Lhassane Idoumghar
Université de Haute-Alsace (UHA), Computer and science, Faculty Member
- Full Professor, First-Class, in computer scienceedit
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This paper provides a comparison study of the quality services of RPL protocols in low-power and lossy net- works (LLN). We evaluate and compare our proposed protocol which is an extension of RPL based on Operator Calculus (OC), called... more
This paper provides a comparison study of the quality services of RPL protocols in low-power and lossy net- works (LLN). We evaluate and compare our proposed protocol which is an extension of RPL based on Operator Calculus (OC), called RPL-OC, with the standard and other RPL variants. OC based approach is applied to extract the feasible end-to-end paths while assigning a rank to each one. The goal is to provide a tuple that containing the most efficient paths in end-to-end manner by considering more network metrics instead of one. Further, to address some significant issues of the performance analysis, a statistical test has been performed in order to determine whether the proposed protocol outperforms others or not by using Friedman test. The results show that there is a strong indication that four different protocols were analyzed and compared. It reveals that the proposed scheme outperforms others, especially in terms of end-to-end delay and energy consumption which allow ensuring quality of services requirements for Internet of Things (IoT) or smart city applications.
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Today, diseases and illnesses are becoming the most dangerous enemy to humans. The number of patients is increasing day after day accompanied with the emergence of new types of viruses and diseases. Indeed, most hospitals suffer from the... more
Today, diseases and illnesses are becoming the most dangerous enemy to humans. The number of patients is increasing day after day accompanied with the emergence of new types of viruses and diseases. Indeed, most hospitals suffer from the deficiency of qualified staff needed to continuously monitor patients and act when an urgent situation is detected. Recently, wireless body sensor network (WBSN) has been considered as an efficient technology for real-time health-monitoring applications. It provides a low cost solution for hospitals, performs a relief for staff and allows doctors to remotely track patients. However, the huge amount of data collected by sensors produce two major challenges for WBSN: the quickly depletion of the available sensor energy and the complex decision making by the doctor. In this article, we propose an efficient Patient-to-Doctor (P2D) framework for real-time health monitoring and decision making. P2D works on two levels: sensors and coordinator. At the sensor level, P2D allows to save the sensor energy, by adapting its sensing frequency, and to directly detect any abnormal situation of the patient. Whilst, at the coordinator level, P2D allows to store an archive for each patient, predict the patient situation during the next periods of time and make a suitable decision by the doctors. We conducted a set of simulations on real health data in order to show the relevance of our platforms compared to other existing systems.
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The rapid growing of Internet of things (IoT) is one of the most important factors of integrating WSN in smart buildings. Furthermore, the presence of these sensors and technologies allow improving the accuracy of the measured data while... more
The rapid growing of Internet of things (IoT) is one of the most important factors of integrating WSN in smart buildings. Furthermore, the presence of these sensors and technologies allow improving the accuracy of the measured data while reducing buildings energy consumption and guaranteeing the comfort required by the indoor users. However, finding the optimal sensor nodes positions in an indoor environment with heterogeneous obstacles is the keystone to ensure a full sensing coverage with a full connectivity. To meet this challenge, various initiatives have been proposed in the literature. First, we summarize the main developed solutions to optimize WSN deployment in an indoor/outdoor environment. Then, we propose our conceptual approach that relies on exploiting BIM (Building information modeling) database to get real time and valid information about the target area. Indeed, the proposed solution can be integrated within BIM tools as a plugin in order to optimize sensors deployment in real time by taking into account nodes and obstacles heterogeneity at the same time.
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The works on Search Based Software Engineering (SBSE) have been a big increase in the last decade. An approach to software engineering in which search based optimisation algorithms are applied to address problems in software engineering.... more
The works on Search Based Software Engineering (SBSE) have been a big increase in the last decade. An approach to software engineering in which search based optimisation algorithms are applied to address problems in software engineering. SBSE has been applied to problems throughout the software engineering lifecycle, from requirements and project planning to maintenance and re-engineering. This paper provides a modification and an implementation of SBSE on evolutionary multi-objective based approach for deployment of wireless sensor network (WSN) with the presence of fixed obstacle. In this work a multi-objective evolutionary algorithms based on elitist non-dominated sorting genetic algorithm (NSGA-II) is proposed to address the deployment problem. Two functions namely ranking function and fitness function are used to select the best optimal solution from Pareto optimal fronts.
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This paper presents a multi-objective mixed-integer non-linear programming model for a congested multiple-server discrete facility location problem with uniformly distributed demands along the network edges. Regarding the capacity of each... more
This paper presents a multi-objective mixed-integer non-linear programming model for a congested multiple-server discrete facility location problem with uniformly distributed demands along the network edges. Regarding the capacity of each facility and the maximum waiting time threshold, the developed model aims to determine the number and locations of established facilities along with their corresponding number of assigned servers such that the traveling distance, the waiting time, the total cost, and the number of lost sales (uncovered customers) are minimized simultaneously. Also, this paper proposes modified versions of some of the existing heuristics and metaheuristic algorithms currently used to solve NP-hard location problems. Here, the memetic algorithm along with its modified version called the stochastic memetic algorithm, as well as the modified add and modified drop heuristics are used as the solution methods. Computational results and comparisons demonstrate that althoug...
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This paper provides a comparison study of the quality services of RPL protocols in low-power and lossy net- works (LLN). We evaluate and compare our proposed protocol which is an extension of RPL based on Operator Calculus (OC), called... more
This paper provides a comparison study of the quality services of RPL protocols in low-power and lossy net- works (LLN). We evaluate and compare our proposed protocol which is an extension of RPL based on Operator Calculus (OC), called RPL-OC, with the standard and other RPL variants. OC based approach is applied to extract the feasible end-to-end paths while assigning a rank to each one. The goal is to provide a tuple that containing the most efficient paths in end-to-end manner by considering more network metrics instead of one. Further, to address some significant issues of the performance analysis, a statistical test has been performed in order to determine whether the proposed protocol outperforms others or not by using Friedman test. The results show that there is a strong indication that four different protocols were analyzed and compared. It reveals that the proposed scheme outperforms others, especially in terms of end-to-end delay and energy consumption which allow ensuring quality of services requirements for Internet of Things (IoT) or smart city applications.
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In this paper, a high-level relay hybridization of three metaheuristics with different properties is proposed. Our objective is to investigate the use of this kind of hybridization to tackle black-box optimization problems. Indeed,... more
In this paper, a high-level relay hybridization of three metaheuristics with different properties is proposed. Our objective is to investigate the use of this kind of hybridization to tackle black-box optimization problems. Indeed, without any knowledge about the nature of the problem to optimize, combining the strengths of different algorithms, belonging to different classes of metaheuristics, may increase the probability of success of the optimization process. The proposed hybrid algorithm combines the multiple local search algorithm for dynamic optimization, the success-history based adaptive differential evolution, and the standard particle swarm optimization 2011 algorithm. An experimental analysis using two well-known benchmarks is presented, i.e. the Black-Box Optimization Benchmarking (BBOB) 2015 and the Black Box optimization Competition (BBComp). The proposed algorithm obtains promising results on both benchmarks. The ones obtained at BBComp show the relevance of the proposed hybridization.
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Typical evolutionary algorithms for Unmanned Aerial Vehicles (UAV) path planning problem represent solutions by considering a fixed number of way points, from which and by using an interpolation strategy they can generate the actual path.... more
Typical evolutionary algorithms for Unmanned Aerial Vehicles (UAV) path planning problem represent solutions by considering a fixed number of way points, from which and by using an interpolation strategy they can generate the actual path. This paper proposed an alternative method in which the number of control points is determined during the optimization process. We investigate to what extent optimizing the number of these points during the search process could contribute to improve the results. Towards this goal, a novel approach is proposed which combines the standard teaching learning-based optimization (TLBO) with the ideas of mutation and crossover from genetic algorithm (GA). Experiments are conducted on a set of scenarios in two-dimension (2D) and three-dimension (3D) environments. The results demonstrate promising performance for solving the path planning problem of UAV.
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The performance of Differential Evolution (DE) algorithm strongly depends on its control parameters. Despite its efficiency and wide use, it might get trapped in local minimum due to premature convergence. In this study, a novel parameter... more
The performance of Differential Evolution (DE) algorithm strongly depends on its control parameters. Despite its efficiency and wide use, it might get trapped in local minimum due to premature convergence. In this study, a novel parameter adaptation strategy is proposed to address the mentioned problems. To do so, a pheromone matrix is employed to adjust parameter setting of the algorithm during the optimization process. Moreover, the convergence issue of DE is tackled by incorporating a new restart strategy. The performance of the proposed algorithm is firstly evaluated on the CEC 2011 real world problems test suite. Thereafter, we applied the algorithm to find optimized structure of a recent electric motor design considered for this study. The results reveal the competitive performance of the proposed approach with state-of-the-art algorithms.
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This paper considers multiobjective UAV path planning in a real 3D environment with the objective to find a safe energy-efficient path. An Enhanced Non-dominated Sorting Genetic Algorithm-II, called ENSGA-II, is proposed and combines... more
This paper considers multiobjective UAV path planning in a real 3D environment with the objective to find a safe energy-efficient path. An Enhanced Non-dominated Sorting Genetic Algorithm-II, called ENSGA-II, is proposed and combines several sorts of heuristic information to customize crossover and mutation operators. Furthermore, a local search and a ranking-based roulette wheel selection are incorporated for the mating procedure. Experiment results confirm that ENSGA-II has a better convergence rate and spread of solutions on several new real-world datasets. The effectiveness of the local search component is also validated on the CrazyS robot operating system (ROS) package which consists of a pelican quadcopter's modeling.
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Data augmentation in deep neural networks is the process of generating artificial data in order to reduce the variance of the classifier with the goal to reduce the number of errors. This idea has been shown to improve deep neural... more
Data augmentation in deep neural networks is the process of generating artificial data in order to reduce the variance of the classifier with the goal to reduce the number of errors. This idea has been shown to improve deep neural network's generalization capabilities in many computer vision tasks such as image recognition and object localization. Apart from these applications, deep Convolutional Neural Networks (CNNs) have also recently gained popularity in the Time Series Classification (TSC) community. However, unlike in image recognition problems, data augmentation techniques have not yet been investigated thoroughly for the TSC task. This is surprising as the accuracy of deep learning models for TSC could potentially be improved, especially for small datasets that exhibit overfitting, when a data augmentation method is adopted. In this paper, we fill this gap by investigating the application of a recently proposed data augmentation technique based on the Dynamic Time Warpin...
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This paper introduces a hybridization of Backtracking Search Optimization Algorithm (BSA) with Differential Evolution (DE) and Simulated Annealing (SA) in order to improve the convergence speed of BSA. An experimental study, conducted on... more
This paper introduces a hybridization of Backtracking Search Optimization Algorithm (BSA) with Differential Evolution (DE) and Simulated Annealing (SA) in order to improve the convergence speed of BSA. An experimental study, conducted on 20 benchmark problems, shows that this approach outperforms BSA and two other hybridizations [4, 18], in terms of solution quality and convergence speed. We also describe our CUDA implementation of this algorithm for graphics processing unit (GPU). Experimental results are reported for 10 high-dimensional benchmark problems, and it highlights that significant speedup can be achieved.
Research Interests: Computer Science and CUDA
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La generation d'un plan de frequences est une tâche difficile dans le processus de planification, qui peut conduire a des plans de frequences peu adequats d'un point de vue metier. En effet, le processus de generation s'appuie... more
La generation d'un plan de frequences est une tâche difficile dans le processus de planification, qui peut conduire a des plans de frequences peu adequats d'un point de vue metier. En effet, le processus de generation s'appuie d'une part sur une modelisation des contraintes existant entre les points de service du reseau etudie, et d'autre part sur une optimisation combinatoire qui vise a satisfaire ces contraintes. Cette optimisation combinatoire fournit une solution optimale d'un point de vue mathematique, mais selon la finesse de modelisation des contraintes, la solution generee peut etre inutilisable dans la realite. Dans cette these, nous presentons de nouvelles methodes algorithmiques permettant de resoudre efficacement le probleme d'allocation de frequences dans le cadre de la radiodiffusion. L'utilisation de ces nouvelles approches a montre que la qualite des solutions obtenues est nettement meilleure que les meilleures solutions operationnelle...
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The surrogate models are offered as effective tools to approximate computationally expensive objective functions. This study investigates how approximation strategy of such models can be used for high dimensional protein structure... more
The surrogate models are offered as effective tools to approximate computationally expensive objective functions. This study investigates how approximation strategy of such models can be used for high dimensional protein structure prediction (PSP) problems. Two major contributions of the proposed approach are: 1) employing Stochastic Response Surface (SRS) to bias the initial population toward promising areas and 2) using queries of an active learning algorithm and a surrogate model to replace in part the original computationally expensive solver. The introduced framework is applied on several extensions of the differential evolution (DE) algorithm which are among noteworthy approaches for the PSP. Numerical experiments indicate that the proposed schema is able to improve performance of the conventional algorithms for the PSP problems in both terms of convergence speed and accuracy.
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The Unicost Set Covering Problem (USCP) is a well-known \(\mathcal {NP}\)-hard combinatorial optimization problem. This paper presents a memetic algorithm that combines and adapts the Hybrid Evolutionary Algorithm in Duet (HEAD) and the... more
The Unicost Set Covering Problem (USCP) is a well-known \(\mathcal {NP}\)-hard combinatorial optimization problem. This paper presents a memetic algorithm that combines and adapts the Hybrid Evolutionary Algorithm in Duet (HEAD) and the Row Weighting Local Search (RWLS) to solve the USCP. The former is a memetic approach with a population of only two individuals which was originally developed to tackle the graph coloring problem. The latter is a heuristic algorithm designed to solve the USCP by using a smart weighting scheme that prevents early convergence and guides the algorithm toward interesting sets. RWLS has been shown to be one of the most effective algorithm for the USCP. In the proposed approach, RWLS is modified to be efficiently used as the local search of HEAD (for exploitation purpose) on the one hand, and also to be used as the crossover (for exploration purpose) on the other hand. The HEAD framework is also adapted to take advantage of the information provided by the ...
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The present research is designed to present an Improved Success-History based Adaptive Differential Evolution (L-SHADE) to solve a practical Economic Dispatch Problem (ED P). Simultaneously involving both of the multiple fuel option and... more
The present research is designed to present an Improved Success-History based Adaptive Differential Evolution (L-SHADE) to solve a practical Economic Dispatch Problem (ED P). Simultaneously involving both of the multiple fuel option and valve-point effects make the EDP formulation more and more complex non-smooth optimization issue. Noting that the L-SHADE serves to introduce an adaptive mechanism whereby improves the control parameters selection and through the application of a linear population size reduction technique, reduces linearly the population size throughout the optimization process. Thus, enabling to yield better offspring for the next generation. For an effective evaluation of the proposed design's relevant performance, the L-SHDE optimizer is subjected to the IEEE 10 unit test system involving multiple fuel option and valve point effect. The experiment results confirm the ability of the L-SHADE algorithm to obtain best optimum results with lowest computational time...
Zeolite structure determination is an interesting challenge even with the progress in terms of structural resolution from X-rays and electron diffraction. The infinite number of potential solutions and the computational cost of this... more
Zeolite structure determination is an interesting challenge even with the progress in terms of structural resolution from X-rays and electron diffraction. The infinite number of potential solutions and the computational cost of this problem make the use of an evolutionary algorithm significant for this challenge. In this paper, we propose a new parallel and distributed hybrid genetic algorithm called MEmory Genetic Algorithm Hybridized for Zeolite (MEGA-HZ). This experimentation shows that the proposed algorithm is able to satisfy the constraints of the objective function to determine viable zeolite structures. From the 6 unit cell parameters and density, the MEGA-HZ has found 6 different viable zeolite structures.
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The 𝒩𝒫-hard minimum set cover problem (SCP) is a very typical model to use when attempting to formalise optimal camera placement (OCP) applications. In a generic form, the OCP problem relates to the positioning of individual cameras such... more
The 𝒩𝒫-hard minimum set cover problem (SCP) is a very typical model to use when attempting to formalise optimal camera placement (OCP) applications. In a generic form, the OCP problem relates to the positioning of individual cameras such that the overall network is able to cover a given area while meeting a set of application-specific requirements (image quality, redundancy, ...) and optimising an objective, typically minimum cost or maximum coverage. In this paper, we focus on an application called global or persistent surveillance: camera networks which ensure full coverage of a given area. As preliminary work, an instance generation pipeline is proposed to create OCP instances from real-world data and solve them using existing literature. The computational cost of both the instance generation process and the solving algorithms however highlights a need for more efficient methods for decision makers to use in real-world settings. In this paper, we therefore propose to review the s...
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Optimal camera placement (OCP) is one of many practical applications of a core $\mathcal{NP}$-complete problem in the field of combinatorial optimisation: set cover (SCP). In a generic form, the OCP problem relates to the positioning and... more
Optimal camera placement (OCP) is one of many practical applications of a core $\mathcal{NP}$-complete problem in the field of combinatorial optimisation: set cover (SCP). In a generic form, the OCP problem relates to the positioning and setting up of individual cameras such that the overall network is able to cover a given area while meeting a set of application-specific constraints (such as image quality or redundancy) and optimising an objective, typically minimum cost or maximum coverage, depending on the application's focus. In this paper, we consider the problem of positioning and orienting a minimal number of cameras such that the network is able to reach full coverage. More specifically, we introduce a framework for OCP instance generation which leaves the common realm of academic study cases and models the problem in real-world settings, using 8 West-European cities for numerical tests. A baseline is established by running several basic algorithms, which serve as a starting point for a more focused benchmark involving various state-of-the-art algorithms from both OCP and SCP literature. The results are then discussed and several elements highlighted for future research.
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State-of-the-art optimization algorithms often expose many parameters that should be configured to improve empirical performance. Manually tuning of such parameters is synonymous with tedious experiments which tend to lead to... more
State-of-the-art optimization algorithms often expose many parameters that should be configured to improve empirical performance. Manually tuning of such parameters is synonymous with tedious experiments which tend to lead to unsatisfactory outcomes. Accordingly, researchers developed several frameworks to tune the parameters of a given algorithm over a class of problems. Until very recently, however, these approaches are not testified and applied to many-objective algorithms. This study formulates a many-objective algorithm configuration (MAC) method which is available for the Matlab and Python. In MAC, we take into account the importance of a given configuration by building a conditional probability graph. In this light, the introduced algorithm aims to explore more important variables using an undirected fully-connected graph. Experimental results reveal that MAC performs better in comparison with state-of-the-art F-Race and SMAC frameworks.
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The rapid growing of Internet of things (IoT) is one of the most important factors of integrating WSN in smart buildings. Furthermore, the presence of these sensors and technologies allow improving the accuracy of the measured data while... more
The rapid growing of Internet of things (IoT) is one of the most important factors of integrating WSN in smart buildings. Furthermore, the presence of these sensors and technologies allow improving the accuracy of the measured data while reducing buildings energy consumption and guaranteeing the comfort required by the indoor users. However, finding the optimal sensor nodes positions in an indoor environment with heterogeneous obstacles is the keystone to ensure a full sensing coverage with a full connectivity. To meet this challenge, various initiatives have been proposed in the literature. First, we summarize the main developed solutions to optimize WSN deployment in an indoor/outdoor environment. Then, we propose our conceptual approach that relies on exploiting BIM (Building information modeling) database to get real time and valid information about the target area. Indeed, the proposed solution can be integrated within BIM tools as a plugin in order to optimize sensors deployment in real time by taking into account nodes and obstacles heterogeneity at the same time.
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PurposeThis paper aims to investigate to what extent hybrid differential evolution (DE) algorithms can be successful in solving the optimal camera placement problem.Design/methodology/approachThis problem is stated as a unicost set... more
PurposeThis paper aims to investigate to what extent hybrid differential evolution (DE) algorithms can be successful in solving the optimal camera placement problem.Design/methodology/approachThis problem is stated as a unicost set covering problem (USCP) and 18 problem instances are defined according to practical operational needs. Three methods are selected from the literature to solve these instances: a CPLEX solver, greedy algorithm and row weighting local search (RWLS). Then, it is proposed to hybridize these algorithms with two hybrid DE approaches designed for combinatorial optimization problems. The first one is a set-based approach (DEset) from the literature. The second one is a new similarity-based approach (DEsim) that takes advantage of the geometric characteristics of a camera to find better solutions.FindingsThe experimental study highlights that RWLS and DEsim-CPLEX are the best proposed algorithms. Both easily outperform CPLEX, and it turns out that RWLS performs be...
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ABSTRACT
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ABSTRACT This paper is a study about deployment strategy for achieving coverage and connectivity as two fundamental issues in wireless sensor networks. To achieve the best deployment, a new approach based on elitist non-dominated sorting... more
ABSTRACT This paper is a study about deployment strategy for achieving coverage and connectivity as two fundamental issues in wireless sensor networks. To achieve the best deployment, a new approach based on elitist non-dominated sorting genetic algorithm (NSGA-II) is used. There are two objectives in this study, connectivity and coverage. We defined a fitness function to achieve the best nodes deployment. Further we performed simulation to verify and validate the deployment of wireless sensor network as an output from the proposed mechanism. Some performance parameters have been measured to investigate and analyze the proposed sensor-deployment. The simulation results show that the proposed algorithm can maintain the coverage and connectivity in a given sensing area with a relatively small number of sensor nodes.
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Differential Evolution (DE) is a well-known metaheuristic designed to solve continuous optimization problems. Its simple structure and straight forward search operators make it suitable for solving a wide range of real world problems.... more
Differential Evolution (DE) is a well-known metaheuristic designed to solve continuous optimization problems. Its simple structure and straight forward search operators make it suitable for solving a wide range of real world problems. Despite its success, DE performance may be limited when tackling high dimensional complex problems. Therefore, its algorithmic structure can be reconsidered by adaptively controlling its parameters, and incorporating more resilient search operators. In this study, a Q-learning-based strategy is proposed to adapt DE parameters during the search process. Moreover, an eigenvector-based crossover is introduced in order to accelerate the convergence rate when ill-conditioned landscapes are treated. However, to avoid premature convergence, a simple yet efficient switching technique is proposed to choose between the normal and the eigenvector-based crossover. Due to the high computational time that might occur when applying the eigenvector-based crossover, a ...
Research Interests: Computer Science and IEEE
Over the past one hundred years, the classic teaching methodology of "see one, do one, teach one" has governed the surgical education systems worldwide. With the advent of Operation Room 2.0, recording video, kinematic and many... more
Over the past one hundred years, the classic teaching methodology of "see one, do one, teach one" has governed the surgical education systems worldwide. With the advent of Operation Room 2.0, recording video, kinematic and many other types of data during the surgery became an easy task, thus allowing artificial intelligence systems to be deployed and used in surgical and medical practice. Recently, surgical videos has been shown to provide a structure for peer coaching enabling novice trainees to learn from experienced surgeons by replaying those videos. However, the high inter-operator variability in surgical gesture duration and execution renders learning from comparing novice to expert surgical videos a very difficult task. In this paper, we propose a novel technique to align multiple videos based on the alignment of their corresponding kinematic multivariate time series data. By leveraging the Dynamic Time Warping measure, our algorithm synchronizes a set of videos in ...
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Metaheuristic algorithms (MAs) have seen unprecedented growth thanks to their successful applications in fields including engineering and health sciences. In this work, we investigate the use of a deep learning (DL) model as an... more
Metaheuristic algorithms (MAs) have seen unprecedented growth thanks to their successful applications in fields including engineering and health sciences. In this work, we investigate the use of a deep learning (DL) model as an alternative tool to do so. The proposed method, called MaNet, is motivated by the fact that most of the DL models often need to solve massive nasty optimization problems consisting of millions of parameters. Feature selection is the main adopted concepts in MaNet that helps the algorithm to skip irrelevant or partially relevant evolutionary information and uses those which contribute most to the overall performance. The introduced model is applied on several unimodal and multimodal continuous problems. The experiments indicate that MaNet is able to yield competitive results compared to one of the best hand-designed algorithms for the aforementioned problems, in terms of the solution accuracy and scalability.
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In this paper, we evaluate and compare our scheduling strategy for various IPTV services traffic over 802.16j networks with several scheduling algorithms such as Strict Priority algorithm (SP), Weighted Round Robin (WRR), and Modified... more
In this paper, we evaluate and compare our scheduling strategy for various IPTV services traffic over 802.16j networks with several scheduling algorithms such as Strict Priority algorithm (SP), Weighted Round Robin (WRR), and Modified Dynamic Weighted Round Robin (MWRR). The proposed scheme adapts dynamically the scheduler operation to according queue load and QoS constraints. In particular, the proposed mechanism gives more priority to HD-TV and SD-TV traffics by using two schedulers. The proposed scheduling algorithm has been simulated using the QualNet network simulator. The Friedman test has been used to address some significant issues of the analysis to compare the proposed scheme with the others scheme. The experimental result and analysis show that the proposed scheduler schemes outperform the traditional scheduling techniques for rtPS traffic that allows ensuring QoS requirements for IPTV application.
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The optimal camera placement problem is that of determining the best possible set of camera positions and orientations in order to meet application-specific constraints and objectives. This paper focuses on one application of the problem:... more
The optimal camera placement problem is that of determining the best possible set of camera positions and orientations in order to meet application-specific constraints and objectives. This paper focuses on one application of the problem: global area surveillance. Given an area to be covered, the question is to design a camera network which fully covers critical subareas and proceeds in a best-effort manner in the rest of the environment, given a limited budget. This is achieved through the integration of user-provided input into a mixed combinatorial model which brings together two variants of a popular optimisation problem. Time-efficient algorithms then allow for regular user interaction in between solving iterations. This human-assisted design is based off requirements set by experts and decision makers and yields components of a decision support system to support law enforcement officers and officials when designing video surveillance infrastructure.
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This paper investigates the application of a UAV path planning method to avoid collision with static and dynamic obstacles in urban environments. For this purpose, a hybrid algorithm based on differential evolution (DE) assisted by... more
This paper investigates the application of a UAV path planning method to avoid collision with static and dynamic obstacles in urban environments. For this purpose, a hybrid algorithm based on differential evolution (DE) assisted by $A^{\ast}$ algorithm is used for global path planning. Thereafter, multiple sub-paths surrounding the global path is generated based on local information. This helps the UAV to react appropriately with split seconds, to avoid the collision if moving obstacles appear. A series of experiments have been conducted using two urban datasets to demonstrate the effectiveness of the presented approach in terms of the path cost and computational time.