872 resultados para direct search optimization algorithm
Resumo:
We report the first direct search for the Kaluza-Klein (KK) modes of Randall-Sundrum gravitons using dielectron, dimuon, and diphoton events observed with the D0 detector operating at the Fermilab Tevatron p (p) over bar Collider at root s = 1: 96 TeV. No evidence for resonant production of gravitons has been found in the data corresponding to an integrated luminosity of approximate to 260 pb(-1). Lower limits on the mass of the first KK mode at the 95% C. L. have been set between 250 and 785 GeV, depending on its coupling to standard model particles.
Resumo:
Long-lived, heavy particles are predicted in a number of models beyond the standard model of particle physics. We present the first direct search for such particles' decays, occurring up to 100 h after their production and not synchronized with an accelerator bunch crossing. We apply the analysis to the gluino (g), predicted in split supersymmetry, which after hadronization can become charged and lose enough momentum through ionization to come to rest in dense particle detectors. Approximately 410 pb(-1) of p (p) over bar collisions at root s = 1.96 TeV collected with the D0 detector during Run II of the Fermilab Tevatron collider are analyzed in search of such stopped gluinos decaying into a gluon and a neutralino ((chi) over tilde (0)(1)). Limits are placed on the (gluino cross section) x (probability to stop) x [BR((g) over tilde -> g (chi) over tilde (0)(1))] as a function of the gluino and (chi) over tilde (0)(1) masses, for gluino lifetimes from 30 mu s-100 h.
Resumo:
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Resumo:
Pattern recognition in large amount of data has been paramount in the last decade, since that is not straightforward to design interactive and real time classification systems. Very recently, the Optimum-Path Forest classifier was proposed to overcome such limitations, together with its training set pruning algorithm, which requires a parameter that has been empirically set up to date. In this paper, we propose a Harmony Search-based algorithm that can find near optimal values for that. The experimental results have showed that our algorithm is able to find proper values for the OPF pruning algorithm parameter. © 2011 IEEE.
Resumo:
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Resumo:
Pós-graduação em Ciências Cartográficas - FCT
Resumo:
Pós-graduação em Engenharia Elétrica - FEIS
Resumo:
In this letter, a semiautomatic method for road extraction in object space is proposed that combines a stereoscopic pair of low-resolution aerial images with a digital terrain model (DTM) structured as a triangulated irregular network (TIN). First, we formulate an objective function in the object space to allow the modeling of roads in 3-D. In this model, the TIN-based DTM allows the search for the optimal polyline to be restricted along a narrow band that is overlaid upon it. Finally, the optimal polyline for each road is obtained by optimizing the objective function using the dynamic programming optimization algorithm. A few seed points need to be supplied by an operator. To evaluate the performance of the proposed method, a set of experiments was designed using two stereoscopic pairs of low-resolution aerial images and a TIN-based DTM with an average resolution of 1 m. The experimental results showed that the proposed method worked properly, even when faced with anomalies along roads, such as obstructions caused by shadows and trees.
Resumo:
Biogeography is the science that studies the geographical distribution and the migration of species in an ecosystem. Biogeography-based optimization (BBO) is a recently developed global optimization algorithm as a generalization of biogeography to evolutionary algorithm and has shown its ability to solve complex optimization problems. BBO employs a migration operator to share information between the problem solutions. The problem solutions are identified as habitat, and the sharing of features is called migration. In this paper, a multiobjective BBO, combined with a predator-prey (PPBBO) approach, is proposed and validated in the constrained design of a brushless dc wheel motor. The results demonstrated that the proposed PPBBO approach converged to promising solutions in terms of quality and dominance when compared with the classical BBO in a multiobjective version.
Resumo:
Small scale fluid flow systems have been studied for various applications, such as chemical reagent dosages and cooling devices of compact electronic components. This work proposes to present the complete cycle development of an optimized heat sink designed by using Topology Optimization Method (TOM) for best performance, including minimization of pressure drop in fluid flow and maximization of heat dissipation effects, aiming small scale applications. The TOM is applied to a domain, to obtain an optimized channel topology, according to a given multi-objective function that combines pressure drop minimization and heat transfer maximization. Stokes flow hypothesis is adopted. Moreover, both conduction and forced convection effects are included in the steady-state heat transfer model. The topology optimization procedure combines the Finite Element Method (to carry out the physical analysis) with Sequential Linear Programming (as the optimization algorithm). Two-dimensional topology optimization results of channel layouts obtained for a heat sink design are presented as example to illustrate the design methodology. 3D computational simulations and prototype manufacturing have been carried out to validate the proposed design methodology.
Resumo:
The aim of this Doctoral Thesis is to develop a genetic algorithm based optimization methods to find the best conceptual design architecture of an aero-piston-engine, for given design specifications. Nowadays, the conceptual design of turbine airplanes starts with the aircraft specifications, then the most suited turbofan or turbo propeller for the specific application is chosen. In the aeronautical piston engines field, which has been dormant for several decades, as interest shifted towards turboaircraft, new materials with increased performance and properties have opened new possibilities for development. Moreover, the engine’s modularity given by the cylinder unit, makes it possible to design a specific engine for a given application. In many real engineering problems the amount of design variables may be very high, characterized by several non-linearities needed to describe the behaviour of the phenomena. In this case the objective function has many local extremes, but the designer is usually interested in the global one. The stochastic and the evolutionary optimization techniques, such as the genetic algorithms method, may offer reliable solutions to the design problems, within acceptable computational time. The optimization algorithm developed here can be employed in the first phase of the preliminary project of an aeronautical piston engine design. It’s a mono-objective genetic algorithm, which, starting from the given design specifications, finds the engine propulsive system configuration which possesses minimum mass while satisfying the geometrical, structural and performance constraints. The algorithm reads the project specifications as input data, namely the maximum values of crankshaft and propeller shaft speed and the maximal pressure value in the combustion chamber. The design variables bounds, that describe the solution domain from the geometrical point of view, are introduced too. In the Matlab® Optimization environment the objective function to be minimized is defined as the sum of the masses of the engine propulsive components. Each individual that is generated by the genetic algorithm is the assembly of the flywheel, the vibration damper and so many pistons, connecting rods, cranks, as the number of the cylinders. The fitness is evaluated for each individual of the population, then the rules of the genetic operators are applied, such as reproduction, mutation, selection, crossover. In the reproduction step the elitist method is applied, in order to save the fittest individuals from a contingent mutation and recombination disruption, making it undamaged survive until the next generation. Finally, as the best individual is found, the optimal dimensions values of the components are saved to an Excel® file, in order to build a CAD-automatic-3D-model for each component of the propulsive system, having a direct pre-visualization of the final product, still in the engine’s preliminary project design phase. With the purpose of showing the performance of the algorithm and validating this optimization method, an actual engine is taken, as a case study: it’s the 1900 JTD Fiat Avio, 4 cylinders, 4T, Diesel. Many verifications are made on the mechanical components of the engine, in order to test their feasibility and to decide their survival through generations. A system of inequalities is used to describe the non-linear relations between the design variables, and is used for components checking for static and dynamic loads configurations. The design variables geometrical boundaries are taken from actual engines data and similar design cases. Among the many simulations run for algorithm testing, twelve of them have been chosen as representative of the distribution of the individuals. Then, as an example, for each simulation, the corresponding 3D models of the crankshaft and the connecting rod, have been automatically built. In spite of morphological differences among the component the mass is almost the same. The results show a significant mass reduction (almost 20% for the crankshaft) in comparison to the original configuration, and an acceptable robustness of the method have been shown. The algorithm here developed is shown to be a valid method for an aeronautical-piston-engine preliminary project design optimization. In particular the procedure is able to analyze quite a wide range of design solutions, rejecting the ones that cannot fulfill the feasibility design specifications. This optimization algorithm could increase the aeronautical-piston-engine development, speeding up the production rate and joining modern computation performances and technological awareness to the long lasting traditional design experiences.
Resumo:
Im Jahr 2011 wurde am Large Hadron Collider mit dem ATLAS Experiment ein Datensatz von 4.7 inversen Femtobarn bei einer Schwerpunktsenergie von 7 TeV aufgezeichnet. Teil des umfangreichen Physikprogrammes des ATLAS Experiments ist die Suche nach Physik jenseits des Standardmodells. Supersymmetrie - eine neue Symmetrie zwischen Bosonen und Fermionen - wird als aussichtsreichester Kandidat für neue Physik angesehen, und zahlreiche direkte und indirekte Suchen nach Supersymmetrie wurden in den letzten Jahrzehnten bereits durchgeführt. In der folgenden Arbeit wird eine direkte Suche nach Supersymmetrie in Endzuständen mit Jets, fehlender Transversalenergie und genau einem Elektron oder Myon durchgeführt. Der analysierte Datensatz von 4.7 inversen Femtobarn umfasst die gesamte Datenmenge, welche am ATLAS Experiment bei einer Schwerpunktsenergie von 7 TeV aufgezeichnet wurde. Die Ergebnisse der Analyse werden mit verschiedenen anderen leptonischen Suchkanälen kombiniert, um die Sensitivität auf diversen supersymmetrischen Produktions- und Zerfallsmodi zu maximieren. Die gemessenen Daten sind kompatibel mit der Standardmodellerwartung, und neue Ausschlussgrenzen in verschiedenen supersymmetrischen Modellen werden berechnet.
Resumo:
Wind energy has been one of the most growing sectors of the nation’s renewable energy portfolio for the past decade, and the same tendency is being projected for the upcoming years given the aggressive governmental policies for the reduction of fossil fuel dependency. Great technological expectation and outstanding commercial penetration has shown the so called Horizontal Axis Wind Turbines (HAWT) technologies. Given its great acceptance, size evolution of wind turbines over time has increased exponentially. However, safety and economical concerns have emerged as a result of the newly design tendencies for massive scale wind turbine structures presenting high slenderness ratios and complex shapes, typically located in remote areas (e.g. offshore wind farms). In this regard, safety operation requires not only having first-hand information regarding actual structural dynamic conditions under aerodynamic action, but also a deep understanding of the environmental factors in which these multibody rotating structures operate. Given the cyclo-stochastic patterns of the wind loading exerting pressure on a HAWT, a probabilistic framework is appropriate to characterize the risk of failure in terms of resistance and serviceability conditions, at any given time. Furthermore, sources of uncertainty such as material imperfections, buffeting and flutter, aeroelastic damping, gyroscopic effects, turbulence, among others, have pleaded for the use of a more sophisticated mathematical framework that could properly handle all these sources of indetermination. The attainable modeling complexity that arises as a result of these characterizations demands a data-driven experimental validation methodology to calibrate and corroborate the model. For this aim, System Identification (SI) techniques offer a spectrum of well-established numerical methods appropriated for stationary, deterministic, and data-driven numerical schemes, capable of predicting actual dynamic states (eigenrealizations) of traditional time-invariant dynamic systems. As a consequence, it is proposed a modified data-driven SI metric based on the so called Subspace Realization Theory, now adapted for stochastic non-stationary and timevarying systems, as is the case of HAWT’s complex aerodynamics. Simultaneously, this investigation explores the characterization of the turbine loading and response envelopes for critical failure modes of the structural components the wind turbine is made of. In the long run, both aerodynamic framework (theoretical model) and system identification (experimental model) will be merged in a numerical engine formulated as a search algorithm for model updating, also known as Adaptive Simulated Annealing (ASA) process. This iterative engine is based on a set of function minimizations computed by a metric called Modal Assurance Criterion (MAC). In summary, the Thesis is composed of four major parts: (1) development of an analytical aerodynamic framework that predicts interacted wind-structure stochastic loads on wind turbine components; (2) development of a novel tapered-swept-corved Spinning Finite Element (SFE) that includes dampedgyroscopic effects and axial-flexural-torsional coupling; (3) a novel data-driven structural health monitoring (SHM) algorithm via stochastic subspace identification methods; and (4) a numerical search (optimization) engine based on ASA and MAC capable of updating the SFE aerodynamic model.
Resumo:
We investigate a class of optimal control problems that exhibit constant exogenously given delays in the control in the equation of motion of the differential states. Therefore, we formulate an exemplary optimal control problem with one stock and one control variable and review some analytic properties of an optimal solution. However, analytical considerations are quite limited in case of delayed optimal control problems. In order to overcome these limits, we reformulate the problem and apply direct numerical methods to calculate approximate solutions that give a better understanding of this class of optimization problems. In particular, we present two possibilities to reformulate the delayed optimal control problem into an instantaneous optimal control problem and show how these can be solved numerically with a stateof- the-art direct method by applying Bock’s direct multiple shooting algorithm. We further demonstrate the strength of our approach by two economic examples.
Resumo:
Wireless sensor networks (WSNs) have shown their potentials in various applications, which bring a lot of benefits to users from both research and industrial areas. For many setups, it is envisioned thatWSNs will consist of tens to hundreds of nodes that operate on small batteries. However due to the diversity of the deployed environments and resource constraints on radio communication, sensing ability and energy supply, it is a very challenging issue to plan optimized WSN topology and predict its performance before real deployment. During the network planning phase, the connectivity, coverage, cost, network longevity and service quality should all be considered. Therefore it requires designers coping with comprehensive and interdisciplinary knowledge, including networking, radio engineering, embedded system and so on, in order to efficiently construct a reliable WSN for any specific types of environment. Nowadays there is still a lack of the analysis and experiences to guide WSN designers to efficiently construct WSN topology successfully without many trials. Therefore, simulation is a feasible approach to the quantitative analysis of the performance of wireless sensor networks. However the existing planning algorithms and tools, to some extent, have serious limitations to practically design reliable WSN topology: Only a few of them tackle the 3D deployment issue, and an overwhelming number of works are proposed to place devices in 2D scheme. Without considering the full dimension, the impacts of environment to the performance of WSN are not completely studied, thus the values of evaluated metrics such as connectivity and sensing coverage are not sufficiently accurate to make proper decision. Even fewer planning methods model the sensing coverage and radio propagation by considering the realistic scenario where obstacles exist. Radio signals propagate with multi-path phenomenon in the real world, in which direct paths, reflected paths and diffracted paths contribute to the received signal strength. Besides, obstacles between the path of sensor and objects might block the sensing signals, thus create coverage hole in the application. None of the existing planning algorithms model the network longevity and packet delivery capability properly and practically. They often employ unilateral and unrealistic formulations. The optimization targets are often one-sided in the current works. Without comprehensive evaluation on the important metrics, the performance of planned WSNs can not be reliable and entirely optimized. Modeling of environment is usually time consuming and the cost is very high, while none of the current works figure out any method to model the 3D deployment environment efficiently and accurately. Therefore many researchers are trapped by this issue, and their algorithms can only be evaluated in the same scenario, without the possibility to test the robustness and feasibility for implementations in different environments. In this thesis, we propose a novel planning methodology and an intelligent WSN planning tool to assist WSN designers efficiently planning reliable WSNs. First of all, a new method is proposed to efficiently and automatically model the 3D indoor and outdoor environments. To the best of our knowledge, this is the first time that the advantages of image understanding algorithm are applied to automatically reconstruct 3D outdoor and indoor scenarios for signal propagation and network planning purpose. The experimental results indicate that the proposed methodology is able to accurately recognize different objects from the satellite images of the outdoor target regions and from the scanned floor plan of indoor area. Its mechanism offers users a flexibility to reconstruct different types of environment without any human interaction. Thereby it significantly reduces human efforts, cost and time spent on reconstructing a 3D geographic database and allows WSN designers concentrating on the planning issues. Secondly, an efficient ray-tracing engine is developed to accurately and practically model the radio propagation and sensing signal on the constructed 3D map. The engine contributes on efficiency and accuracy to the estimated results. By using image processing concepts, including the kd-tree space division algorithm and modified polar sweep algorithm, the rays are traced efficiently without detecting all the primitives in the scene. The radio propagation model iv is proposed, which emphasizes not only the materials of obstacles but also their locations along the signal path. The sensing signal of sensor nodes, which is sensitive to the obstacles, is benefit from the ray-tracing algorithm via obstacle detection. The performance of this modelling method is robust and accurate compared with conventional methods, and experimental results imply that this methodology is suitable for both outdoor urban scenes and indoor environments. Moreover, it can be applied to either GSM communication or ZigBee protocol by varying frequency parameter of the radio propagation model. Thirdly, WSN planning method is proposed to tackle the above mentioned challenges and efficiently deploy reliable WSNs. More metrics (connectivity, coverage, cost, lifetime, packet latency and packet drop rate) are modeled more practically compared with other works. Especially 3D ray tracing method is used to model the radio link and sensing signal which are sensitive to the obstruction of obstacles; network routing is constructed by using AODV protocol; the network longevity, packet delay and packet drop rate are obtained via simulating practical events in WSNet simulator, which to the best of our knowledge, is the first time that network simulator is involved in a planning algorithm. Moreover, a multi-objective optimization algorithm is developed to cater for the characteristics of WSNs. The capability of providing multiple optimized solutions simultaneously allows users making their own decisions accordingly, and the results are more comprehensively optimized compared with other state-of-the-art algorithms. iMOST is developed by integrating the introduced algorithms, to assist WSN designers efficiently planning reliable WSNs for different configurations. The abbreviated name iMOST stands for an Intelligent Multi-objective Optimization Sensor network planning Tool. iMOST contributes on: (1) Convenient operation with a user-friendly vision system; (2) Efficient and automatic 3D database reconstruction and fast 3D objects design for both indoor and outdoor environments; (3) It provides multiple multi-objective optimized 3D deployment solutions and allows users to configure the network properties, hence it can adapt to various WSN applications; (4) Deployment solutions in the 3D space and the corresponding evaluated performance are visually presented to users; and (5) The Node Placement Module of iMOST is available online as well as the source code of the other two rebuilt heuristics. Therefore WSN designers will be benefit from v this tool on efficiently constructing environment database, practically and efficiently planning reliable WSNs for both outdoor and indoor applications. With the open source codes, they are also able to compare their developed algorithms with ours to contribute to this academic field. Finally, solid real results are obtained for both indoor and outdoor WSN planning. Deployments have been realized for both indoor and outdoor environments based on the provided planning solutions. The measured results coincide well with the estimated results. The proposed planning algorithm is adaptable according to the WSN designer’s desirability and configuration, and it offers flexibility to plan small and large scale, indoor and outdoor 3D deployments. The thesis is organized in 7 chapters. In Chapter 1, WSN applications and motivations of this work are introduced, the state-of-the-art planning algorithms and tools are reviewed, challenges are stated out and the proposed methodology is briefly introduced. In Chapter 2, the proposed 3D environment reconstruction methodology is introduced and its performance is evaluated for both outdoor and indoor environment. The developed ray-tracing engine and proposed radio propagation modelling method are described in details in Chapter 3, their performances are evaluated in terms of computation efficiency and accuracy. Chapter 4 presents the modelling of important metrics of WSNs and the proposed multi-objective optimization planning algorithm, the performance is compared with the other state-of-the-art planning algorithms. The intelligent WSN planning tool iMOST is described in Chapter 5. RealWSN deployments are prosecuted based on the planned solutions for both indoor and outdoor scenarios, important data are measured and results are analysed in Chapter 6. Chapter 7 concludes the thesis and discusses about future works. vi Resumen en Castellano Las redes de sensores inalámbricas (en inglés Wireless Sensor Networks, WSNs) han demostrado su potencial en diversas aplicaciones que aportan una gran cantidad de beneficios para el campo de la investigación y de la industria. Para muchas configuraciones se prevé que las WSNs consistirán en decenas o cientos de nodos que funcionarán con baterías pequeñas. Sin embargo, debido a la diversidad de los ambientes para desplegar las redes y a las limitaciones de recursos en materia de comunicación de radio, capacidad de detección y suministro de energía, la planificación de la topología de la red y la predicción de su rendimiento es un tema muy difícil de tratar antes de la implementación real. Durante la fase de planificación del despliegue de la red se deben considerar aspectos como la conectividad, la cobertura, el coste, la longevidad de la red y la calidad del servicio. Por lo tanto, requiere de diseñadores con un amplio e interdisciplinario nivel de conocimiento que incluye la creación de redes, la ingeniería de radio y los sistemas embebidos entre otros, con el fin de construir de manera eficiente una WSN confiable para cualquier tipo de entorno. Hoy en día todavía hay una falta de análisis y experiencias que orienten a los diseñadores de WSN para construir las topologías WSN de manera eficiente sin realizar muchas pruebas. Por lo tanto, la simulación es un enfoque viable para el análisis cuantitativo del rendimiento de las redes de sensores inalámbricos. Sin embargo, los algoritmos y herramientas de planificación existentes tienen, en cierta medida, serias limitaciones para diseñar en la práctica una topología fiable de WSN: Sólo unos pocos abordan la cuestión del despliegue 3D mientras que existe una gran cantidad de trabajos que colocan los dispositivos en 2D. Si no se analiza la dimensión completa (3D), los efectos del entorno en el desempeño de WSN no se estudian por completo, por lo que los valores de los parámetros evaluados, como la conectividad y la cobertura de detección, no son lo suficientemente precisos para tomar la decisión correcta. Aún en menor medida los métodos de planificación modelan la cobertura de los sensores y la propagación de la señal de radio teniendo en cuenta un escenario realista donde existan obstáculos. Las señales de radio en el mundo real siguen una propagación multicamino, en la que los caminos directos, los caminos reflejados y los caminos difractados contribuyen a la intensidad de la señal recibida. Además, los obstáculos entre el recorrido del sensor y los objetos pueden bloquear las señales de detección y por lo tanto crear áreas sin cobertura en la aplicación. Ninguno de los algoritmos de planificación existentes modelan el tiempo de vida de la red y la capacidad de entrega de paquetes correctamente y prácticamente. A menudo se emplean formulaciones unilaterales y poco realistas. Los objetivos de optimización son a menudo tratados unilateralmente en los trabajos actuales. Sin una evaluación exhaustiva de los parámetros importantes, el rendimiento previsto de las redes inalámbricas de sensores no puede ser fiable y totalmente optimizado. Por lo general, el modelado del entorno conlleva mucho tiempo y tiene un coste muy alto, pero ninguno de los trabajos actuales propone algún método para modelar el entorno de despliegue 3D con eficiencia y precisión. Por lo tanto, muchos investigadores están limitados por este problema y sus algoritmos sólo se pueden evaluar en el mismo escenario, sin la posibilidad de probar la solidez y viabilidad para las implementaciones en diferentes entornos. En esta tesis, se propone una nueva metodología de planificación así como una herramienta inteligente de planificación de redes de sensores inalámbricas para ayudar a los diseñadores a planificar WSNs fiables de una manera eficiente. En primer lugar, se propone un nuevo método para modelar demanera eficiente y automática los ambientes interiores y exteriores en 3D. Según nuestros conocimientos hasta la fecha, esta es la primera vez que las ventajas del algoritmo de _image understanding_se aplican para reconstruir automáticamente los escenarios exteriores e interiores en 3D para analizar la propagación de la señal y viii la planificación de la red. Los resultados experimentales indican que la metodología propuesta es capaz de reconocer con precisión los diferentes objetos presentes en las imágenes satelitales de las regiones objetivo en el exterior y de la planta escaneada en el interior. Su mecanismo ofrece a los usuarios la flexibilidad para reconstruir los diferentes tipos de entornos sin ninguna interacción humana. De este modo se reduce considerablemente el esfuerzo humano, el coste y el tiempo invertido en la reconstrucción de una base de datos geográfica con información 3D, permitiendo así que los diseñadores se concentren en los temas de planificación. En segundo lugar, se ha desarrollado un motor de trazado de rayos (en inglés ray tracing) eficiente para modelar con precisión la propagación de la señal de radio y la señal de los sensores en el mapa 3D construido. El motor contribuye a la eficiencia y la precisión de los resultados estimados. Mediante el uso de los conceptos de procesamiento de imágenes, incluyendo el algoritmo del árbol kd para la división del espacio y el algoritmo _polar sweep_modificado, los rayos se trazan de manera eficiente sin la detección de todas las primitivas en la escena. El modelo de propagación de radio que se propone no sólo considera los materiales de los obstáculos, sino también su ubicación a lo largo de la ruta de señal. La señal de los sensores de los nodos, que es sensible a los obstáculos, se ve beneficiada por la detección de objetos llevada a cabo por el algoritmo de trazado de rayos. El rendimiento de este método de modelado es robusto y preciso en comparación con los métodos convencionales, y los resultados experimentales indican que esta metodología es adecuada tanto para escenas urbanas al aire libre como para ambientes interiores. Por otra parte, se puede aplicar a cualquier comunicación GSM o protocolo ZigBee mediante la variación de la frecuencia del modelo de propagación de radio. En tercer lugar, se propone un método de planificación de WSNs para hacer frente a los desafíos mencionados anteriormente y desplegar redes de sensores fiables de manera eficiente. Se modelan más parámetros (conectividad, cobertura, coste, tiempo de vida, la latencia de paquetes y tasa de caída de paquetes) en comparación con otros trabajos. Especialmente el método de trazado de rayos 3D se utiliza para modelar el enlace de radio y señal de los sensores que son sensibles a la obstrucción de obstáculos; el enrutamiento de la red se construye utilizando el protocolo AODV; la longevidad de la red, retardo de paquetes ix y tasa de abandono de paquetes se obtienen a través de la simulación de eventos prácticos en el simulador WSNet, y según nuestros conocimientos hasta la fecha, es la primera vez que simulador de red está implicado en un algoritmo de planificación. Por otra parte, se ha desarrollado un algoritmo de optimización multi-objetivo para satisfacer las características de las redes inalámbricas de sensores. La capacidad de proporcionar múltiples soluciones optimizadas de forma simultánea permite a los usuarios tomar sus propias decisiones en consecuencia, obteniendo mejores resultados en comparación con otros algoritmos del estado del arte. iMOST se desarrolla mediante la integración de los algoritmos presentados, para ayudar de forma eficiente a los diseñadores en la planificación de WSNs fiables para diferentes configuraciones. El nombre abreviado iMOST (Intelligent Multi-objective Optimization Sensor network planning Tool) representa una herramienta inteligente de planificación de redes de sensores con optimización multi-objetivo. iMOST contribuye en: (1) Operación conveniente con una interfaz de fácil uso, (2) Reconstrucción eficiente y automática de una base de datos con información 3D y diseño rápido de objetos 3D para ambientes interiores y exteriores, (3) Proporciona varias soluciones de despliegue optimizadas para los multi-objetivo en 3D y permite a los usuarios configurar las propiedades de red, por lo que puede adaptarse a diversas aplicaciones de WSN, (4) las soluciones de implementación en el espacio 3D y el correspondiente rendimiento evaluado se presentan visualmente a los usuarios, y (5) El _Node Placement Module_de iMOST está disponible en línea, así como el código fuente de las otras dos heurísticas de planificación. Por lo tanto los diseñadores WSN se beneficiarán de esta herramienta para la construcción eficiente de la base de datos con información del entorno, la planificación práctica y eficiente de WSNs fiables tanto para aplicaciones interiores y exteriores. Con los códigos fuente abiertos, son capaces de comparar sus algoritmos desarrollados con los nuestros para contribuir a este campo académico. Por último, se obtienen resultados reales sólidos tanto para la planificación de WSN en interiores y exteriores. Los despliegues se han realizado tanto para ambientes de interior y como para ambientes de exterior utilizando las soluciones de planificación propuestas. Los resultados medidos coinciden en gran medida con los resultados estimados. El algoritmo de planificación x propuesto se adapta convenientemente al deiseño de redes de sensores inalámbricas, y ofrece flexibilidad para planificar los despliegues 3D a pequeña y gran escala tanto en interiores como en exteriores. La tesis se estructura en 7 capítulos. En el Capítulo 1, se presentan las aplicaciones de WSN y motivaciones de este trabajo, se revisan los algoritmos y herramientas de planificación del estado del arte, se presentan los retos y se describe brevemente la metodología propuesta. En el Capítulo 2, se presenta la metodología de reconstrucción de entornos 3D propuesta y su rendimiento es evaluado tanto para espacios exteriores como para espacios interiores. El motor de trazado de rayos desarrollado y el método de modelado de propagación de radio propuesto se describen en detalle en el Capítulo 3, evaluándose en términos de eficiencia computacional y precisión. En el Capítulo 4 se presenta el modelado de los parámetros importantes de las WSNs y el algoritmo de planificación de optimización multi-objetivo propuesto, el rendimiento se compara con los otros algoritmos de planificación descritos en el estado del arte. La herramienta inteligente de planificación de redes de sensores inalámbricas, iMOST, se describe en el Capítulo 5. En el Capítulo 6 se llevan a cabo despliegues reales de acuerdo a las soluciones previstas para los escenarios interiores y exteriores, se miden los datos importantes y se analizan los resultados. En el Capítulo 7 se concluye la tesis y se discute acerca de los trabajos futuros.