973 resultados para INTERACTION NETWORKS
Resumo:
It is currently widely accepted that the understanding of complex cell functions depends on an integrated network theoretical approach and not on an isolated view of the different molecular agents. Aim of this thesis was the examination of topological properties that mirror known biological aspects by depicting the human protein network with methods from graph- and network theory. The presented network is a partial human interactome of 9222 proteins and 36324 interactions, consisting of single interactions reliably extracted from peer-reviewed scientific publications. In general, one can focus on intra- or intermodular characteristics, where a functional module is defined as "a discrete entity whose function is separable from those of other modules". It is found that the presented human network is also scale-free and hierarchically organised, as shown for yeast networks before. The interactome also exhibits proteins with high betweenness and low connectivity which are biologically analyzed and interpreted here as shuttling proteins between organelles (e.g. ER to Golgi, internal ER protein translocation, peroxisomal import, nuclear pores import/export) for the first time. As an optimisation for finding proteins that connect modules, a new method is developed here based on proteins located between highly clustered regions, rather than regarding highly connected regions. As a proof of principle, the Mediator complex is found in first place, the prime example for a connector complex. Focusing on intramodular aspects, the measurement of k-clique communities discriminates overlapping modules very well. Twenty of the largest identified modules are analysed in detail and annotated to known biological structures (e.g. proteasome, the NFκB-, TGF-β complex). Additionally, two large and highly interconnected modules for signal transducer and transcription factor proteins are revealed, separated by known shuttling proteins. These proteins yield also the highest number of redundant shortcuts (by calculating the skeleton), exhibit the highest numbers of interactions and might constitute highly interconnected but spatially separated rich-clubs either for signal transduction or for transcription factors. This design principle allows manifold regulatory events for signal transduction and enables a high diversity of transcription events in the nucleus by a limited set of proteins. Altogether, biological aspects are mirrored by pure topological features, leading to a new view and to new methods that assist the annotation of proteins to biological functions, structures and subcellular localisations. As the human protein network is one of the most complex networks at all, these results will be fruitful for other fields of network theory and will help understanding complex network functions in general.
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n the last few years, the vision of our connected and intelligent information society has evolved to embrace novel technological and research trends. The diffusion of ubiquitous mobile connectivity and advanced handheld portable devices, amplified the importance of the Internet as the communication backbone for the fruition of services and data. The diffusion of mobile and pervasive computing devices, featuring advanced sensing technologies and processing capabilities, triggered the adoption of innovative interaction paradigms: touch responsive surfaces, tangible interfaces and gesture or voice recognition are finally entering our homes and workplaces. We are experiencing the proliferation of smart objects and sensor networks, embedded in our daily living and interconnected through the Internet. This ubiquitous network of always available interconnected devices is enabling new applications and services, ranging from enhancements to home and office environments, to remote healthcare assistance and the birth of a smart environment. This work will present some evolutions in the hardware and software development of embedded systems and sensor networks. Different hardware solutions will be introduced, ranging from smart objects for interaction to advanced inertial sensor nodes for motion tracking, focusing on system-level design. They will be accompanied by the study of innovative data processing algorithms developed and optimized to run on-board of the embedded devices. Gesture recognition, orientation estimation and data reconstruction techniques for sensor networks will be introduced and implemented, with the goal to maximize the tradeoff between performance and energy efficiency. Experimental results will provide an evaluation of the accuracy of the presented methods and validate the efficiency of the proposed embedded systems.
Resumo:
Gels are elastic porous polymer networks that are accompanied by pronounced mechanical properties. Due to their biocompatibility, ‘responsive hydrogels’ (HG) have many biomedical applications ranging from biosensors and drug delivery to tissue engineering. They respond to external stimuli such as temperature and salt by changing their dimensions. Of paramount importance is the ability to engineer penetrability and diffusion of interacting molecules in the crowded HG environment, as this would enable one to optimize a specific functionality. Even though the conditions under which biomedical devices operate are rather complex, a bottom-up approach could reduce the complexity of mutually coupled parameters influencing tracer mobility. The present thesis focuses on the interaction-induced tracer diffusion in polymer solutions and their homologous gels, probed by means of Fluorescence Correlation Spectroscopy (FCS). This is a single-molecule-sensitive technique having the advantage of optimal performance under ultralow tracer concentrations, typically employed in biosensors. Two different types of hydrogels have been investigated, a conventional one with broad polydispersity in the distance between crosslink points and a so-called ‘ideal’, with uniform mesh size distribution. The former is based on a thermoresponsive polymer, exhibiting phase separation in water at temperatures close to the human body temperature. The latter represents an optimal platform to study tracer diffusion. Mobilities of different tracers have been investigated in each network, varying in size, geometry and in terms of tracer-polymer attractive strength, as perturbed by different stimuli. The thesis constitutes a systematic effort towards elucidating the role of the strength and nature of different tracer-polymer interactions, on tracer mobilities; it outlines that interactions can still be very important even in the simplified case of dilute polymer solutions; it also demonstrates that the presence of permanent crosslinks exerts distinct tracer slowdown, depending on the tracer type and the nature of the tracer-polymer interactions, expressed differently by each tracer with regard to the selected stimulus. In aqueous polymer solutions, the tracer slowdown is found to be system-dependent and no universal trend seems to hold, in contrast to predictions from scaling theory for non-interacting nanoparticle mobility and empirical relations concerning the mesh size in polymer solutions. Complex tracer dynamics in polymer networks may be distinctly expressed by FCS, depending on the specific synergy among-at least some of - the following parameters: nature of interactions, external stimuli employed, tracer size and type, crosslink density and swelling ratio.
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Open collaborative projects are moving to the foreground of knowledge production. Some online user communities develop into longterm projects that generate a highly valuable and at the same time freely accessible output. Traditional copyright law that is organized around the idea of a single creative entity is not well equipped to accommodate the needs of these forms of collaboration. In order to enable a peculiar network-type of interaction participants instead draw on public licensing models that determine the freedoms to use individual contributions. With the help of these access rules the operational logic of the project can be implemented successfully. However, as the case of the Wikipedia GFDL-CC license transition demonstrates, the adaptation of access rules in networks to new circumstances raises collective action problems and suffers from pitfalls caused by the fact that public licensing is grounded in individual copyright. Legal governance of open collaboration projects is a largely unexplored field. The article argues that the license steward of a public license assumes the position of a fiduciary of the knowledge commons generated under the license regime. Ultimately, the governance of decentralized networks translates into a composite of organizational and contractual elements. It is concluded that the production of global knowledge commons relies on rules of transnational private law.
Resumo:
Plectin is a versatile cytolinker of the plakin family conferring cell resilience to mechanical stress in stratified epithelia and muscles. It acts as a critical organizer of the cytoskeletal system by tethering various intermediate filament (IF) networks through its C-terminal IF-binding domain (IFBD). Mutations affecting the IFBD cause devastating human diseases. Here, we show that serine 4642, which is located in the extreme C-terminus of plectin, is phosphorylated in different cell lines. Phosphorylation of S4642 decreased the ability of plectin IFBD to associate with various IFs, as assessed by immunofluorescence microscopy and cell fractionation studies, as well as in yeast two-hybrid assays. Plectin phosphorylated at S4642 was reduced at sites of IF network anchorage along cell-substrate contacts in both skin and cultured keratinocytes. Treatment of SK-MEL-2 and HeLa cells with okadaic acid increased plectin S4642 phosphorylation, suggesting that protein phosphatase 2A dephosphorylates this residue. Moreover, plectin S4642 phosphorylation was enhanced after cell treatment with EGF, phorbol ester, sorbitol and 8-bromo-cyclic AMP, as well as during wound healing and protease-mediated cell detachment. Using selective protein kinase inhibitors, we identified two different kinases that modulate the phosphorylation of plectin S4642 in HeLa cells: MNK2, which is downstream of the ERK1/2-dependent MAPK cascade, and PKA. Our study indicates that phosphorylation of S4642 has an important regulatory role in the interaction of plectin with IFs and identifies a novel link between MNK2 and the cytoskeleton.
Resumo:
Excavations of Neolithic (4000 – 3500 BC) and Late Bronze Age (1200 – 800 BC) wetland sites on the northern Alpine periphery have produced astonishing and detailed information about the life and human environment of prehistoric societies. It is even possible to reconstruct settlement histories and settlement dynamics, which suggest a high degree of mobility during the Neolithic. Archaeological finds—such as pottery—show local typological developments in addition to foreign influences. Furthermore, exogenous lithic forms indicate far reaching interaction. Many hundreds of bronze artefacts are recorded from the Late Bronze Age settlements, demonstrating that some wetland sites were centres of bronzework production. Exogenous forms of bronzework are relatively rare in the wetland settlements during the Late Bronze Age. However, the products produced in the lake-settlements can be found widely across central Europe, indicating their continued involvement in interregional exchange partnerships. Potential motivations and dynamics of the relationships between sites and other regions of Europe will be detailed using case studies focussing on the settlements Seedorf Lobsigensee (BE), Concise (VD), and Sutz-Lattrigen Hauptstation innen (BE), and an initial assessment of intra-site connectivity through Network Analysis of sites within the region of Lake Neuchâtel, Lake Biel, and Lake Murten.
Resumo:
The numerical simulations of the magnetic properties of extended three-dimensional networks containing M(II) ions with an S = 5/2 ground-state spin have been carried out within the framework of the isotropic Heisenberg model. Analytical expressions fitting the numerical simulations for the primitive cubic, diamond, together with (10−3) cubic networks have all been derived. With these empirical formulas in hands, we can now extract the interaction between the magnetic ions from the experimental data for these networks. In the case of the primitive cubic network, these expressions are directly compared with those from the high-temperature expansions of the partition function. A fit of the experimental data for three complexes, namely [(N(CH3)4][Mn(N3)] 1, [Mn(CN4)]n 2, and [FeII(bipy)3][MnII2(ox)3] 3, has been carried out. The best fits were those obtained using the following parameters, J = −3.5 cm-1, g = 2.01 (1); J = −8.3 cm-1, g = 1.95 (2); and J = −2.0 cm-1, g = 1.95 (3).
Resumo:
The field of molecule-based magnets is a relatively new branch of chemistry, which involves the design and study of molecular compounds that exhibit a spontaneous magnetic ordering below a critical temperature, Tc. One major goal involves the design of materials with tuneable Tc's for specific applications in memory storage devices. Molecule-based magnets with high magnetic ordering temperatures have recently been obtained from bimetallic and mixed-valence transition metal μ-cyanide complexes of the Prussian blue family. Since the μ-cyanide linkages permit an interaction between paramagnetic metal ions, cyanometalate building blocks have found useful applications in the field of molecule-based magnets. Our work involves the use of octacyanometalate building blocks for the self-assembly of two new classes of magnetic materials namely, high-spin molecular clusters which exhibit both ferromagnetic intra- and intercluster coupling, and specific extended network topologies which show long-range ferromagnetic ordering.
Resumo:
Luminescence and energy transfer in [Zn1-xRux(bpy)3][NaAl1-yCry(ox)3] (x ≈ 0.01, y = 0.006 − 0.22; bpy = 2,2‘-bipyridine, ox = C2O42-) and [Zn1-x-yRuxOsy(bpy)3][NaAl(ox)3] (x ≈ 0.01, y = 0.012) are presented and discussed. Surprisingly, the luminescence of the isolated luminophores [Ru(bpy)3]2+ and [Os(bpy)3]2+ in [Zn(bpy)3][NaAl(ox)3] is hardly quenched at room temperature. Steady-state luminescence spectra and decay curves show that energy transfer occurs between [Ru(bpy)3]2+ and [Cr(ox)3]3- and between [Ru(bpy)3]2+ and [Os(bpy)3]2+ in [Zn1-xRux(bpy)3][NaAl1-yCry(ox)3] and [Zn1-x-yRuxOsy(bpy)3] [NaAl(ox)3], respectively. For a quantitative investigation of the energy transfer, a shell type model is developed, using a Monte Carlo procedure and the structural parameters of the systems. A good description of the experimental data is obtained assuming electric dipole−electric dipole interaction between donors and acceptors, with a critical distance Rc for [Ru(bpy)3]2+ to [Cr(ox)3]3- energy transfer of 15 Å and for [Ru(bpy)3]2+ to [Os(bpy)3]2+ energy transfer of 33 Å. These values are in good agreement with those derived using the Förster−Dexter theory.
Resumo:
The aim is to obtain computationally more powerful, neuro physiologically founded, artificial neurons and neural nets. Artificial Neural Nets (ANN) of the Perceptron type evolved from the original proposal by McCulloch an Pitts classical paper [1]. Essentially, they keep the computing structure of a linear machine followed by a non linear operation. The McCulloch-Pitts formal neuron (which was never considered by the author’s to be models of real neurons) consists of the simplest case of a linear computation of the inputs followed by a threshold. Networks of one layer cannot compute anylogical function of the inputs, but only those which are linearly separable. Thus, the simple exclusive OR (contrast detector) function of two inputs requires two layers of formal neurons
Resumo:
Self-consciousness implies not only self or group recognition, but also real knowledge of one’s own identity. Self-consciousness is only possible if an individual is intelligent enough to formulate an abstract self-representation. Moreover, it necessarily entails the capability of referencing and using this elf-representation in connection with other cognitive features, such as inference, and the anticipation of the consequences of both one’s own and other individuals’ acts. In this paper, a cognitive architecture for self-consciousness is proposed. This cognitive architecture includes several modules: abstraction, self-representation, other individuals'representation, decision and action modules. It includes a learning process of self-representation by direct (self-experience based) and observational learning (based on the observation of other individuals). For model implementation a new approach is taken using Modular Artificial Neural Networks (MANN). For model testing, a virtual environment has been implemented. This virtual environment can be described as a holonic system or holarchy, meaning that it is composed of autonomous entities that behave both as a whole and as part of a greater whole. The system is composed of a certain number of holons interacting. These holons are equipped with cognitive features, such as sensory perception, and a simplified model of personality and self-representation. We explain holons’ cognitive architecture that enables dynamic self-representation. We analyse the effect of holon interaction, focusing on the evolution of the holon’s abstract self-representation. Finally, the results are explained and analysed and conclusions drawn.
Resumo:
In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease. The structural analysis of the resulting network can then be used to feed standard data-mining algorithms, for instance for the classification of new (unlabeled) subjects. Furthermore, we show how the structure of the network is resilient to the presence of external additive noise, and how it can be used to extract relevant knowledge about the development of the disease.
Resumo:
Cuando una colectividad de sistemas dinámicos acoplados mediante una estructura irregular de interacciones evoluciona, se observan dinámicas de gran complejidad y fenómenos emergentes imposibles de predecir a partir de las propiedades de los sistemas individuales. El objetivo principal de esta tesis es precisamente avanzar en nuestra comprensión de la relación existente entre la topología de interacciones y las dinámicas colectivas que una red compleja es capaz de mantener. Siendo este un tema amplio que se puede abordar desde distintos puntos de vista, en esta tesis se han estudiado tres problemas importantes dentro del mismo que están relacionados entre sí. Por un lado, en numerosos sistemas naturales y artificiales que se pueden describir mediante una red compleja la topología no es estática, sino que depende de la dinámica que se desarrolla en la red: un ejemplo son las redes de neuronas del cerebro. En estas redes adaptativas la propia topología emerge como consecuencia de una autoorganización del sistema. Para conocer mejor cómo pueden emerger espontáneamente las propiedades comúnmente observadas en redes reales, hemos estudiado el comportamiento de sistemas que evolucionan según reglas adaptativas locales con base empírica. Nuestros resultados numéricos y analíticos muestran que la autoorganización del sistema da lugar a dos de las propiedades más universales de las redes complejas: a escala mesoscópica, la aparición de una estructura de comunidades, y, a escala macroscópica, la existencia de una ley de potencias en la distribución de las interacciones en la red. El hecho de que estas propiedades aparecen en dos modelos con leyes de evolución cuantitativamente distintas que siguen unos mismos principios adaptativos sugiere que estamos ante un fenómeno que puede ser muy general, y estar en el origen de estas propiedades en sistemas reales. En segundo lugar, proponemos una medida que permite clasificar los elementos de una red compleja en función de su relevancia para el mantenimiento de dinámicas colectivas. En concreto, estudiamos la vulnerabilidad de los distintos elementos de una red frente a perturbaciones o grandes fluctuaciones, entendida como una medida del impacto que estos acontecimientos externos tienen en la interrupción de una dinámica colectiva. Los resultados que se obtienen indican que la vulnerabilidad dinámica es sobre todo dependiente de propiedades locales, por tanto nuestras conclusiones abarcan diferentes topologías, y muestran la existencia de una dependencia no trivial entre la vulnerabilidad y la conectividad de los elementos de una red. Finalmente, proponemos una estrategia de imposición de una dinámica objetivo genérica en una red dada e investigamos su validez en redes con diversas topologías que mantienen regímenes dinámicos turbulentos. Se obtiene como resultado que las redes heterogéneas (y la amplia mayora de las redes reales estudiadas lo son) son las más adecuadas para nuestra estrategia de targeting de dinámicas deseadas, siendo la estrategia muy efectiva incluso en caso de disponer de un conocimiento muy imperfecto de la topología de la red. Aparte de la relevancia teórica para la comprensión de fenómenos colectivos en sistemas complejos, los métodos y resultados propuestos podrán dar lugar a aplicaciones en sistemas experimentales y tecnológicos, como por ejemplo los sistemas neuronales in vitro, el sistema nervioso central (en el estudio de actividades síncronas de carácter patológico), las redes eléctricas o los sistemas de comunicaciones. ABSTRACT The time evolution of an ensemble of dynamical systems coupled through an irregular interaction scheme gives rise to dynamics of great of complexity and emergent phenomena that cannot be predicted from the properties of the individual systems. The main objective of this thesis is precisely to increase our understanding of the interplay between the interaction topology and the collective dynamics that a complex network can support. This is a very broad subject, so in this thesis we will limit ourselves to the study of three relevant problems that have strong connections among them. First, it is a well-known fact that in many natural and manmade systems that can be represented as complex networks the topology is not static; rather, it depends on the dynamics taking place on the network (as it happens, for instance, in the neuronal networks in the brain). In these adaptive networks the topology itself emerges from the self-organization in the system. To better understand how the properties that are commonly observed in real networks spontaneously emerge, we have studied the behavior of systems that evolve according to local adaptive rules that are empirically motivated. Our numerical and analytical results show that self-organization brings about two of the most universally found properties in complex networks: at the mesoscopic scale, the appearance of a community structure, and, at the macroscopic scale, the existence of a power law in the weight distribution of the network interactions. The fact that these properties show up in two models with quantitatively different mechanisms that follow the same general adaptive principles suggests that our results may be generalized to other systems as well, and they may be behind the origin of these properties in some real systems. We also propose a new measure that provides a ranking of the elements in a network in terms of their relevance for the maintenance of collective dynamics. Specifically, we study the vulnerability of the elements under perturbations or large fluctuations, interpreted as a measure of the impact these external events have on the disruption of collective motion. Our results suggest that the dynamic vulnerability measure depends largely on local properties (our conclusions thus being valid for different topologies) and they show a non-trivial dependence of the vulnerability on the connectivity of the network elements. Finally, we propose a strategy for the imposition of generic goal dynamics on a given network, and we explore its performance in networks with different topologies that support turbulent dynamical regimes. It turns out that heterogeneous networks (and most real networks that have been studied belong in this category) are the most suitable for our strategy for the targeting of desired dynamics, the strategy being very effective even when the knowledge on the network topology is far from accurate. Aside from their theoretical relevance for the understanding of collective phenomena in complex systems, the methods and results here discussed might lead to applications in experimental and technological systems, such as in vitro neuronal systems, the central nervous system (where pathological synchronous activity sometimes occurs), communication systems or power grids.
Resumo:
A new method to study large scale neural networks is presented in this paper. The basis is the use of Feynman- like diagrams. These diagrams allow the analysis of collective and cooperative phenomena with a similar methodology to the employed in the Many Body Problem. The proposed method is applied to a very simple structure composed by an string of neurons with interaction among them. It is shown that a new behavior appears at the end of the row. This behavior is different to the initial dynamics of a single cell. When a feedback is present, as in the case of the hippocampus, this situation becomes more complex with a whole set of new frequencies, different from the proper frequencies of the individual neurons. Application to an optical neural network is reported.
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.