639 research outputs found

    Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

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    In recent years, Wireless sensor communication is growing expeditiously on the capability to gather information, communicate and transmit data effectively. Clustering is the main objective of improving the network lifespan in Wireless sensor network. It includes selecting the cluster head for each cluster in addition to grouping the nodes into clusters. The cluster head gathers data from the normal nodes in the cluster, and the gathered information is then transmitted to the base station. However, there are many reasons in effect opposing unsteady cluster head selection and dead nodes. The technique for selecting a cluster head takes into factors to consider including residual energy, neighbors’ nodes, and the distance between the base station to the regular nodes. In this study, we thoroughly investigated by number of methods of selecting a cluster head and constructing a cluster. Additionally, a quick performance assessment of the techniques' performance is given together with the methods' criteria, advantages, and future directions

    The Application of Ant Colony Optimization

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    The application of advanced analytics in science and technology is rapidly expanding, and developing optimization technics is critical to this expansion. Instead of relying on dated procedures, researchers can reap greater rewards by utilizing cutting-edge optimization techniques like population-based metaheuristic models, which can quickly generate a solution with acceptable quality. Ant Colony Optimization (ACO) is one the most critical and widely used models among heuristics and meta-heuristics. This book discusses ACO applications in Hybrid Electric Vehicles (HEVs), multi-robot systems, wireless multi-hop networks, and preventive, predictive maintenance

    An upper bound on the k-modem illumination problem

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    A variation on the classical polygon illumination problem was introduced in [Aichholzer et. al. EuroCG'09]. In this variant light sources are replaced by wireless devices called k-modems, which can penetrate a fixed number k, of "walls". A point in the interior of a polygon is "illuminated" by a k-modem if the line segment joining them intersects at most k edges of the polygon. It is easy to construct polygons of n vertices where the number of k-modems required to illuminate all interior points is Omega(n/k). However, no non-trivial upper bound is known. In this paper we prove that the number of k-modems required to illuminate any polygon of n vertices is at most O(n/k). For the cases of illuminating an orthogonal polygon or a set of disjoint orthogonal segments, we give a tighter bound of 6n/k + 1. Moreover, we present an O(n log n) time algorithm to achieve this bound.Comment: 9 pages, 4 figure

    An Improved Excitation Matching Method based on an Ant Colony Optimization for Suboptimal-Free Clustering in Sum-Difference Compromise Synthesis

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    Dealing with an excitation matching method, this paper presents a global optimization strategy for the optimal clustering in sum-difference compromise linear arrays. Starting from a combinatorial formulation of the problem at hand, the proposed technique is aimed at determining the sub-array configuration expressed as the optimal path inside a directed acyclic graph structure modelling the solution space. Towards this end, an ant colony metaheuristic is used to benefit of its hill-climbing properties in dealing with the non-convexity of the sub-arraying as well as in managing graph searches. A selected set of numerical experiments are reported to assess the efficiency and current limitations of the ant-based strategy also in comparison with previous local combinatorial search methods. (c) 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

    Precise Characterization and Multiobjective Optimization of Low Noise Amplifiers

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    Although practically all function blocks of the satellite navigation receivers are realized using the CMOS digital integrated circuits, it is appropriate to create a separate low noise antenna preamplifier based on a low noise pHEMT. Such an RF front end can be strongly optimized to attain a suitable tradeoff between the noise figure and transducer power gain. Further, as all the four principal navigation systems (GPS, GLONASS, Galileo, and COMPASS) work in similar frequency bands (roughly from 1.1 to 1.7 GHz), it is reasonable to create the low noise preamplifier for all of them. In the paper, a sophisticated method of the amplifier design is suggested based on multiobjective optimization. A substantial improvement of a standard optimization method is also outlined to satisfy a uniform coverage of Pareto front. Moreover, for enhancing efficiency of many times repeated solutions of large linear systems during the optimization, a new modification of the Markowitz criterion is suggested compatible with fast modes of the LU factorization. Extraordinary attention was also given to the accuracy of modeling. First, an extraction of pHEMT model parameters was performed including its noise part, and several models were compared. The extraction was carried out by an original identification procedure based on a combination of metaheuristic and direct methods. Second, the equations of the passive elements (including transmission lines and T-splitters) were carefully defined using frequency dispersion of their parameters as Q, ESR, etc. Third, an optimal selection of the operating point and essential passive elements was performed using the improved optimization method. Finally, the s-parameters and noise figure of the amplifier were measured, and stability and third-order intermodulation products were also checked

    A Survey on the Application of Evolutionary Algorithms for Mobile Multihop Ad Hoc Network Optimization Problems

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    Evolutionary algorithms are metaheuristic algorithms that provide quasioptimal solutions in a reasonable time. They have been applied to many optimization problems in a high number of scientific areas. In this survey paper, we focus on the application of evolutionary algorithms to solve optimization problems related to a type of complex network likemobilemultihop ad hoc networks. Since its origin, mobile multihop ad hoc network has evolved causing new types of multihop networks to appear such as vehicular ad hoc networks and delay tolerant networks, leading to the solution of new issues and optimization problems. In this survey, we review the main work presented for each type of mobile multihop ad hoc network and we also present some innovative ideas and open challenges to guide further research in this topic

    Mission-based mobility models for UAV networks

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    Las redes UAV han atraído la atención de los investigadores durante la última década. Las numerosas posibilidades que ofrecen los sistemas single-UAV aumentan considerablemente al usar múltiples UAV. Sin embargo, el gran potencial del sistema multi-UAV viene con un precio: la complejidad de controlar todos los aspectos necesarios para garantizar que los UAVs cumplen la misión que se les ha asignado. Ha habido numerosas investigaciones dedicadas a los sistemas multi-UAV en el campo de la robótica en las cuales se han utilizado grupos de UAVs para diferentes aplicaciones. Sin embargo, los aspectos relacionados con la red que forman estos sistemas han comenzado a reclamar un lugar entre la comunidad de investigación y han hecho que las redes de UAVs se consideren como un nuevo paradigma entre las redes multi-salto. La investigación de redes de UAVs, de manera similar a otras redes multi-salto, se divide principalmente en dos categorías: i) modelos de movilidad que capturan la movilidad de la red, y ii) algoritmos de enrutamiento. Ambas categorías han heredado muchos algoritmos que pertenecían a las redes MANET, que fueron el primer paradigma de redes multi-salto que atrajo la atención de los investigadores. Aunque hay esfuerzos de investigación en curso que proponen soluciones para ambas categorías, el número de modelos de movilidad y algoritmos de enrutamiento específicos para redes UAV es limitado. Además, en el caso de los modelos de movilidad, las soluciones existentes propuestas son simplistas y apenas representan la movilidad real de un equipo de UAVs, los cuales se utilizan principalmente en operaciones orientadas a misiones, en la que cada UAV tiene asignados movimientos específicos. Esta tesis propone dos modelos de movilidad basados en misiones para una red de UAVs que realiza dos operaciones diferentes. El escenario elegido en el que se desarrollan las misiones corresponde con una región en la que ha ocurrido, por ejemplo, un desastre natural. La elección de este tipo de escenario se debe a que en zonas de desastre, la infraestructura de comunicaciones comúnmente está dañada o totalmente destruida. En este tipo de situaciones, una red de UAVs ofrece la posibilidad de desplegar rápidamente una red de comunicaciones. El primer modelo de movilidad, llamado dPSO-U, ha sido diseñado para capturar la movilidad de una red UAV en una misión con dos objetivos principales: i) explorar el área del escenario para descubrir las ubicaciones de los nodos terrestres, y ii) hacer que los UAVs converjan de manera autónoma a los grupos en los que se organizan los nodos terrestres (también conocidos como clusters). El modelo de movilidad dPSO-U se basa en el conocido algoritmo particle swarm optimization (PSO), considerando los UAV como las partículas del algoritmo, y también utilizando el concepto de valores dinámicos para la inercia, el local best y el neighbour best de manera que el modelo de movilidad tenga ambas capacidades: la de exploración y la de convergencia. El segundo modelo, denominado modelo de movilidad Jaccard-based, captura la movilidad de una red UAV que tiene asignada la misión de proporcionar servicios de comunicación inalámbrica en un escenario de mediano tamaño. En este modelo de movilidad se ha utilizado una combinación del virtual forces algorithm (VFA), de la distancia Jaccard entre cada par de UAVs y metaheurísticas como hill climbing y simulated annealing, para cumplir los dos objetivos de la misión: i) maximizar el número de nodos terrestres (víctimas) que se encuentran bajo el área de cobertura inalámbrica de la red UAV, y ii) mantener la red UAV como una red conectada, es decir, evitando las desconexiones entre UAV. Se han realizado simulaciones exhaustivas con herramientas software específicamente desarrolladas para los modelos de movilidad propuestos. También se ha definido un conjunto de métricas para cada modelo de movilidad. Estas métricas se han utilizado para validar la capacidad de los modelos de movilidad propuestos de emular los movimientos de una red UAV en cada misión.UAV networks have attracted the attention of the research community in the last decade. The numerous capabilities of single-UAV systems increase considerably by using multiple UAVs. The great potential of a multi-UAV system comes with a price though: the complexity of controlling all the aspects required to guarantee that the UAV team accomplish the mission that it has been assigned. There have been numerous research works devoted to multi-UAV systems in the field of robotics using UAV teams for different applications. However, the networking aspects of multi-UAV systems started to claim a place among the research community and have made UAV networks to be considered as a new paradigm among the multihop ad hoc networks. UAV networks research, in a similar manner to other multihop ad hoc networks, is mainly divided into two categories: i) mobility models that capture the network mobility, and ii) routing algorithms. Both categories have inherited previous algorithms mechanisms that originally belong to MANETs, being these the first multihop networking paradigm attracting the attention of researchers. Although there are ongoing research efforts proposing solutions for the aforementioned categories, the number of UAV networks-specific mobility models and routing algorithms is limited. In addition, in the case of the mobility models, the existing solutions proposed are simplistic and barely represent the real mobility of a UAV team, which are mainly used in missions-oriented operations. This thesis proposes two mission-based mobility models for a UAV network carrying out two different operations over a disaster-like scenario. The reason for selecting a disaster scenario is because, usually, the common communication infrastructure is malfunctioning or completely destroyed. In these cases, a UAV network allows building a support communication network which is rapidly deployed. The first mobility model, called dPSO-U, has been designed for capturing the mobility of a UAV network in a mission with two main objectives: i) exploring the scenario area for discovering the location of ground nodes, and ii) making the UAVs to autonomously converge to the groups in which the nodes are organized (also referred to as clusters). The dPSO-U mobility model is based on the well-known particle swarm optimization algorithm (PSO), considering the UAVs as the particles of the algorithm, and also using the concept of dynamic inertia, local best and neighbour best weights so the mobility model can have both abilities: exploration and convergence. The second one, called Jaccard-based mobility model, captures the mobility of a UAV network that has been assigned with the mission of providing wireless communication services in a medium-scale scenario. A combination of the virtual forces algorithm (VFA), the Jaccard distance between each pair of UAVs and metaheuristics such as hill climbing or simulated annealing have been used in this mobility model in order to meet the two mission objectives: i) to maximize the number of ground nodes (i.e. victims) under the UAV network wireless coverage area, and ii) to maintain the UAV network as a connected network, i.e. avoiding UAV disconnections. Extensive simulations have been performed with software tools that have been specifically developed for the proposed mobility models. Also, a set of metrics have been defined and measured for each mobility model. These metrics have been used for validating the ability of the proposed mobility models to emulate the movements of a UAV network in each mission

    A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

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    The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area
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