information of the neighboring sensors, but in the same time, the energy consumption during the communications and executing the algorithm is highly lower. Whatever the case, this would result in a shorter lifetime coverage in WSNs + + + + + + + + + + + + + + + + +%Many coverage algorithms for maintaining the coverage and improving the network lifetime in WSNs were proposed. On one hand, the centralized coverage algorithms provide optimal or near optimal solution with low computation power but they deplete the battery power due to the communication overhead, as well as they are not scalable for large size networks. On the other hand, the distributed coverage algorithms provide a lower quality solution in comparison with centralized approaches and consume more power for computation but they are reliable, scalable, and provide low communication overhead on the WSNs. + %none of them ensure the coverage for the sensing field with optimal minimum number of active sensor nodes, and for a long time as possible. In full centralized algorithms, the optimal solutions can be given by using optimization approaches, but in the same time, a high energy is consumed for the execution time of the algorithm and the communications among the sensors in the sensing field. Therefore, the full centralized approaches are not a good candidate to be used especially in large WSNs. Whilst, a fully distributed algorithms can not give optimal solutions because these algorithms use only local information of the neighboring sensors, but in the same time, the energy consumption during the communications and executing the algorithm is highly lower. Whatever the case, this would result in a shorter lifetime coverage in WSNs % Several centralized approaches have been demonstrated, where they are concentrated on modeling the coverage problem and provide the maximum cover set so as to extend the network lifetime. The proposed algorithms are executed in a central node and based on global information. The central node transmits the resulted schedule to other nodes in the network. Even if the centralized algorithms have been produced optimal or near optimal solutions, It seems to be difficult and unpractical to apply the full centralized approaches in WSNs. On the other hand, many distributed algorithms have been described. These approaches seem to be more realistic to be used in WSNs from point of view of designer, but they can not assure optimal or near optimal solutions so as to extend the network lifetime as long time as possible. diff --git a/entete.tex b/entete.tex index 844d3e7..c56ba45 100755 --- a/entete.tex +++ b/entete.tex @@ -58,7 +58,7 @@ %% The second mandatory parameter is the date of the PhD defense. %% The third mandatory parameter is the reference number given by the University Library after the PhD defense. %%\declarethesis[Sous-titre]{Titre}{17 septembre 2012}{XXX} -\declarethesis{Distributed Optimization Techniques for Improving Lifetime of Wireless Networks}{30 September 2015}{2015930} +\declarethesis{Distributed Coverage Optimization Techniques for Improving Lifetime of Wireless Sensor Networks}{30 September 2015}{2015930} %%-------------------- diff --git a/upmext-spimufcphdthesis.cfg b/upmext-spimufcphdthesis.cfg index f1ad8e7..db8fc83 100755 --- a/upmext-spimufcphdthesis.cfg +++ b/upmext-spimufcphdthesis.cfg @@ -50,7 +50,7 @@ \Set{jurystyle}{} % Defense message -\Set{defensemessage}{COMMITTEE:} +\Set{defensemessage}{Dissertation Committee:} \Set{cfrontpage}{ @@ -104,17 +104,17 @@ \begin{center}% -{\huge \textbf{Distributed Optimization Techniques for Improving Lifetime of Wireless Networks}} \\[.5cm] +{\huge \textbf{Distributed Coverage Optimization Techniques for Improving Lifetime of Wireless Sensor Networks}} \\[.5cm] {\large By}\\[.5cm]% {\huge Ali Kadhum IDREES}\\[.5cm]% \end{center} \begin{center}% -{\Large Dissertation Submitted to the} \\[.5cm] +{\Large A Dissertation Submitted to the} \\[.5cm] {\Large University of Franche-Comt\'e} \\[.5cm] -{\Large In Partial Fulfillment of the Requirements}\\[.5cm] -{\Large For the Ph.D. Degree in}\\[.5cm] -{\Large Computer Science}\\[.5cm] +{\Large in Partial Fulfillment of the Requirements for the degree of}\\[.5cm] +{\Large DOCTOR OF PHILOSOPHY}\\[.5cm] +{\Large in Computer Science}\\[.5cm] \end{center} -- 2.39.5