\item[DILCO] Distributed Lifetime Coverage Optimization
\item[MuDiLCO] Multiround Distributed Lifetime Coverage Optimization
\item[PeCO] Perimeter-based Coverage Optimization
+\item[OMNeT++] Objective Modular Network Testbed
\item[DESK] Distributed Energy-efficient Scheduling for K-coverage
\item[GAF] Geographical Adaptive Fidelity
\item[PDA] Personal Digital Assistant
\item[MAV] Micro Aerial Vehicle
\item[ECG] Electrocardiogram
\item[SCADA] Supervisory Control and Data Acquisition
-\item[]
-\item[]
-\item[]
-\item[]
+\item[QoS] Quality of Service
+\item[DSC] Disjoint Set Covers
+\item[MIP] Mixed Integer Programming
+\item[LP] Linear Programming
+\item[GAS] Geometrically based Activity Scheduling
+\item[NCG] Node Coverage Grouping
+\item[CG] Column Generation
+\item[MLP] Maximum-network Lifetime Problem
+\item[RMP] Restricted Master Problem
+\item[PS] Pricing Subproblem
+\item[GRASP] Greedy Randomized Adaptive Search Procedure
+\item[VNS] Variable Neighborhood Search
+\item[CSB] Cover Sets Balance
+\item[CNSC] Correlated Node Set Computing
+\item[HREF] High Residual Energy First
+\item[SHM] Structural Health Monitoring
+\item[ESA] Effective Sensing Area
+\item[MSCR] Maximum Sensing Coverage Region
+\item[DASSA] Distributed Adaptive Sleep Scheduling Algorithm
+\item[DTGA] Distributed Truncated Greedy Algorithm
+\item[FIT] Future Internet of the Things
+\item[GUI] Graphical User Interface
+\item[NED] NEtwork Description
+\item[ns-2] Network Simulator-2
+\item[OPNET] Optimized Network Engineering tool
+\item[GloMoSim] Global Mobile System Simulator
+\item[SENSE] Sensor Network Simulator and Emulator
+\item[GTSNetS] Georgia Tech Sensor Network Simulator
+\item[GNU] GNU's Not Unix
+\item[GLPK] GNU Linear Programming Kit
+\item[MPS] Mathematical Programming System
+\item[COIN-OR] Linear Programming
+\item[BCP] Branch Cut and Price
+\item[CBC] COIN-OR Branch and Cut
+\item[OPL] Optimization Programming Language
+\item[QP] Quadratic Programming
+\item[QCP] Quadratically Constrained Programming
+\item[MILP] Mixed Integer Linear Programming
+\item[MIQP] Mixed-Integer Quadratic Programming
+\item[MIQCP] Mixed-Integer Quadratically Constrained Programming
+\item[AIMMS] Advanced Interactive Multidimensional Modeling System
+\item[AMPL] A Mathematical Programming Language
+\item[GAMS] General Algebraic Modeling System
+\item[MPL] Mathematical Programming Language
+\item[UAV] Unmanned Aerial Vehicle
+\item[WSNL] Wireless Sensor Node Leader
+\item[MCU] Microcontroller Unit
+\item[CR] Coverage Ratio
+\item[EC] Energy Consumption
+\item[ASR] Active Sensors Ratio
+\item[]
+
\end{abbreviations}
\ No newline at end of file
the WSN lifetime and provides improved coverage performance.
-\textbf{KEY WORDS:} Wireless Sensor Networks, Area Coverage, Network Lifetime, Optimization, Scheduling, Distributed Algorithms, Energy-efficiency.
+\textbf{KEY WORDS:} Wireless Networks, Wireless Sensor Networks, Area Coverage, Network Lifetime, Optimization, Scheduling, Distributed Algorithms, Centralized Algorithms, Robustness, Connectivity, Parallel Algorithms, Energy-efficiency, Heterogeneous Energy Network, Homogeneous Network.
\end{table}
-In this dissertation, the sensing field is divided into smaller subregions using divide-and-conquer method. The division continues until the distance between each two sensors inside the subregion is 3 or 2 hops maximum. This division makes our protocols more scalable for large networks, less energy consumer for communication, less processing power for decision, and more reliable against network failure. Our proposed protocols are distributed on the sensor nodes of the subregions. The protocols in each subregion work in independent and simultaneous way with the protocols in the other subregions. If the network disconnected in one subregion, it will not affect the other subregions of the sensing field. There is no a fixed sensor node in the subregion for executing the optimization algorithm. Each period of the network lifetime, the sensor nodes in the subregion cooperate in order to select a sensor node to execute the optimization algorithm according to a predefined priority metrics. The local optimal schedule resulted from the optimization algorithm is applied within the subregion. The elected sensor node sends a packet to every sensor within the subregion to inform him to stay active or sleep during this period. Each optimization algorithm in a subregion provides locally optimal solution, so the solution for all the sensing field is near-optimal.
+In this dissertation, the sensing field is divided into smaller subregions using divide-and-conquer method. The division continues until the distance between each two sensors inside the subregion is 3 or 2 hops maximum. This division makes our protocols more scalable for large networks, less energy consumer for communication, less processing power for decision, and more reliable against network failure. Our proposed protocols are distributed on the sensor nodes of the subregions. The protocols in each subregion work in independent and simultaneous way with the protocols in the other subregions. If the network is disconnected in one subregion, it will not affect the other subregions of the sensing field. There is no a fixed sensor node in the subregion for executing the optimization algorithm. Each period of the network lifetime, the sensor nodes in the subregion cooperate in order to select a sensor node to execute the optimization algorithm according to a predefined priority metrics. The resulted local optimal schedule of optimization algorithm is applied within the subregion. The elected sensor node sends a packet to every sensor within the subregion to inform him to stay active or sleep during this period. Each optimization algorithm in a subregion provides locally optimal solution, so the solution for all the sensing field is near-optimal.
-Several algorithms to retain the coverage and maximize the network lifetime were proposed in~\cite{ref113,ref101,ref103,ref105}. Table~\ref{Table1:ch2} summarized the main characteristics of some coverage approaches in previous literatures. In table~\ref{Table1:ch2}, the "SET K-COVER" characteristic refers to the maximum number of disjoint or non-disjoint sets of sensors such that each set cover can assure the coverage for the whole region. The K-COVER algorithm provides a solution with K cover sets in each execution. The k-coverage characteristic refers to that every point inside the monitored area is always covered by at least k active sensors.
+Several algorithms to retain the coverage and maximize the network lifetime were proposed in~\cite{ref113,ref101,ref103,ref105}. Table~\ref{Table1:ch2} summarizes the main characteristics of some coverage approaches in previous literatures. In table~\ref{Table1:ch2}, the "SET K-COVER" characteristic refers to the maximum number of disjoint or non-disjoint sets of sensors such that each set cover can assure the coverage for the whole region. The K-COVER algorithm provides a solution with K cover sets in each execution. The k-coverage characteristic refers to that every point inside the monitored area is always covered by at least k active sensors.
\label{ch2:sec:02}
The major idea of most centralized algorithms is to divide/organize the sensors into a suitable number of cover sets, where each set completely covers an interest region and to activate these cover sets successively. The centralized algorithms always provide optimal or near-optimal solution since the algorithm has a global view of the whole network. Energy-efficient centralized approaches differ according to several criteria \cite{ref113}, such as the coverage objective (target coverage or area coverage), the node deployment method (random or deterministic), and the heterogeneity of sensor nodes (common sensing range, common battery lifetime).
-The first algorithms proposed in the literature consider that the cover sets are disjoint: a sensor node appears in exactly one of the generated cover sets~\cite{ref114,ref115,ref116}. For instance, Slijepcevic and Potkonjak \cite{ref116} propose an algorithm, which allocates sensor nodes in mutually independent sets to monitor an area divided into several fields. Their algorithm builds a cover set by including in priority the sensor nodes, which cover critical fields, that is to say, fields that are covered by the smallest number of sensors. The time complexity of their heuristic is $O(n^2)$ where $n$ is the number of sensors. M. Cardei et al.~\cite{ref227}, suggest a graph coloring technique to achieve energy savings by organizing the sensor nodes into a maximum number of disjoint dominating sets, which are activated successively. They have defined the maximum disjoint dominating sets problem and they have produced a heuristic that computes the disjoint cover sets so as to monitor the area of interest. The dominating sets do not guarantee the coverage of the whole region of interest. Abrams et al.~\cite{ref114} design three approximation algorithms for a variation of the set k-cover problem, where the objective is to partition the sensors into covers such that the number of covers that include an area, summed over all areas, is maximized.
+The first algorithms proposed in the literature consider that the cover sets are disjoint: a sensor node appears in exactly one of the generated cover sets~\cite{ref114,ref115,ref116}. For instance, Slijepcevic and Potkonjak \cite{ref116} propose an algorithm, which allocates sensor nodes in mutually independent sets to monitor an area divided into several fields. Their algorithm builds a cover set by including in priority the sensor nodes, which cover critical fields, that is to say, fields that are covered by the smallest number of sensors. The time complexity of their heuristic is $O(n^2)$ where $n$ is the number of sensors. M. Cardei et al.~\cite{ref227}, suggest a graph coloring technique to achieve energy savings by organizing the sensor nodes into a maximum number of disjoint dominating sets, which are activated successively. They have defined the maximum disjoint dominating sets problem and they have produced a heuristic that computes the disjoint cover sets so as to monitor the area of interest. The dominating sets do not guarantee the coverage of the whole region of interest. Abrams et al.~\cite{ref114} design three approximation algorithms for a variation of the set k-cover problem, where the objective is to partition the sensors into covers such that the number of covers that include an area, summed over all areas, is maximized.
Their work builds upon previous work in~\cite{ref116} and the generated cover sets do not provide complete coverage of the monitoring zone.
-The authors in~\cite{ref115} propose a heuristic to compute the disjoint set covers (DSC). In order to compute the maximum number of covers, they first transform DSC into a maximum-flow problem, which is then formulated as a mixed integer programming problem (MIP). Based on the solution of the MIP, they design a heuristic to compute the final number of covers. The results show a slight performance improvement in terms of the number of produced DSC in comparison to~\cite{ref116}, but it incurs higher execution time due to the complexity of the mixed integer programming solving. Zorbas et al. \cite{ref228} present B\{GOP\}, a centralized target coverage algorithm introducing sensor candidate categorization depending on their coverage status and the notion of critical target to call targets that are associated with a small number of sensors. The total running time of their heuristic is $0(m n^2)$ where
-$n$ is the number of sensors and $m$ the number of targets. Compared to algorithm's results of Slijepcevic and Potkonjak \cite{ref116}, their heuristic produces more cover sets with a slight growth rate in execution time. L. Liu et al.~\cite{ref150} formulate the maximum disjoint sets for maintaining target coverage and connectivity problem in WSN. They propose a graph theoretical framework to study the joint problem of connectivity and coverage in a WSN with random deployment of nodes with no restrictions on the sensing and communication ranges of nodes. They propose heuristic algorithms to find the suitable number of nodes to guarantee connectivity and coverage while maximizing network lifetime.
+The authors in~\cite{ref115} propose a heuristic to compute the Disjoint Set Covers (DSC). In order to compute the maximum number of covers, they first transform DSC into a maximum-flow problem, which is then formulated as a Mixed Integer Programming problem (MIP). Based on the solution of the MIP, they design a heuristic to compute the final number of covers. The results show a slight performance improvement in terms of the number of produced DSC in comparison to~\cite{ref116}, but it incurs higher execution time due to the complexity of the mixed integer programming solving. Zorbas et al. \cite{ref228} present B\{GOP\}, a centralized target coverage algorithm introducing sensor candidate categorization depending on their coverage status and the notion of critical target to call targets that are associated with a small number of sensors. The total running time of their heuristic is $0(m n^2)$ where
+$n$ is the number of sensors and $m$ the number of targets. Compared to algorithm's results of Slijepcevic and Potkonjak \cite{ref116}, their heuristic produces more cover sets with a slight growth rate in execution time. L. Liu et al.~\cite{ref150} formulate the maximum disjoint sets for maintaining target coverage and connectivity problem in WSN. They propose a graph theoretical framework to study the joint problem of connectivity and coverage in a WSN with random deployment of nodes with no restrictions on the sensing and communication ranges of nodes. They propose heuristic algorithms to find the suitable number of nodes to guarantee connectivity and coverage while maximizing network lifetime.
%This work did not take into account the sensor node failure, which is an unpredictable event because the two solutions are full centralized algorithms.
-Y. Li et al.~\cite{ref142} present a framework with heuristic strategies to solve the area coverage problem. The framework converts any complete coverage problem to a partial coverage one with any coverage ratio. They execute a complete coverage algorithm to find a full coverage sets with virtual radii and transforming the coverage sets to a partial coverage sets by adjusting sensing radii . This framework has four strategies, two of them are designed for the network where the sensors have fixed sensing range and the other two are for the network where the sensors have adjustable sensing range. The properties of the algorithms can be maintained by this framework and the transformation process has a low execution time. The simulation results validate the efficiency of the four proposed strategies. More recently, Deschinkel and Hakem \cite{229} introduce a near-optimal heuristic algorithm for solving the target coverage problem in WSN. The sensor nodes are organized into disjoint cover sets by the resolution of an integer programming problem. Each cover set is capable of monitoring all the targets of the region of interest. Those covers sets are scheduled periodically. Their algorithm is able to construct the different cover sets in parallel. The results show that their algorithm achieves near-optimal solutions compared to the optimal ones obtained by the exact method.
+Y. Li et al.~\cite{ref142} present a framework with heuristic strategies to solve the area coverage problem. The framework converts any complete coverage problem to a partial coverage one with any coverage ratio. They execute a complete coverage algorithm to find full coverage sets with virtual radii and then transform to the coverage sets to a partial coverage sets by adjusting sensing radii . This framework has four strategies, two of them are designed for the network where the sensors have fixed sensing range and the other two are for the network where the sensors have adjustable sensing range. The properties of the algorithms can be maintained by this framework and the transformation process has a low execution time. The simulation results validate the efficiency of the four proposed strategies. More recently, Deschinkel and Hakem \cite{ref229} introduce a near-optimal heuristic algorithm for solving the target coverage problem in WSN. The sensor nodes are organized into disjoint cover sets, each capable of monitoring all the targets of the region of interest. %Those covers sets are scheduled periodically.
+Their algorithm is able to construct the different cover sets in parallel. The results show that their algorithm achieves near-optimal solutions compared to the optimal ones obtained by the resolution of an integer programming.
+%exact method.
In the case of non-disjoint algorithms~\cite{ref117}, sensors may participate in more than one cover set. In some cases, this may prolong the lifetime of the network in comparison to the disjoint cover set algorithms, but designing algorithms for non-disjoint cover sets generally induces a higher order of complexity. Moreover, in case of a sensor's failure, non-disjoint scheduling policies are less resilient and reliable because a sensor may be involved in more than one cover sets. For instance, Cardei et al.~\cite{ref167}
-present a linear programming (LP) solution and a greedy approach to
+present a Linear Programming (LP) solution and a greedy approach to
extend the sensor network lifetime by organizing the sensors into a
maximal number of non-disjoint cover sets. Simulation results show
that by allowing sensors to participate in multiple sets, the network
-lifetime increases compared with related work~\cite{ref115}. The authors in~\cite{ref148}, address the problem of minimum cost area coverage in which full coverage is performed by using the minimum number of sensors for an arbitrary geometric shape region. A geometric solution to the minimum cost coverage problem under a deterministic deployment is proposed. The probabilistic coverage solution which provides a relationship between the probability of coverage and the number of randomly deployed sensors in an arbitrarily-shaped region is suggested. The authors explained that with a random deployment about seven times more nodes are required to supply full coverage compared to deterministic deployment. The work in~\cite{ref144} address the area coverage problem by proposing a geometrically based activity scheduling scheme, named GAS, to fully cover the area of interest in WSNs. The authors deal with a small area, called target area coverage, which can be monitored by a single sensor instead of area coverage, which focuses on a large area that should be monitored by many sensors cooperatively. They explained that GAS is capable to monitor the target area by using a few sensors as possible and it can produce as many cover sets as possible. A novel area coverage method to divide the sensors called node coverage grouping (NCG) is suggested~\cite{ref147}. The sensors in the connectivity group are within sensing range of each other, and the data collected by them in the same group are supposed to be similar. They proved that dividing N sensors via NCG into connectivity groups is an NP-hard problem. So, a heuristic algorithm of NCG with time complexity of $O(n^3)$ is proposed.
-For some applications, such as monitoring an ecosystem with extremely diversified environment, it might be premature assumption that sensors near to each other sense similar data. The problem of k-coverage over the area of interest in WSNs was addressed in~\cite{ref152}. It is mathematically formulated and the spatial sensor density for full k-coverage is determined. The relation between the communication range and the sensing range is constructed by this work to retain the k-coverage and connectivity in WSN. After that, four configuration protocols are proposed for treating the k-coverage in WSNs. Simulation results show that their protocols outperform an existing distributed k-coverage configuration protocol. The work presented in~\cite{ref151} solves the area coverage and connectivity problem in sensor networks in an integrated way. The network lifetime is divided into a fixed number of rounds. A coverage bitmap of sensors of the domain is generated in each round and based on this bitmap, it is decided which sensors stay active or go to sleep. They checked the connection of the graph via laplacian of the adjacency graph of active sensors in each round. The generation of coverage bitmap by using Minkowski technique, the network is able to providing the desired ratio of coverage. They defined the connected coverage problem as an optimization problem and a centralized genetic algorithm is used to find the solution.
+lifetime increases compared with related work~\cite{ref115}. The authors in~\cite{ref148}, address the problem of minimum cost area coverage in which full coverage is performed by using the minimum number of sensors for an arbitrary geometric shape region. A geometric solution to the minimum cost coverage problem under a deterministic deployment is proposed. The probabilistic coverage solution which provides a relationship between the probability of coverage and the number of randomly deployed sensors in an arbitrarily-shaped region is suggested.
+%The authors explained that with a random deployment about seven times more nodes are required to supply full coverage compared to deterministic deployment.
+The work in~\cite{ref144} address the area coverage problem by proposing a Geometrically based Activity Scheduling scheme, named GAS, to fully cover the area of interest in WSNs. The authors deal with a small area, called target area coverage, which can be monitored by a single sensor instead of area coverage, which focuses on a large area that should be monitored by many sensors cooperatively. They explain that GAS is capable to monitor the target area by using a few sensors as possible and it can produce as many cover sets as possible. A novel area coverage method to divide the sensors called Node Coverage Grouping (NCG) is suggested~\cite{ref147}. The sensors in the connectivity group are within sensing range of each other, and the data collected by them in the same group are supposed to be similar. They prove that dividing N sensors via NCG into connectivity groups is an NP-hard problem. So, a heuristic algorithm of NCG with time complexity of $O(n^3)$ is proposed.
+For some applications, such as monitoring an ecosystem with extremely diversified environment, it might be premature assumption that sensors near to each other sense similar data. The problem of k-coverage over the area of interest in WSNs is addressed in~\cite{ref152}. It is mathematically formulated and the spatial sensor density for full k-coverage is determined. The relation between the communication range and the sensing range is constructed by this work to retain the k-coverage and connectivity in WSN. After that, four configuration protocols are proposed for treating the k-coverage in WSNs. Simulation results show that their protocols outperform an existing distributed k-coverage configuration protocol. The work presented in~\cite{ref151} solves the area coverage and connectivity problem in sensor networks in an integrated way. The network lifetime is divided into a fixed number of rounds. A coverage bitmap of sensors of the domain is generated in each round and based on this bitmap, it is decided which sensors stay active or go to sleep. They check the connection of the graph via laplacian of the adjacency graph of active sensors in each round. %The generation of coverage bitmap by using Minkowski technique, the network is able to providing the desired ratio of coverage.
+They define the connected coverage problem as an optimization problem and a centralized genetic algorithm is used to find the solution.
-Recent studies show an increasing interest in the use of exact schemes to solve optimization problems in WSN \cite{ref230,ref231,ref121,ref122,ref120}. Column Generation (CG) has been widely used to address different versions of maximum network lifetime problem (MLP). CG decomposes the problem into a restricted master problem (RMP) and a pricing subproblem (PS). The former maximizes lifetime using an incomplete set of columns, and the latter is used to identify new profitable columns. A. Rossi et al.~\cite{ref121} introduce an efficient implementation of a genetic algorithm based on CG to extend the lifetime and maximize target coverage in wireless sensor networks under bandwidth constraints. The authors show that the use of metaheuristic methods to solve PS in the context of CG allows to obtain optimal solutions quite fast and to produce high-quality solutions when the algorithm is stopped before returning an optimal solution. More recently, F. Castano et al. \cite{ref120} address the maximum network lifetime and the target coverage problem in WSNs with connectivity and coverage constraints. They consider two cases to schedule the activity of a set of sensor nodes, keeping them connected while network lifetime is maximized. First, the full coverage of the targets is required, and second only a fraction of the targets has to be monitored at any instant of time. They propose an exact approach based on column generation and boosted by a greedy randomized adaptive search procedure (GRASP) and a variable neighborhood search (VNS) to address both of these problems. Finally, a multiphase framework combining these two approaches is constructed by sequentially using these two heuristics at each iteration of the column generation algorithm. The results show that combining the two heuristic methods enhances the results significantly.
+Recent studies show an increasing interest in the use of exact schemes to solve optimization problems in WSN \cite{ref230,ref231,ref121,ref122,ref120}. Column Generation (CG) has been widely used to address different versions of Maximum-network Lifetime Problem (MLP). CG decomposes the problem into a Restricted Master Problem (RMP) and a Pricing Subproblem (PS). The former maximizes lifetime using an incomplete set of columns, and the latter is used to identify new profitable columns. A. Rossi et al.~\cite{ref121} introduce an efficient implementation of a genetic algorithm based on CG to extend the lifetime and maximize target coverage in wireless sensor networks under bandwidth constraints. The authors show that the use of metaheuristic methods to solve PS in the context of CG allows to obtain optimal solutions quite fast and to produce high-quality solutions when the algorithm is stopped before returning an optimal solution. More recently, F. Castano et al. \cite{ref120} address the maximum network lifetime and the target coverage problem in WSNs with connectivity and coverage constraints. They consider two cases to schedule the activity of a set of sensor nodes, keeping them connected while network lifetime is maximized. First, the full coverage of the targets is required, and second only a fraction of the targets has to be monitored at any instant of time. They propose an exact approach based on column generation and boosted by a Greedy Randomized Adaptive Search Procedure (GRASP) and a Variable Neighborhood Search (VNS) to address both of these problems. Finally, a multiphase framework combining these two approaches is constructed sequentially using these two heuristics at each iteration of the column generation algorithm. The results show that combining the two heuristic methods enhances the results significantly.
-More recently, the authors in~\cite{ref118}, consider an area coverage optimization algorithm based on linear programming approach to select the minimum number of working sensor nodes, in order to preserve a maximum coverage and to extend the lifetime of the network. The experimental results show that linear programming can provide a fewest number of active nodes and maximize the network lifetime coverage. M. Rebai et al.~\cite{ref141}, formulate the problem of full grid area coverage problem using two integer linear programming models: the first, a model that takes into account only the overall coverage constraint; the second, both the connectivity and the full grid coverage constraints are taken into consideration. This work does not take into account the energy constraint. H. Cheng et al.~\cite{ref119} define a heuristic area coverage algorithm called Cover Sets Balance (CSB), which chooses a set of active nodes using the tuple (data coverage range, residual energy). Then, they introduce a new Correlated Node Set Computing (CNSC) algorithm to find the correlated node set for a given node. After that, they propose a High Residual Energy First (HREF) node selection algorithm to minimize the number of active nodes so as to prolong the network lifetime. X. Liu et al.~\cite{ref143} explain that in some applications of WSNs such as structural health monitoring (SHM) and volcano monitoring, the traditional coverage model which is a geographic area defined for individual sensors is not always valid. For this reason, they define a generalized area coverage model, which is not required to have the coverage area of individual nodes, but only based on a function to determine whether a set of sensor nodes is capable of satisfy the requested monitoring task for a certain area. They propose two approaches for dividing the deployed nodes into suitable cover sets.
+More recently, the authors in~\cite{ref118}, consider an area coverage optimization algorithm based on linear programming approach to select the minimum number of working sensor nodes, in order to preserve a maximum coverage and to extend the lifetime of the network. The experimental results show that linear programming can provide a fewest number of active nodes and maximize the network lifetime coverage. M. Rebai et al.~\cite{ref141}, formulate the problem of full grid area coverage problem using two integer linear programming models: the first, a model that takes into account only the overall coverage constraint; the second, both the connectivity and the full grid coverage constraints are taken into consideration. This work does not take into account the energy constraint. H. Cheng et al.~\cite{ref119} define a heuristic area coverage algorithm called Cover Sets Balance (CSB), which chooses a set of active nodes using the tuple (data coverage range, residual energy). Then, they introduce a new Correlated Node Set Computing (CNSC) algorithm to find the correlated node set for a given node. After that, they propose a High Residual Energy First (HREF) node selection algorithm to minimize the number of active nodes so as to prolong the network lifetime. X. Liu et al.~\cite{ref143} explain that in some applications of WSNs such as Structural Health Monitoring (SHM) and volcano monitoring, the traditional coverage model which is a geographic area defined for individual sensors is not always valid. For this reason, they define a generalized area coverage model, which is not required to have the coverage area of individual nodes, but only based on a function to determine whether a set of sensor nodes is capable of satisfy the requested monitoring task for a certain area. They propose two approaches for dividing the deployed nodes into suitable cover sets.
Many distributed algorithms have been developed to perform the scheduling so as to preserve coverage, see for example \cite{ref123,ref124,ref125,ref126,ref109,ref127,ref128,ref97}. Localized and distributed algorithms generally result in non-disjoint set covers.
-X. Deng et al. \cite{ref133} formulate the area coverage problem as a decision problem, whose goal is to determine whether every point in the area of interest is monitored by at least k sensors. The authors prove that if the perimeters of sensors are sufficiently covered it will be the case for the whole area. They provide an algorithm in $O(nd~log~d)$ time to compute the perimeter coverage of each sensor, where $d$ denotes the maximum number of sensors that are neighboring to a sensor and $n$ is the total number of sensors in the network. Their solutions can be translated to distributed protocols to solve the coverage problem.
+X. Deng et al. \cite{ref133} formulate the area coverage problem as a decision problem, whose goal is to determine whether every point in the area of interest is monitored by at least k sensors. The authors prove that if the perimeters of sensors are sufficiently covered it will be the case for the whole area. They provide an algorithm in $O(nd~log~d)$ time to compute the perimeter coverage of each sensor, where $d$ denotes the maximum number of sensors that are neighboring to a sensor and $n$ is the total number of sensors in the network.
+%Their solutions can be translated to distributed protocols to solve the coverage problem.
Distributed algorithms typically operate in rounds for a predetermined duration. At the beginning of each round, a sensor exchanges information with its neighbors and makes a decision to either remain turned on or to go to sleep for the round. This decision is basically made on simple greedy criteria like the largest uncovered area \cite{ref130} or maximum uncovered targets \cite{ref131}.
-Cho et al.~\cite{ref145} propose a distributed node scheduling protocol, which can retain sensing coverage needed by applications and increase network lifetime via putting in sleep mode some redundant nodes. In this work, the effective sensing area (ESA) concept of a sensor node is used, which refers to the sensing area that is not overlapping with another sensor's sensing area. A sensor node can determine whether it will be active or turned off by computing its ESA. The suggested work permits sensor nodes to be in sleep mode opportunistically whilst fulfill the needed sensing coverage. The authors in~\cite{ref146}, define a maximum sensing coverage region problem (MSCR) in WSNs and then propose a distributed algorithm to solve it. The maximum observed area is fully covered by a minimum active sensors. In this work, the major property is to get rid of the redundant sensors in high-density WSNs and putting them in sleep mode, and choosing a smaller number of active sensors so as to ensure the full area is k-covered, and all events appearing in that area can be precisely and timely detected. This algorithm minimizes the total energy consumption and increases the network lifetime. The Distributed Adaptive Sleep Scheduling Algorithm (DASSA) \cite{ref127} does not require location information of sensors while maintaining connectivity and satisfying a user-defined coverage target. In DASSA, nodes use the residual energy levels and feedback from the sink for scheduling the activity of their neighbors. This feedback mechanism reduces the randomness in scheduling that would otherwise occur due to the absence of location information.
-A graph theoretical framework for connectivity-based area coverage with configurable coverage granularity was proposed~\cite{ref149}. A new coverage criterion and scheduling approach is proposed based on cycle partition. This method is capable of build a sparse coverage set in distributed way by means of only connectivity information. This work considers only the communication range of the sensor is smaller two times the sensing range of sensor. Shibo et al.~\cite{ref137} express that the area coverage problem as a minimum weight submodular set cover problem and propose a Distributed Truncated Greedy Algorithm (DTGA) to solve it. They take advantage from both temporal and spatial correlations between data sensed by different sensors, and leverage prediction, to improve the lifetime. The authors in \cite{ref160}, design an energy-efficient approach to area coverage problems by transforming the area coverage problem to the target coverage problem taking into account the intersection points among disks of sensors nodes or between disk of sensor nodes and boundaries. They proposed two mechanisms for the converted target coverage problems to produce cover sets covering the sensing
+Cho et al.~\cite{ref145} propose a distributed node scheduling protocol, which can retain sensing coverage needed by applications and increase network lifetime via putting in sleep mode some redundant nodes. In this work, the Effective Sensing Area (ESA) concept of a sensor node is used, which refers to the sensing area that is not overlapping with another sensor's sensing area. A sensor node can determine whether it will be active or turned off by computing its ESA. The suggested work permits sensor nodes to be in sleep mode opportunistically whilst fulfill the needed sensing coverage. The authors in~\cite{ref146}, define a Maximum Sensing Coverage Region problem (MSCR) in WSNs and then propose a distributed algorithm to solve it. The maximum observed area is fully covered by a minimum active sensors. In this work, the major property is to get rid of the redundant sensors in high-density WSNs and putting them in sleep mode, and choosing a smaller number of active sensors so as to ensure the full area is k-covered, and all events appearing in that area can be precisely and timely detected. This algorithm minimizes the total energy consumption and increases the network lifetime. The Distributed Adaptive Sleep Scheduling Algorithm (DASSA) \cite{ref127} does not require location information of sensors while maintaining connectivity and satisfying a user-defined coverage target. In DASSA, nodes use the residual energy levels and feedback from the sink for scheduling the activity of their neighbors. This feedback mechanism reduces the randomness in scheduling that would otherwise occur due to the absence of location information.
+A graph theoretical framework for connectivity-based area coverage with configurable coverage granularity is proposed~\cite{ref149}. A new coverage criterion and scheduling approach is proposed based on cycle partition. This method is capable of build a sparse coverage set in distributed way by means of only connectivity information. This work considers only that the communication range of the sensor is smaller two times the sensing range of sensor. Shibo et al.~\cite{ref137} express the area coverage problem as a minimum weight submodular set cover problem and propose a Distributed Truncated Greedy Algorithm (DTGA) to solve it. They take advantage from both temporal and spatial correlations between data sensed by different sensors, and leverage prediction, to improve the lifetime. The authors in \cite{ref160}, design an energy-efficient approach to area coverage problems by transforming the area coverage problem to the target coverage problem taking into account the intersection points among disks of sensors nodes or between disk of sensor nodes and boundaries. They propose two mechanisms for the converted target coverage problems to produce cover sets covering the sensing
field completely. Simulations results show that this approach can prolong the lifetime of the network compared with other works.
The works presented in~\cite{ref134,ref135,ref136} focus on coverage-aware, distributed energy-efficient, and distributed clustering methods respectively, which aim at extending the network lifetime, while the coverage is ensured.
-In this dissertation, we focused in more detail on two distributed coverage algorithms, GAF and DESK because we compared our proposed coverage optimization protocols with them during performance evaluation.
+In this dissertation, we focus in more details on two distributed coverage algorithms, GAF and DESK because we compared our proposed coverage optimization protocols with them during performance evaluation.
\subsection{GAF}
\subsection{DESK}
\label{ch2:sec:03:2}
-The authors in~\cite{DESK} design a novel distributed heuristic, called Distributed Energy-efficient Scheduling for k-coverage (DESK), which ensures that the energy consumption among the sensors is balanced and the lifetime maximized while the coverage requirement is satisfied. This heuristic works in rounds, requires only one-hop neighbor information, and each sensor decides its status (active or sleep) based on the perimeter coverage model from~\cite{ref133}.
+The authors in~\cite{DESK} design a novel distributed heuristic, called Distributed Energy-efficient Scheduling for K-coverage (DESK), which ensures that the energy consumption among the sensors is balanced and the lifetime maximized while the coverage requirement is satisfied. This heuristic works in rounds, requires only one-hop neighbor information, and each sensor decides its status (active or sleep) based on the perimeter coverage model from~\cite{ref133}.
-DESK is based on the result from \cite{ref133}. In \cite{ref133}, the whole area is k-covered if and only if the perimeters of all sensors are k-covered. The coverage level of perimeter of a sensor $s_i$ is determined by calculating the angle corresponding to the arc that each of its neighbors covers its perimeter. Figure~\ref{figp}~(a) illuminates such arcs whilst figure~\ref{figp}~(b) shows the angles corresponding with those arcs, which were posted into the range [0,2$ \pi $]. According to figure~\ref{figp}~(b), the coverage level of sensor $s_i$ can be calculated via traversing the range from 0 to 2$ \pi $.
+DESK is based on the result from \cite{ref133}. In \cite{ref133}, the whole area is K-covered if and only if the perimeters of all sensors are k-covered. The coverage level of perimeter of a sensor $s_i$ is determined by calculating the angle corresponding to the arc that each of its neighbors covers its perimeter. Figure~\ref{figp}~(a) illuminates such arcs whilst figure~\ref{figp}~(b) shows the angles corresponding with those arcs, which were posted into the range [0,2$ \pi $]. According to figure~\ref{figp}~(b), the coverage level of sensor $s_i$ can be calculated via traversing the range from 0 to 2$ \pi $.
\begin{figure}[h!]
\end{equation}
Where $\alpha, \beta,$ and $\eta$ are constant, z is a random number between [0; d], where d is a time slot, to avoid the case where two sensors having the same $w_i$ to be active at the same time. $l(e_i, r_i)$ is the function computing the lifetime of sensor $s_i$ in terms of its current remaining energy $e_i$ and its sensing range $r_i$.
-DESK uses two types of messages, mACTIVATE message by which a sensor informs others that it becomes active, and mASK2SLEEP by which a sensor suggests a neighbor to go to sleep due to its uselessness. The concept of uselessness or a redundant neighbor refers to one that does not contribute to the perimeter coverage of the considered sensor. This means that the segment of the perimeter of the considered sensor overlapping with that neighbor is already covered by active sensors.
+DESK uses two types of messages, mACTIVATE message by which a sensor informs others that it becomes active, and mASK2SLEEP by which a sensor suggests a neighbor to go to sleep due to its uselessness. The concept of uselessness or a redundant neighbor refers to one that does not contribute to the perimeter coverage of the considered sensor. This means that the segment of the perimeter of the considered sensor overlapping with that neighbor is already covered by active sensors.
The coverage problem is considered as 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 aim 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 lower 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 an 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.
+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 an hybrid approach to 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.
\item \textbf{IoT-LAB:}
IoT-LAB testbed~\cite{ref184,ref185} supplies a very large scale infrastructure service appropriate for evaluating wireless sensor devices and heterogeneous communicating objects. IoT-LAB includes more than 2700 wireless sensor nodes deployed in six different regions in France. Different kinds of wireless sensor nodes are available, with different processor architectures (MSP430, STM32, and Cortex-A8) and different wireless chips (802.15.4 PHY @ 800 MHz or 2.4 GHz). Sensor nodes are either mobile or fixed and can be used in different topologies throughout all the regions.
-IoT-LAB provides web-based reservation and tools for protocols and applications development, along with direct command-line access to the platform. Wireless sensor nodes firmware can be constructed from source and deployed on reserved nodes, application activity can be controlled and observed, power consumption or radio interference can be measured using the offered tools. IoT-LAB is a part of the FIT experimental platform, a set of supplementary elements that enable experimentation with innovative services for academic and industrial users.
+IoT-LAB provides web-based reservation and tools for protocols and applications development, along with direct command-line access to the platform. Wireless sensor nodes firmware can be constructed from source and deployed on reserved nodes, application activity can be controlled and observed, power consumption or radio interference can be measured using the offered tools. IoT-LAB is a part of the FIT (Future Internet of the Things) experimental platform, a set of supplementary elements that enable experimentation with innovative services for academic and industrial users.
\end{enumerate}
\item \textbf{NS2:}
-The Network Simulator-2 (ns-2)~\cite{ref191,ref192} is an open source, discrete event, network simulator. The major goal of ns-2 is to provide a simulation environment for wired as well as wireless networks to simulate different protocols with different network topologies. ns-2 is constructed using C++ and the simulation interface is provided via OTcl, an object-oriented dialect of Tcl. The network topology is determined by the users by writing OTcl scripts, and then the main program of ns-2 simulates this topology with fixed parameters. ns-2 provides a graphical view of the network by using network animator (NAM). NAM interface includes control features that allow researchers to forward, pause, stop, and control the simulation. ns-2 is the most common and widely used network simulator for scientific research work.
+The Network Simulator-2 (ns-2)~\cite{ref191,ref192} is an open source, discrete event, network simulator. The major goal of ns-2 is to provide a simulation environment for wired as well as wireless networks to simulate different protocols with different network topologies. ns-2 is constructed using C++ and the simulation interface is provided via OTcl, an object-oriented dialect of Tcl. The network topology is determined by the users by writing OTcl scripts, and then the main program of ns-2 simulates this topology with fixed parameters. ns-2 provides a graphical view of the network by using network animator (Nam). Nam interface includes control features that allow researchers to forward, pause, stop, and control the simulation. ns-2 is the most common and widely used network simulator for scientific research work.
The next version, ns-3, is considered as a new simulator and a final replacement of ns-2, not a simple extension~\cite{ref194}. The ns-3 project~\cite{ref193} was started in mid-2006 and is still under intensive development. Like ns-2, ns-3 is an open source, discrete-event network simulator targeted essentially for research and educational use~\cite{ref195}. ns-3 supports both simulation and emulation using sockets. It also provides a tracing facility to help users in debugging.
\item \textbf{OMNeT++:}
-OMNeT++ (Objective Modular Network Testbed) is an open-source, free, discrete-event, component-based C++ simulation library, modular simulation framework for building network simulators~\cite{ref158,ref203}. Even if OMNeT++ is not a network simulator itself, it is very popular as a network simulation platform for both scientific and industrial communities. The major goal behind the development of OMNeT++ is to provide a strong simulation tool, which can be used by the academic and commercial researchers for simulating different types of networks in a distributed and parallel way~\cite{ref197}. OMNeT++ has an extensive graphical user interface (GUI) and intelligence support. It runs on Windows, Linux, Mac OS~X, and other Unix-like systems, and provides a component architecture for models. Components (modules) are first programmed in C++, then assembled into larger components and models using a high-level language (NED)~\cite{ref198}. Several simulation frameworks can be used with OMNeT++ such as INET, INETMANET, MiXiM, and Castalia, where each of them provides a set of simulation facilities (modelity and soon) and can be used for specific applications.
+OMNeT++ (Objective Modular Network Testbed) is an open-source, free, discrete-event, component-based C++ simulation library, modular simulation framework for building network simulators~\cite{ref158,ref203}. Even if OMNeT++ is not a network simulator itself, it is very popular as a network simulation platform for both scientific and industrial communities. The major goal behind the development of OMNeT++ is to provide a strong simulation tool, which can be used by the academic and commercial researchers for simulating different types of networks in a distributed and parallel way~\cite{ref197}. OMNeT++ has an extensive Graphical User Interface (GUI) and intelligence support. It runs on Windows, Linux, Mac OS~X, and other Unix-like systems, and provides a component architecture for models. Components (modules) are first programmed in C++, then assembled into larger components and models using a high-level language (NED)~\cite{ref198}. Several simulation frameworks can be used with OMNeT++ such as INET, INETMANET, MiXiM, and Castalia, where each of them provides a set of simulation facilities (modelity and soon) and can be used for specific applications.
\item \textbf{OPNET:}
-OPNET (Optimized Network Engineering tool)~\cite{ref192,ref200,ref201} is the first commercial simulation tool developed in 1987 for communication networks. It is a discrete event, object-oriented, general purpose network simulator, which is widely used in industry. It uses C and Java languages. It provides a comprehensive development environment for the specification, simulation, configuration, and performance analysis of the communication network. OPNET allows researchers to develop various models by means of a graphical interface. It provides different types of tools such as Probe Editor, Filter Tool, and Animation Viewer for data collection to model graph and animate the resulting output. Unlike ns-2, OPNET provides modeling for different sensor-specific hardware, such as physical-link transceivers and antennas. It includes sensor-specific models such as ad-hoc connectivity, mobility of nodes, node failure models, modeling of power-consumption, etc. OPNET is, a commercial simulator and the license is very expensive. This represents the main disadvantage of that simulator.
+OPNET (Optimized Network Engineering tool)~\cite{ref192,ref200,ref201} is the first commercial simulation tool developed in 1987 for communication networks. It is a discrete event, object-oriented, general purpose network simulator, which is widely used in industry. It uses C and Java languages. It provides a comprehensive development environment for the specification, simulation, configuration, and performance analysis of the communication network. OPNET allows researchers to develop various models by means of a graphical interface. It provides different types of tools such as Probe Editor, Filter Tool, and Animation Viewer for data collection to model graph and animate the resulting output. Unlike ns-2, OPNET provides modeling for different sensor-specific hardware, such as physical-link transceivers and antennas. It includes sensor-specific models such as ad-hoc connectivity, mobility of nodes, node failure models, modeling of power-consumption, etc. OPNET is, a commercial simulator and the license is very expensive. This represents the main disadvantage of that simulator.
-\item \textbf{GloMoSim:}
+\item \textbf{GloMoSim:}
GloMoSim (Global Mobile System Simulator)~\cite{ref202,ref204,ref205} is an open source, well-documented source code and scalable simulation environment developed in 1998 for mobile wireless networks. It uses a library called Parsec, which is an extension of C for parallel programming. The main feature of GloMoSim simulator is the parallel environment. A parallel network simulation is hard due to the communication among the simulated nodes on different machines. Several types of protocols and models are found in GloMoSim including TCP,
IEEE 802.11 CSMA/CA, MAC, UDP, HTTP, FTP, CBR, ODMRP, WRP, DSR, MACA, Telnet, AODV, etc. It uses a VT visualization tool to observe and debug these protocols. The GloMoSim tool is designed to be extensible with all protocols implemented as modules in its library. It also uses an object-oriented approach.
\label{ch4}
-\iffalse
-\section{Summary}
-\label{ch4:sec:01}
-In this chapter, a Distributed Lifetime Coverage Optimization protocol (DiLCO) to maintain
-the coverage and to improve the lifetime in wireless sensor networks is
-proposed. The area of interest is first divided into subregions using a
-divide-and-conquer method and then the DiLCO protocol is distributed on the
-sensor nodes in each subregion. The DiLCO combines two efficient techniques:
-leader election for each subregion, followed by an optimization-based planning
-of activity scheduling decisions for each subregion. The proposed DiLCO works
-into rounds during which a small number of nodes, remaining active for sensing,
-is selected to ensure coverage so as to maximize the lifetime of wireless sensor
-network. Each round consists of four phases: (i)~Information Exchange,
-(ii)~Leader Election, (iii)~Decision, and (iv)~Sensing. The decision process is
-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}
\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
-Optimization protocol (MuDiLCO) is proposed to maintain the coverage and to improve the lifetime in wireless sensor networks. The area of interest is first
-divided into subregions and then the MuDiLCO protocol is distributed on the sensor nodes in each subregion. The proposed MuDiLCO protocol 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. 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}
\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
-25 runs.
+\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 25 runs.
%Based on the results of our proposed work in~\cite{idrees2014coverage}, we found as the region of interest are divided into larger subregions as the network lifetime increased. In this simulation, the network are divided into 16 subregions.
We performed simulations for five different densities varying from 50 to
250~nodes deployed over a $50 \times 25~m^2 $ sensing field. More
\chapter{Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks}
\label{ch6}
-\iffalse
-
-\section{Summary}
-\label{ch6:sec:01}
-
-The most important problem in a Wireless Sensor Network (WSN) is to optimize the
-use of its limited energy provision so that it can fulfill its monitoring task
-as long as possible. Among known available approaches that can be used to
-improve power management, lifetime coverage optimization provides activity
-scheduling which ensures sensing coverage while minimizing the energy cost. In
-this paper, we propose such an approach called Perimeter-based Coverage Optimization
-protocol (PeCO). It is a hybrid of centralized and distributed methods: the
-region of interest is first subdivided into subregions and our protocol is then
-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. Extensive simulation experiments have been performed using
-OMNeT++, the discrete event simulator, to demonstrate that PeCO can
-offer longer lifetime coverage for WSNs in comparison with some other protocols.
-
-
-\fi
-
\section{Introduction}
\label{ch6:sec:01}
The values of $\alpha^j_i$ and $\beta^j_i$ have been chosen to ensure a good
network coverage and a longer WSN lifetime. We have given a higher priority to
-the undercoverage (by setting the $\alpha^j_i$ with a larger value than
+the undercoverage (by setting the $\alpha^j_i$ with a larger value than
$\beta^j_i$) so as to prevent the non-coverage for the interval~$i$ of the
sensor~$j$. On the other hand, we have assigned to
$\beta^j_i$ a value which is slightly lower so as to minimize the number of active sensor nodes which contribute
\contentsline {chapter}{List of Algorithms}{9}{chapter*.4}
\contentsline {chapter}{List of Acronyms}{9}{chapter*.4}
\contentsline {chapter}{abbreviations}{11}{chapter*.5}
-\contentsline {chapter}{Abstract}{13}{chapter*.6}
-\contentsline {chapter}{Introduction }{15}{chapter*.7}
-\contentsline {section}{1. General Introduction }{15}{section*.8}
-\contentsline {section}{2. Motivation of the Dissertation }{16}{section*.9}
-\contentsline {section}{3. The Objective of this Dissertation}{16}{section*.10}
-\contentsline {section}{4. Main Contributions of this Dissertation}{16}{section*.11}
-\contentsline {section}{5. Dissertation Outline}{18}{section*.12}
-\contentsline {part}{I\hspace {1em}Scientific Background}{19}{part.1}
-\contentsline {chapter}{\numberline {1}Wireless Sensor Networks}{21}{chapter.1}
-\contentsline {section}{\numberline {1.1}Introduction}{21}{section.1.1}
-\contentsline {section}{\numberline {1.2}Architecture}{22}{section.1.2}
-\contentsline {section}{\numberline {1.3}Types of Wireless Sensor Networks}{24}{section.1.3}
-\contentsline {section}{\numberline {1.4}Applications}{26}{section.1.4}
-\contentsline {section}{\numberline {1.5}The Main Challenges}{29}{section.1.5}
-\contentsline {section}{\numberline {1.6}Energy-Efficient Mechanisms of a working WSN}{31}{section.1.6}
-\contentsline {subsection}{\numberline {1.6.1}Energy-Efficient Routing}{31}{subsection.1.6.1}
-\contentsline {subsubsection}{\numberline {1.6.1.1}Routing Metric based on Residual Energy}{31}{subsubsection.1.6.1.1}
-\contentsline {subsubsection}{\numberline {1.6.1.2}Multipath Routing}{31}{subsubsection.1.6.1.2}
-\contentsline {subsection}{\numberline {1.6.2}Cluster Architecture}{32}{subsection.1.6.2}
-\contentsline {subsection}{\numberline {1.6.3}Scheduling Schemes}{32}{subsection.1.6.3}
-\contentsline {subsubsection}{\numberline {1.6.3.1}Wake up Scheduling Schemes}{32}{subsubsection.1.6.3.1}
-\contentsline {subsubsection}{\numberline {1.6.3.2}Topology Control Schemes}{35}{subsubsection.1.6.3.2}
-\contentsline {subsection}{\numberline {1.6.4}Data-Driven Schemes}{35}{subsection.1.6.4}
-\contentsline {subsubsection}{\numberline {1.6.4.1}Data Reduction Schemes}{36}{subsubsection.1.6.4.1}
-\contentsline {subsubsection}{\numberline {1.6.4.2}Energy Efficient Data Acquisition Schemes}{36}{subsubsection.1.6.4.2}
-\contentsline {subsection}{\numberline {1.6.5}Battery Repletion}{36}{subsection.1.6.5}
-\contentsline {subsection}{\numberline {1.6.6}Radio Optimization}{36}{subsection.1.6.6}
-\contentsline {subsection}{\numberline {1.6.7}Relay nodes and Sink Mobility}{37}{subsection.1.6.7}
-\contentsline {subsubsection}{\numberline {1.6.7.1}Relay node placement}{37}{subsubsection.1.6.7.1}
-\contentsline {subsubsection}{\numberline {1.6.7.2}Sink Mobility}{37}{subsubsection.1.6.7.2}
-\contentsline {section}{\numberline {1.7}Network Lifetime}{37}{section.1.7}
-\contentsline {section}{\numberline {1.8}Coverage in Wireless Sensor Networks }{38}{section.1.8}
-\contentsline {section}{\numberline {1.9}Design Issues for Coverage Problems}{40}{section.1.9}
-\contentsline {section}{\numberline {1.10}Energy Consumption Model}{41}{section.1.10}
-\contentsline {section}{\numberline {1.11}Conclusion}{42}{section.1.11}
-\contentsline {chapter}{\numberline {2}Related Works on Coverage Problems}{43}{chapter.2}
-\contentsline {section}{\numberline {2.1}Introduction}{43}{section.2.1}
-\contentsline {section}{\numberline {2.2}Centralized Algorithms}{45}{section.2.2}
-\contentsline {section}{\numberline {2.3}Distributed Algorithms}{48}{section.2.3}
-\contentsline {subsection}{\numberline {2.3.1}GAF}{50}{subsection.2.3.1}
-\contentsline {subsection}{\numberline {2.3.2}DESK}{52}{subsection.2.3.2}
-\contentsline {section}{\numberline {2.4}Conclusion}{54}{section.2.4}
-\contentsline {chapter}{\numberline {3}Evaluation Tools and Optimization Solvers}{57}{chapter.3}
-\contentsline {section}{\numberline {3.1}Introduction}{57}{section.3.1}
-\contentsline {section}{\numberline {3.2}Evaluation Tools}{57}{section.3.2}
-\contentsline {subsection}{\numberline {3.2.1}Testbed Tools}{58}{subsection.3.2.1}
-\contentsline {subsection}{\numberline {3.2.2}Simulation Tools}{59}{subsection.3.2.2}
-\contentsline {section}{\numberline {3.3}Optimization Solvers}{64}{section.3.3}
-\contentsline {section}{\numberline {3.4}Conclusion}{67}{section.3.4}
-\contentsline {part}{II\hspace {1em}Contributions}{69}{part.2}
-\contentsline {chapter}{\numberline {4}Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}{71}{chapter.4}
-\contentsline {section}{\numberline {4.1}Introduction}{71}{section.4.1}
-\contentsline {section}{\numberline {4.2}Description of the DiLCO Protocol}{72}{section.4.2}
-\contentsline {subsection}{\numberline {4.2.1}Assumptions and Network Model}{72}{subsection.4.2.1}
-\contentsline {subsection}{\numberline {4.2.2}Primary Point Coverage Model}{73}{subsection.4.2.2}
-\contentsline {subsection}{\numberline {4.2.3}Main Idea}{74}{subsection.4.2.3}
-\contentsline {subsubsection}{\numberline {4.2.3.1}Information Exchange Phase}{75}{subsubsection.4.2.3.1}
-\contentsline {subsubsection}{\numberline {4.2.3.2}Leader Election Phase}{75}{subsubsection.4.2.3.2}
-\contentsline {subsubsection}{\numberline {4.2.3.3}Decision phase}{75}{subsubsection.4.2.3.3}
-\contentsline {subsubsection}{\numberline {4.2.3.4}Sensing phase}{75}{subsubsection.4.2.3.4}
-\contentsline {section}{\numberline {4.3}Primary Points based Coverage Problem Formulation}{76}{section.4.3}
-\contentsline {section}{\numberline {4.4}Simulation Results and Analysis}{78}{section.4.4}
-\contentsline {subsection}{\numberline {4.4.1}Simulation Framework}{78}{subsection.4.4.1}
-\contentsline {subsection}{\numberline {4.4.2}Modeling Language and Optimization Solver}{78}{subsection.4.4.2}
-\contentsline {subsection}{\numberline {4.4.3}Energy Consumption Model}{78}{subsection.4.4.3}
-\contentsline {subsection}{\numberline {4.4.4}Performance Metrics}{79}{subsection.4.4.4}
-\contentsline {subsection}{\numberline {4.4.5}Performance Analysis for Different Subregions}{80}{subsection.4.4.5}
-\contentsline {subsection}{\numberline {4.4.6}Performance Analysis for Primary Point Models}{86}{subsection.4.4.6}
-\contentsline {subsection}{\numberline {4.4.7}Performance Comparison with other Approaches}{91}{subsection.4.4.7}
-\contentsline {section}{\numberline {4.5}Conclusion}{97}{section.4.5}
-\contentsline {chapter}{\numberline {5}Multiround Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}{99}{chapter.5}
-\contentsline {section}{\numberline {5.1}Introduction}{99}{section.5.1}
-\contentsline {section}{\numberline {5.2}MuDiLCO Protocol Description}{100}{section.5.2}
-\contentsline {subsection}{\numberline {5.2.1}Background Idea and Algorithm}{100}{subsection.5.2.1}
-\contentsline {section}{\numberline {5.3}Primary Points based Multiround Coverage Problem Formulation}{101}{section.5.3}
-\contentsline {section}{\numberline {5.4}Experimental Study and Analysis}{103}{section.5.4}
-\contentsline {subsection}{\numberline {5.4.1}Simulation Setup}{103}{subsection.5.4.1}
-\contentsline {subsection}{\numberline {5.4.2}Metrics}{104}{subsection.5.4.2}
-\contentsline {subsection}{\numberline {5.4.3}Results Analysis and Comparison }{105}{subsection.5.4.3}
-\contentsline {section}{\numberline {5.5}Conclusion}{110}{section.5.5}
-\contentsline {chapter}{\numberline {6}Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks}{113}{chapter.6}
-\contentsline {section}{\numberline {6.1}Introduction}{113}{section.6.1}
-\contentsline {section}{\numberline {6.2}The PeCO Protocol Description}{114}{section.6.2}
-\contentsline {subsection}{\numberline {6.2.1}Assumptions and Models}{114}{subsection.6.2.1}
-\contentsline {subsection}{\numberline {6.2.2}The Main Idea}{117}{subsection.6.2.2}
-\contentsline {subsection}{\numberline {6.2.3}PeCO Protocol Algorithm}{117}{subsection.6.2.3}
-\contentsline {section}{\numberline {6.3}Perimeter-based Coverage Problem Formulation}{118}{section.6.3}
-\contentsline {section}{\numberline {6.4}Performance Evaluation and Analysis}{120}{section.6.4}
-\contentsline {subsection}{\numberline {6.4.1}Simulation Settings}{120}{subsection.6.4.1}
-\contentsline {subsection}{\numberline {6.4.2}Simulation Results}{121}{subsection.6.4.2}
-\contentsline {subsubsection}{\numberline {6.4.2.1}Coverage Ratio}{122}{subsubsection.6.4.2.1}
-\contentsline {subsubsection}{\numberline {6.4.2.2}Active Sensors Ratio}{122}{subsubsection.6.4.2.2}
-\contentsline {subsubsection}{\numberline {6.4.2.3}The Energy Consumption}{123}{subsubsection.6.4.2.3}
-\contentsline {subsubsection}{\numberline {6.4.2.4}The Network Lifetime}{123}{subsubsection.6.4.2.4}
-\contentsline {section}{\numberline {6.5}Conclusion}{126}{section.6.5}
-\contentsline {part}{III\hspace {1em}Conclusion and Perspectives}{127}{part.3}
-\contentsline {chapter}{\numberline {7}Conclusion and Perspectives}{129}{chapter.7}
-\contentsline {section}{\numberline {7.1}Conclusion}{129}{section.7.1}
-\contentsline {section}{\numberline {7.2}Perspectives}{130}{section.7.2}
-\contentsline {part}{Bibliographie}{146}{chapter*.13}
+\contentsline {chapter}{Abstract}{15}{chapter*.6}
+\contentsline {chapter}{Introduction }{17}{chapter*.7}
+\contentsline {section}{1. General Introduction }{17}{section*.8}
+\contentsline {section}{2. Motivation of the Dissertation }{18}{section*.9}
+\contentsline {section}{3. The Objective of this Dissertation}{18}{section*.10}
+\contentsline {section}{4. Main Contributions of this Dissertation}{18}{section*.11}
+\contentsline {section}{5. Dissertation Outline}{20}{section*.12}
+\contentsline {part}{I\hspace {1em}Scientific Background}{21}{part.1}
+\contentsline {chapter}{\numberline {1}Wireless Sensor Networks}{23}{chapter.1}
+\contentsline {section}{\numberline {1.1}Introduction}{23}{section.1.1}
+\contentsline {section}{\numberline {1.2}Architecture}{24}{section.1.2}
+\contentsline {section}{\numberline {1.3}Types of Wireless Sensor Networks}{26}{section.1.3}
+\contentsline {section}{\numberline {1.4}Applications}{28}{section.1.4}
+\contentsline {section}{\numberline {1.5}The Main Challenges}{31}{section.1.5}
+\contentsline {section}{\numberline {1.6}Energy-Efficient Mechanisms of a working WSN}{33}{section.1.6}
+\contentsline {subsection}{\numberline {1.6.1}Energy-Efficient Routing}{33}{subsection.1.6.1}
+\contentsline {subsubsection}{\numberline {1.6.1.1}Routing Metric based on Residual Energy}{33}{subsubsection.1.6.1.1}
+\contentsline {subsubsection}{\numberline {1.6.1.2}Multipath Routing}{33}{subsubsection.1.6.1.2}
+\contentsline {subsection}{\numberline {1.6.2}Cluster Architecture}{34}{subsection.1.6.2}
+\contentsline {subsection}{\numberline {1.6.3}Scheduling Schemes}{34}{subsection.1.6.3}
+\contentsline {subsubsection}{\numberline {1.6.3.1}Wake up Scheduling Schemes}{34}{subsubsection.1.6.3.1}
+\contentsline {subsubsection}{\numberline {1.6.3.2}Topology Control Schemes}{37}{subsubsection.1.6.3.2}
+\contentsline {subsection}{\numberline {1.6.4}Data-Driven Schemes}{37}{subsection.1.6.4}
+\contentsline {subsubsection}{\numberline {1.6.4.1}Data Reduction Schemes}{38}{subsubsection.1.6.4.1}
+\contentsline {subsubsection}{\numberline {1.6.4.2}Energy Efficient Data Acquisition Schemes}{38}{subsubsection.1.6.4.2}
+\contentsline {subsection}{\numberline {1.6.5}Battery Repletion}{38}{subsection.1.6.5}
+\contentsline {subsection}{\numberline {1.6.6}Radio Optimization}{38}{subsection.1.6.6}
+\contentsline {subsection}{\numberline {1.6.7}Relay nodes and Sink Mobility}{39}{subsection.1.6.7}
+\contentsline {subsubsection}{\numberline {1.6.7.1}Relay node placement}{39}{subsubsection.1.6.7.1}
+\contentsline {subsubsection}{\numberline {1.6.7.2}Sink Mobility}{39}{subsubsection.1.6.7.2}
+\contentsline {section}{\numberline {1.7}Network Lifetime}{39}{section.1.7}
+\contentsline {section}{\numberline {1.8}Coverage in Wireless Sensor Networks }{40}{section.1.8}
+\contentsline {section}{\numberline {1.9}Design Issues for Coverage Problems}{42}{section.1.9}
+\contentsline {section}{\numberline {1.10}Energy Consumption Model}{43}{section.1.10}
+\contentsline {section}{\numberline {1.11}Conclusion}{44}{section.1.11}
+\contentsline {chapter}{\numberline {2}Related Works on Coverage Problems}{45}{chapter.2}
+\contentsline {section}{\numberline {2.1}Introduction}{45}{section.2.1}
+\contentsline {section}{\numberline {2.2}Centralized Algorithms}{47}{section.2.2}
+\contentsline {section}{\numberline {2.3}Distributed Algorithms}{50}{section.2.3}
+\contentsline {subsection}{\numberline {2.3.1}GAF}{52}{subsection.2.3.1}
+\contentsline {subsection}{\numberline {2.3.2}DESK}{53}{subsection.2.3.2}
+\contentsline {section}{\numberline {2.4}Conclusion}{56}{section.2.4}
+\contentsline {chapter}{\numberline {3}Evaluation Tools and Optimization Solvers}{59}{chapter.3}
+\contentsline {section}{\numberline {3.1}Introduction}{59}{section.3.1}
+\contentsline {section}{\numberline {3.2}Evaluation Tools}{59}{section.3.2}
+\contentsline {subsection}{\numberline {3.2.1}Testbed Tools}{60}{subsection.3.2.1}
+\contentsline {subsection}{\numberline {3.2.2}Simulation Tools}{61}{subsection.3.2.2}
+\contentsline {section}{\numberline {3.3}Optimization Solvers}{66}{section.3.3}
+\contentsline {section}{\numberline {3.4}Conclusion}{69}{section.3.4}
+\contentsline {part}{II\hspace {1em}Contributions}{71}{part.2}
+\contentsline {chapter}{\numberline {4}Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}{73}{chapter.4}
+\contentsline {section}{\numberline {4.1}Introduction}{73}{section.4.1}
+\contentsline {section}{\numberline {4.2}Description of the DiLCO Protocol}{74}{section.4.2}
+\contentsline {subsection}{\numberline {4.2.1}Assumptions and Network Model}{74}{subsection.4.2.1}
+\contentsline {subsection}{\numberline {4.2.2}Primary Point Coverage Model}{75}{subsection.4.2.2}
+\contentsline {subsection}{\numberline {4.2.3}Main Idea}{76}{subsection.4.2.3}
+\contentsline {subsubsection}{\numberline {4.2.3.1}Information Exchange Phase}{77}{subsubsection.4.2.3.1}
+\contentsline {subsubsection}{\numberline {4.2.3.2}Leader Election Phase}{77}{subsubsection.4.2.3.2}
+\contentsline {subsubsection}{\numberline {4.2.3.3}Decision phase}{77}{subsubsection.4.2.3.3}
+\contentsline {subsubsection}{\numberline {4.2.3.4}Sensing phase}{77}{subsubsection.4.2.3.4}
+\contentsline {section}{\numberline {4.3}Primary Points based Coverage Problem Formulation}{78}{section.4.3}
+\contentsline {section}{\numberline {4.4}Simulation Results and Analysis}{80}{section.4.4}
+\contentsline {subsection}{\numberline {4.4.1}Simulation Framework}{80}{subsection.4.4.1}
+\contentsline {subsection}{\numberline {4.4.2}Modeling Language and Optimization Solver}{80}{subsection.4.4.2}
+\contentsline {subsection}{\numberline {4.4.3}Energy Consumption Model}{80}{subsection.4.4.3}
+\contentsline {subsection}{\numberline {4.4.4}Performance Metrics}{81}{subsection.4.4.4}
+\contentsline {subsection}{\numberline {4.4.5}Performance Analysis for Different Subregions}{82}{subsection.4.4.5}
+\contentsline {subsection}{\numberline {4.4.6}Performance Analysis for Primary Point Models}{88}{subsection.4.4.6}
+\contentsline {subsection}{\numberline {4.4.7}Performance Comparison with other Approaches}{93}{subsection.4.4.7}
+\contentsline {section}{\numberline {4.5}Conclusion}{99}{section.4.5}
+\contentsline {chapter}{\numberline {5}Multiround Distributed Lifetime Coverage Optimization Protocol in Wireless Sensor Networks}{101}{chapter.5}
+\contentsline {section}{\numberline {5.1}Introduction}{101}{section.5.1}
+\contentsline {section}{\numberline {5.2}MuDiLCO Protocol Description}{102}{section.5.2}
+\contentsline {subsection}{\numberline {5.2.1}Background Idea and Algorithm}{102}{subsection.5.2.1}
+\contentsline {section}{\numberline {5.3}Primary Points based Multiround Coverage Problem Formulation}{103}{section.5.3}
+\contentsline {section}{\numberline {5.4}Experimental Study and Analysis}{105}{section.5.4}
+\contentsline {subsection}{\numberline {5.4.1}Simulation Setup}{105}{subsection.5.4.1}
+\contentsline {subsection}{\numberline {5.4.2}Metrics}{106}{subsection.5.4.2}
+\contentsline {subsection}{\numberline {5.4.3}Results Analysis and Comparison }{107}{subsection.5.4.3}
+\contentsline {section}{\numberline {5.5}Conclusion}{112}{section.5.5}
+\contentsline {chapter}{\numberline {6}Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks}{115}{chapter.6}
+\contentsline {section}{\numberline {6.1}Introduction}{115}{section.6.1}
+\contentsline {section}{\numberline {6.2}The PeCO Protocol Description}{116}{section.6.2}
+\contentsline {subsection}{\numberline {6.2.1}Assumptions and Models}{116}{subsection.6.2.1}
+\contentsline {subsection}{\numberline {6.2.2}The Main Idea}{119}{subsection.6.2.2}
+\contentsline {subsection}{\numberline {6.2.3}PeCO Protocol Algorithm}{119}{subsection.6.2.3}
+\contentsline {section}{\numberline {6.3}Perimeter-based Coverage Problem Formulation}{120}{section.6.3}
+\contentsline {section}{\numberline {6.4}Performance Evaluation and Analysis}{122}{section.6.4}
+\contentsline {subsection}{\numberline {6.4.1}Simulation Settings}{122}{subsection.6.4.1}
+\contentsline {subsection}{\numberline {6.4.2}Simulation Results}{123}{subsection.6.4.2}
+\contentsline {subsubsection}{\numberline {6.4.2.1}Coverage Ratio}{124}{subsubsection.6.4.2.1}
+\contentsline {subsubsection}{\numberline {6.4.2.2}Active Sensors Ratio}{124}{subsubsection.6.4.2.2}
+\contentsline {subsubsection}{\numberline {6.4.2.3}The Energy Consumption}{125}{subsubsection.6.4.2.3}
+\contentsline {subsubsection}{\numberline {6.4.2.4}The Network Lifetime}{125}{subsubsection.6.4.2.4}
+\contentsline {section}{\numberline {6.5}Conclusion}{128}{section.6.5}
+\contentsline {part}{III\hspace {1em}Conclusion and Perspectives}{129}{part.3}
+\contentsline {chapter}{\numberline {7}Conclusion and Perspectives}{131}{chapter.7}
+\contentsline {section}{\numberline {7.1}Conclusion}{131}{section.7.1}
+\contentsline {section}{\numberline {7.2}Perspectives}{132}{section.7.2}
+\contentsline {part}{Bibliographie}{148}{chapter*.13}
