-\emph{ \begin{center} \Large Distributed Coverage Optimization Techniques for Improving
- Lifetime of Wireless Sensor Networks \end{center}}
+\emph{ \begin{center} \Large Distributed coverage optimization techniques for improving
+ lifetime of wireless sensor networks \end{center}}
%\emph{ \begin{center} \large By \end{center}}
\emph{ \begin{center} \large Ali Kadhum Idrees \\ University of Franche-Comt\'e, 2015 \end{center}}
%\emph{ \begin{center} \large The University of Franche-Comt\'e, 2015 \end{center}}
%\emph{ \begin{center} \large By \end{center}}
\emph{ \begin{center} \large Ali Kadhum Idrees \\ University of Franche-Comt\'e, 2015 \end{center}}
%\emph{ \begin{center} \large The University of Franche-Comt\'e, 2015 \end{center}}
-Wireless sensor networks (WSNs) have recently received a great deal of research attention due to their wide range of potential applications. Many important characteristics provided by the WSNs make them different from other wireless ad-hoc networks. Furthermore, these characteristics impose lots of limitations that lead to several challenges in the network. These challenges include coverage, topology control, routing, data fusion, security, and many others. One of the main research challenges faced in wireless sensor networks is to preserve continuously and effectively the coverage of an area of interest to be monitored, while simultaneously preventing as much as possible a network failure due to battery-depleted nodes.
-
+%Wireless sensor networks (WSNs) have recently received a great deal of research attention due to their wide range of potential applications. Many important characteristics provided by the WSNs make them different from other wireless ad-hoc networks. Furthermore, these characteristics impose lots of limitations that lead to several challenges in the network. These challenges include coverage, topology control, routing, data fusion, security, and many others. One of the main research challenges faced in wireless sensor networks is to preserve continuously and effectively the coverage of an area of interest to be monitored, while simultaneously preventing as much as possible a network failure due to battery-depleted nodes.
In this dissertation, we highly focus on the area coverage problem, energy-efficiency is also the foremost requirement. We have considered distributed optimization protocols with the ultimate objective of prolonging the network lifetime. The proposed distributed optimization protocols (including algorithms, models, and solving integer programs) should be energy-efficient protocols. To address
this problem, this dissertation proposes two-step approaches. Firstly, the sensing field is divided into smaller subregions using the concept of divide-and-conquer method. Secondly, one of our proposed distributed optimization protocols is distributed and applied on the
sensor nodes in each subregion so as to optimize the coverage and the lifetime performances. In this dissertation, three coverage optimization protocols are proposed. These protocols combine two efficient techniques: leader election for each subregion, followed by an optimization-based planning of sensor activity scheduling decisions for each subregion.
In this dissertation, we highly focus on the area coverage problem, energy-efficiency is also the foremost requirement. We have considered distributed optimization protocols with the ultimate objective of prolonging the network lifetime. The proposed distributed optimization protocols (including algorithms, models, and solving integer programs) should be energy-efficient protocols. To address
this problem, this dissertation proposes two-step approaches. Firstly, the sensing field is divided into smaller subregions using the concept of divide-and-conquer method. Secondly, one of our proposed distributed optimization protocols is distributed and applied on the
sensor nodes in each subregion so as to optimize the coverage and the lifetime performances. In this dissertation, three coverage optimization protocols are proposed. These protocols combine two efficient techniques: leader election for each subregion, followed by an optimization-based planning of sensor activity scheduling decisions for each subregion.
-Last but not least, we propose a Perimeter-based Coverage Optimization (PeCO) protocol which is also 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 a perimeter coverage level to schedule sensors' activities, whereas we used primary points coverage model in the two previous models. A new integer program coverage model is solved by the leader during the decision phase so as to provide only one cover set of sensors for the sensing phase.
+Last but not least, we propose a Perimeter-based Coverage Optimization (PeCO) protocol which is also 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 a perimeter coverage level to schedule sensors' activities, whereas we used primary points coverage model in the two previous models. A new integer program coverage model is solved by the leader during the decision phase so as to provide only one cover set of sensors for the sensing phase.
-Extensive simulations are conducted using the discrete event simulator OMNET++ to validate the efficiency of each of our proposed protocols. We refer to the characteristics of a Medusa II sensor for the energy consumption and the time computation. In comparison with two other existing methods, our protocols are able to increase
+Extensive simulations are conducted using the discrete event simulator OMNeT++ to validate the efficiency of each of our proposed protocols. We refer to the characteristics of a Medusa II sensor for the energy consumption and the time computation. In comparison with two other existing methods, our protocols are able to increase
