\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 \\ The University of Franche-Comt\'e, 2015 \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 Supervisors: Raphaël Couturier, Karine Deschinkel, and Michel Salomon \end{center}}
-Wireless sensor networks (WSNs) have recently received a great deal of research attention due to their wide range of potential applications. in many different fields. Many important characteristics are provided by the WSNs and the sensors that make them different from other wireless ad-hoc networks, and very promising in a wide range of applications. On the other hand, these characteristics are imposed lots of limitations on the WSNs that would lead to several challenges in the network. These challenges might 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 a 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
+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 activity scheduling decisions for each subregion.
+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.
-First, we propose a Distributed Lifetime Coverage Optimization (DILCO) protocol in WSNs. In this protocol, the lifetime is divided into periods. Each period consists of 4 phases: information exchange, leader election, decision, and sensing. The decision process is
+First, we propose a protocol called Distributed Lifetime Coverage Optimization (DiLCO). In this protocol, the lifetime is divided into periods. Each period consists of 4 phases: information exchange, leader election, decision, and sensing. The decision process is
carried out by a leader node, which solves an integer program in order to provide only one cover set of active sensor nodes to ensure coverage during the sensing phase of the current period.
- Then we move to address the problem of a multiround optimization of the area coverage problem in WSNs. The Multiround Distributed Lifetime Coverage Optimization (MuDiLCO) protocol is suggested so as to study the possibility of providing multiple cover sets of sensors for the sensing phase. MuDiLCO protocol also works in periods
-during which sets of sensor nodes are scheduled to remain active for a number of rounds during the sensing phase, to ensure coverage so as to maximize the
-lifetime of WSN. The decision process is carried out by a leader node, which solves an integer program to produce the best representative sets to be used during the rounds of the sensing phase.
+ Then we address the problem of a multiround optimization of the area coverage problem in WSNs. The Multiround Distributed Lifetime Coverage Optimization (MuDiLCO) protocol is suggested so as to study the possibility of providing multiple cover sets of sensors for the sensing phase. MuDiLCO protocol also works in periods
+during which sets of sensor nodes are scheduled to remain active for a number of rounds during the sensing phase, to ensure coverage so as to maximize the lifetime of WSN. The decision process is still carried out by a leader node, which solves an integer program to produce the best representative sets to be used during the rounds of the sensing phase.
-Last but not least, we propose a Perimeter-based Coverage Optimization (PeCO) protocol to maintain the coverage and improve the network lifetime in WSNs. It is a hybrid of centralized and distributed methods, where it 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 the perimeter coverage level to schedule
-sensors' activities. The PeCO protocol resolves a new integer program coverage model 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
-the WSN lifetime and provides improved coverage performance.
+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
+the WSN lifetime and provide improved coverage performance.
-\textbf{KEY WORDS:} Wireless Sensor Networks, Area Coverage, Network Lifetime, Optimization, Scheduling, Distributed Algorithms, Energy-efficiency.
+\textbf{KEY WORDS:} Wireless Sensor Networks, Area Coverage, Network Lifetime, Distributed Optimization, Scheduling.
