Uniformly reweighted belief propagation for distributed Bayesian hypothesis testing

Belief propagation (BP) is a technique for distributed inference in wireless networks and is often used even when the underlying graphical model contains cycles. In this paper, we propose a uniformly reweighted BP scheme that reduces the impact of cycles by weighting messages by a constant ?edge appearance probability? rho ? 1. We apply this algorithm to distributed binary hypothesis testing problems (e.g., distributed detection) in wireless networks with Markov random field models. We demonstrate that in the considered setting the proposed method outperforms standard BP, while maintaining similar complexity. We then show that the optimal ? can be approximated as a simple function of the average node degree, and can hence be computed in a distributed fashion through a consensus algorithm.

DCT Type-III for Multicarrier Modulation

In this paper we propose the use of Discrete Cosine Transform Type-III (DCT3) for multicarrier modulation. There are two DCT3 (even and odd) and, for each of them, we derive the expressions for both prefix and suffix to be appended into each data symbol to be transmitted. Moreover, DCT3 are closely related to the corresponding inverse DCT Type-II even and odd. Furthermore, we give explicit expressions for the 1-tap per subcarrier equalizers that must be implemented at the receiver to perform the channel equalization in the frequency-domain. As a result, the proposed DCT3-based multicarrier modulator can be used as an alternative to DFT-based systems to perform Orthogonal Frequency-Division Multiplexing or Discrete Multitone Modulation

A region based approach to background modeling in a wavelet multi-resolution framework

In the field of detection and monitoring of dynamic objects in quasi-static scenes, background subtraction techniques where background is modeled at pixel-level, although showing very significant limitations, are extensively used. In this work we propose a novel approach to background modeling that operates at region-level in a wavelet based multi-resolution framework. Based on a segmentation of the background, characterization is made for each region independently as a mixture of K Gaussian modes, considering the model of the approximation and detail coefficients at the different wavelet decomposition levels. Background region characterization is updated along time, and the detection of elements of interest is carried out computing the distance between background region models and those of each incoming image in the sequence. The inclusion of the context in the modeling scheme through each region characterization makes the model robust, being able to support not only gradual illumination and long-term changes, but also sudden illumination changes and the presence of strong shadows in the scene

Implementación de Algoritmos de Procesado de Señal sobre FPGA: Especificación, Reutilización y Exploración del Espacio de Diseño

Esta Tesis aborda el diseño e implementación de aplicaciones en el campo de procesado de señal, utilizando como plataforma los dispositivos reconfigurables FPGA. Esta plataforma muestra una alta capacidad de lógica, e incorpora elementos orientados al procesado de señal, que unido a su relativamente bajo coste, la hacen ideal para el desarrollo de aplicaciones de procesado de señal cuando se requiere realizar un procesado intensivo y se buscan unas altas prestaciones. Sin embargo, el coste asociado al desarrollo en estas plataformas es elevado. Mientras que el aumento en la capacidad lógica de los dispositivos FPGA permite el desarrollo de sistemas completos, los requisitos de altas prestaciones obligan a que en muchas ocasiones se deban optimizar operadores a muy bajo nivel. Además de las restricciones temporales que imponen este tipo de aplicaciones, también tienen asociadas restricciones de área asociadas al dispositivo, lo que obliga a evaluar y verificar entre diferentes alternativas de implementación. El ciclo de diseño e implementación para estas aplicaciones se puede prolongar tanto, que es normal que aparezcan nuevos modelos de FPGA, con mayor capacidad y mayor velocidad, antes de completar el sistema, y que hagan a las restricciones utilizadas para el diseño del sistema inútiles. Para mejorar la productividad en el desarrollo de estas aplicaciones, y con ello acortar su ciclo de diseño, se pueden encontrar diferentes métodos. Esta Tesis se centra en la reutilización de componentes hardware previamente diseñados y verificados. Aunque los lenguajes HDL convencionales permiten reutilizar componentes ya definidos, se pueden realizar mejoras en la especificación que simplifiquen el proceso de incorporar componentes a nuevos diseños. Así, una primera parte de la Tesis se orientará a la especificación de diseños basada en componentes predefinidos. Esta especificación no sólo busca mejorar y simplificar el proceso de añadir componentes a una descripción, sino que también busca mejorar la calidad del diseño especificado, ofreciendo una mayor posibilidad de configuración e incluso la posibilidad de informar de características de la propia descripción. Reutilizar una componente ya descrito depende en gran medida de la información que se ofrezca para su integración en un sistema. En este sentido los HDLs convencionales únicamente proporcionan junto con la descripción del componente la interfaz de entrada/ salida y un conjunto de parámetros para su configuración, mientras que el resto de información requerida normalmente se acompaña mediante documentación externa. En la segunda parte de la Tesis se propondrán un conjunto de encapsulados cuya finalidad es incorporar junto con la propia descripción del componente, información que puede resultar útil para su integración en otros diseños. Incluyendo información de la implementación, ayuda a la configuración del componente, e incluso información de cómo configurar y conectar al componente para realizar una función. Finalmente se elegirá una aplicación clásica en el campo de procesado de señal, la transformada rápida de Fourier (FFT), y se utilizará como ejemplo de uso y aplicación, tanto de las posibilidades de especificación como de los encapsulados descritos. El objetivo del diseño realizado no sólo mostrará ejemplos de la especificación propuesta, sino que también se buscará obtener una implementación de calidad comparable con resultados de la literatura. Para ello, el diseño realizado se orientará a su implementación en FPGA, aprovechando tanto los elementos lógicos generalistas como elementos específicos de bajo nivel disponibles en estos dispositivos. Finalmente, la especificación de la FFT obtenida se utilizará para mostrar cómo incorporar en su interfaz información que ayude para su selección y configuración desde fases tempranas del ciclo de diseño. Abstract This PhD. thesis addresses the design and implementation of signal processing applications using reconfigurable FPGA platforms. This kind of platform exhibits high logic capability, incorporates dedicated signal processing elements and provides a low cost solution, which makes it ideal for the development of signal processing applications, where intensive data processing is required in order to obtain high performance. However, the cost associated to the hardware development on these platforms is high. While the increase in logic capacity of FPGA devices allows the development of complete systems, high-performance constraints require the optimization of operators at very low level. In addition to time constraints imposed by these applications, Area constraints are also applied related to the particular device, which force to evaluate and verify a design among different implementation alternatives. The design and implementation cycle for these applications can be tedious and long, being therefore normal that new FPGA models with a greater capacity and higher speed appear before completing the system implementation. Thus, the original constraints which guided the design of the system become useless. Different methods can be used to improve the productivity when developing these applications, and consequently shorten their design cycle. This PhD. Thesis focuses on the reuse of hardware components previously designed and verified. Although conventional HDLs allow the reuse of components already defined, their specification can be improved in order to simplify the process of incorporating new design components. Thus, a first part of the PhD. Thesis will focus on the specification of designs based on predefined components. This specification improves and simplifies the process of adding components to a description, but it also seeks to improve the quality of the design specified with better configuration options and even offering to report on features of the description. Hardware reuse of a component for its integration into a system largely depends on the information it offers. In this sense the conventional HDLs only provide together with the component description, the input/output interface and a set of parameters for its configuration, while other information is usually provided by external documentation. In the second part of the Thesis we will propose a formal way of encapsulation which aims to incorporate with the component description information that can be useful for its integration into other designs. This information will include features of the own implementation, but it will also support component configuration, and even information on how to configure and connect the component to carry out a function. Finally, the fast Fourier transform (FFT) will be chosen as a well-known signal processing application. It will be used as case study to illustrate the possibilities of proposed specification and encapsulation formalisms. The objective of the FFT design is not only to show practical examples of the proposed specification, but also to obtain an implementation of a quality comparable to scientific literature results. The design will focus its implementation on FPGA platforms, using general logic elements as base of the implementation, but also taking advantage of low-level specific elements available on these devices. Last, the specification of the obtained FFT will be used to show how to incorporate in its interface information to assist in the selection and configuration process early in the design cycle.

The correlation between white-matter microstructure and the complexity of spontaneous brain activity: A difussion tensor imaging-MEG study

The advent of new signal processing methods, such as non-linear analysis techniques, represents a new perspective which adds further value to brain signals' analysis. Particularly, Lempel–Ziv's Complexity (LZC) has proven to be useful in exploring the complexity of the brain electromagnetic activity. However, an important problem is the lack of knowledge about the physiological determinants of these measures. Although acorrelation between complexity and connectivity has been proposed, this hypothesis was never tested in vivo. Thus, the correlation between the microstructure of the anatomic connectivity and the functional complexity of the brain needs to be inspected. In this study we analyzed the correlation between LZC and fractional anisotropy (FA), a scalar quantity derived from diffusion tensors that is particularly useful as an estimate of the functional integrity of myelinated axonal fibers, in a group of sixteen healthy adults (all female, mean age 65.56 ± 6.06 years, intervals 58–82). Our results showed a positive correlation between FA and LZC scores in regions including clusters in the splenium of the corpus callosum, cingulum, parahipocampal regions and the sagittal stratum. This study supports the notion of a positive correlation between the functional complexity of the brain and the microstructure of its anatomical connectivity. Our investigation proved that a combination of neuroanatomical and neurophysiological techniques may shed some light on the underlying physiological determinants of brain's oscillations

Two way clustering of Microarray Data using a Hybrid Approach

The Microarray technique is rather powerful, as it allows to test up thousands of genes at a time, but this produces an overwhelming set of data files containing huge amounts of data, which is quite difficult to pre-process, separate, classify and correlate for interesting conclusions to be extracted. Modern machine learning, data mining and clustering techniques based on information theory, are needed to read and interpret the information contents buried in those large data sets. Independent Component Analysis method can be used to correct the data affected by corruption processes or to filter the uncorrectable one and then clustering methods can group similar genes or classify samples. In this paper a hybrid approach is used to obtain a two way unsupervised clustering for a corrected microarray data.

Técnicas de procesado digital de señal aplicadas a acelerómetros

En este proyecto se estudian y analizan las diferentes técnicas de procesado digital de señal aplicadas a acelerómetros. Se hace uso de una tarjeta de prototipado, basada en DSP, para realizar las diferentes pruebas. El proyecto se basa, principalmente, en realizar filtrado digital en señales provenientes de un acelerómetro en concreto, el 1201F, cuyo campo de aplicación es básicamente la automoción. Una vez estudiadas la teoría de procesado y las características de los filtros, diseñamos una aplicación basándonos sobre todo en el entorno en el que se desarrollaría una aplicación de este tipo. A lo largo del diseño, se explican las diferentes fases: diseño por ordenador (Matlab), diseño de los filtros en el DSP (C), pruebas sobre el DSP sin el acelerómetro, calibración del acelerómetro, pruebas finales sobre el acelerómetro... Las herramientas utilizadas son: la plataforma Kit de evaluación 21-161N de Analog Devices (equipado con el entorno de desarrollo Visual DSP 4.5++), el acelerómetro 1201F, el sistema de calibración de acelerómetros CS-18-LF de Spektra y los programas software MATLAB 7.5 y CoolEditPRO 2.0. Se realizan únicamente filtros IIR de 2º orden, de todos los tipos (Butterworth, Chebyshev I y II y Elípticos). Realizamos filtros de banda estrecha, paso-banda y banda eliminada, de varios tipos, dentro del fondo de escala que permite el acelerómetro. Una vez realizadas todas las pruebas, tanto simulaciones como físicas, se seleccionan los filtros que presentan un mejor funcionamiento y se analizan para obtener conclusiones. Como se dispone de un entorno adecuado para ello, se combinan los filtros entre sí de varias maneras, para obtener filtros de mayor orden (estructura paralelo). De esta forma, a partir de filtros paso-banda, podemos obtener otras configuraciones que nos darán mayor flexibilidad. El objetivo de este proyecto no se basa sólo en obtener buenos resultados en el filtrado, sino también de aprovechar las facilidades del entorno y las herramientas de las que disponemos para realizar el diseño más eficiente posible. In this project, we study and analize digital signal processing in order to design an accelerometer-based application. We use a hardware card of evaluation, based on DSP, to make different tests. This project is based in design digital filters for an automotion application. The accelerometer type is 1201F. First, we study digital processing theory and main parameters of real filters, to make a design based on the application environment. Along the application, we comment all the different steps: computer design (Matlab), filter design on the DSP (C language), simulation test on the DSP without the accelerometer, accelerometer calibration, final tests on the accelerometer... Hardware and software tools used are: Kit of Evaluation 21-161-N, based on DSP, of Analog Devices (equiped with software development tool Visual DSP 4.5++), 1201-F accelerometer, CS-18-LF calibration system of SPEKTRA and software tools MATLAB 7.5 and CoolEditPRO 2.0. We only perform 2nd orden IIR filters, all-type : Butterworth, Chebyshev I and II and Ellyptics. We perform bandpass and stopband filters, with very narrow band, taking advantage of the accelerometer's full scale. Once all the evidence, both simulations and physical, are finished, filters having better performance and analyzed and selected to draw conclusions. As there is a suitable environment for it, the filters are combined together in different ways to obtain higher order filters (parallel structure). Thus, from band-pass filters, we can obtain many configurations that will give us greater flexibility. The purpose of this project is not only based on good results in filtering, but also to exploit the facilities of the environment and the available tools to make the most efficient design possible.

Análisis y clasificación de maderas mediante técnicas no invasivas de baja energía

El estudio de materiales, especialmente biológicos, por medios no destructivos está adquiriendo una importancia creciente tanto en las aplicaciones científicas como industriales. Las ventajas económicas de los métodos no destructivos son múltiples. Existen numerosos procedimientos físicos capaces de extraer información detallada de las superficie de la madera con escaso o nulo tratamiento previo y mínima intrusión en el material. Entre los diversos métodos destacan las técnicas ópticas y las acústicas por su gran versatilidad, relativa sencillez y bajo coste. Esta tesis pretende establecer desde la aplicación de principios simples de física, de medición directa y superficial, a través del desarrollo de los algoritmos de decisión mas adecuados basados en la estadística, unas soluciones tecnológicas simples y en esencia, de coste mínimo, para su posible aplicación en la determinación de la especie y los defectos superficiales de la madera de cada muestra tratando, en la medida de lo posible, no alterar su geometría de trabajo. Los análisis desarrollados han sido los tres siguientes: El primer método óptico utiliza las propiedades de la luz dispersada por la superficie de la madera cuando es iluminada por un laser difuso. Esta dispersión produce un moteado luminoso (speckle) cuyas propiedades estadísticas permiten extraer propiedades muy precisas de la estructura tanto microscópica como macroscópica de la madera. El análisis de las propiedades espectrales de la luz laser dispersada genera ciertos patrones mas o menos regulares relacionados con la estructura anatómica, composición, procesado y textura superficial de la madera bajo estudio que ponen de manifiesto características del material o de la calidad de los procesos a los que ha sido sometido. El uso de este tipo de láseres implica también la posibilidad de realizar monitorizaciones de procesos industriales en tiempo real y a distancia sin interferir con otros sensores. La segunda técnica óptica que emplearemos hace uso del estudio estadístico y matemático de las propiedades de las imágenes digitales obtenidas de la superficie de la madera a través de un sistema de scanner de alta resolución. Después de aislar los detalles mas relevantes de las imágenes, diversos algoritmos de clasificacion automatica se encargan de generar bases de datos con las diversas especies de maderas a las que pertenecían las imágenes, junto con los márgenes de error de tales clasificaciones. Una parte fundamental de las herramientas de clasificacion se basa en el estudio preciso de las bandas de color de las diversas maderas. Finalmente, numerosas técnicas acústicas, tales como el análisis de pulsos por impacto acústico, permiten complementar y afinar los resultados obtenidos con los métodos ópticos descritos, identificando estructuras superficiales y profundas en la madera así como patologías o deformaciones, aspectos de especial utilidad en usos de la madera en estructuras. La utilidad de estas técnicas esta mas que demostrada en el campo industrial aun cuando su aplicación carece de la suficiente expansión debido a sus altos costes y falta de normalización de los procesos, lo cual hace que cada análisis no sea comparable con su teórico equivalente de mercado. En la actualidad gran parte de los esfuerzos de investigación tienden a dar por supuesto que la diferenciación entre especies es un mecanismo de reconocimiento propio del ser humano y concentran las tecnologías en la definición de parámetros físicos (módulos de elasticidad, conductividad eléctrica o acústica, etc.), utilizando aparatos muy costosos y en muchos casos complejos en su aplicación de campo. Abstract The study of materials, especially the biological ones, by non-destructive techniques is becoming increasingly important in both scientific and industrial applications. The economic advantages of non-destructive methods are multiple and clear due to the related costs and resources necessaries. There are many physical processes capable of extracting detailed information on the wood surface with little or no previous treatment and minimal intrusion into the material. Among the various methods stand out acoustic and optical techniques for their great versatility, relative simplicity and low cost. This thesis aims to establish from the application of simple principles of physics, surface direct measurement and through the development of the more appropriate decision algorithms based on statistics, a simple technological solutions with the minimum cost for possible application in determining the species and the wood surface defects of each sample. Looking for a reasonable accuracy without altering their work-location or properties is the main objetive. There are three different work lines: Empirical characterization of wood surfaces by means of iterative autocorrelation of laser speckle patterns: A simple and inexpensive method for the qualitative characterization of wood surfaces is presented. it is based on the iterative autocorrelation of laser speckle patterns produced by diffuse laser illumination of the wood surfaces. The method exploits the high spatial frequency content of speckle images. A similar approach with raw conventional photographs taken with ordinary light would be very difficult. A few iterations of the algorithm are necessary, typically three or four, in order to visualize the most important periodic features of the surface. The processed patterns help in the study of surface parameters, to design new scattering models and to classify the wood species. Fractal-based image enhancement techniques inspired by differential interference contrast microscopy: Differential interference contrast microscopy is a very powerful optical technique for microscopic imaging. Inspired by the physics of this type of microscope, we have developed a series of image processing algorithms aimed at the magnification, noise reduction, contrast enhancement and tissue analysis of biological samples. These algorithms use fractal convolution schemes which provide fast and accurate results with a performance comparable to the best present image enhancement algorithms. These techniques can be used as post processing tools for advanced microscopy or as a means to improve the performance of less expensive visualization instruments. Several examples of the use of these algorithms to visualize microscopic images of raw pine wood samples with a simple desktop scanner are provided. Wood species identification using stress-wave analysis in the audible range: Stress-wave analysis is a powerful and flexible technique to study mechanical properties of many materials. We present a simple technique to obtain information about the species of wood samples using stress-wave sounds in the audible range generated by collision with a small pendulum. Stress-wave analysis has been used for flaw detection and quality control for decades, but its use for material identification and classification is less cited in the literature. Accurate wood species identification is a time consuming task for highly trained human experts. For this reason, the development of cost effective techniques for automatic wood classification is a desirable goal. Our proposed approach is fully non-invasive and non-destructive, reducing significantly the cost and complexity of the identification and classification process.

An approach to operational modal analysis using the expectation maximization algorithm

This paper presents the Expectation Maximization algorithm (EM) applied to operational modal analysis of structures. The EM algorithm is a general-purpose method for maximum likelihood estimation (MLE) that in this work is used to estimate state space models. As it is well known, the MLE enjoys some optimal properties from a statistical point of view, which make it very attractive in practice. However, the EM algorithm has two main drawbacks: its slow convergence and the dependence of the solution on the initial values used. This paper proposes two different strategies to choose initial values for the EM algorithm when used for operational modal analysis: to begin with the parameters estimated by Stochastic Subspace Identification method (SSI) and to start using random points. The effectiveness of the proposed identification method has been evaluated through numerical simulation and measured vibration data in the context of a benchmark problem. Modal parameters (natural frequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using SSI and the EM algorithm. On the whole, the results show that the application of the EM algorithm starting from the solution given by SSI is very useful to identify the vibration modes of a structure, discarding the spurious modes that appear in high order models and discovering other hidden modes. Similar results are obtained using random starting values, although this strategy allows us to analyze the solution of several starting points what overcome the dependence on the initial values used.

Modal contribution and state space order selection in operational modal analysis

The estimation of modal parameters of a structure from ambient measurements has attracted the attention of many researchers in the last years. The procedure is now well established and the use of state space models, stochastic system identification methods and stabilization diagrams allows to identify the modes of the structure. In this paper the contribution of each identified mode to the measured vibration is discussed. This modal contribution is computed using the Kalman filter and it is an indicator of the importance of the modes. Also the variation of the modal contribution with the order of the model is studied. This analysis suggests selecting the order for the state space model as the order that includes the modes with higher contribution. The order obtained using this method is compared to those obtained using other well known methods, like Akaike criteria for time series or the singular values of the weighted projection matrix in the Stochastic Subspace Identification method. Finally, both simulated and measured vibration data are used to show the practicability of the derived technique. Finally, it is important to remark that the method can be used with any identification method working in the state space model.

On the use of discrete cosine transforms for multicarrier communications

In this correspondence, the conditions to use any kind of discrete cosine transform (DCT) for multicarrier data transmission are derived. The symmetric convolution-multiplication property of each DCT implies that when symmetric convolution is performed in the time domain, an element-by-element multiplication is performed in the corresponding discrete trigonometric domain. Therefore, appending symmetric redun-dancy (as prefix and suffix) into each data symbol to be transmitted, and also enforcing symmetry for the equivalent channel impulse response, the linear convolution performed in the transmission channel becomes a symmetric convolution in those samples of interest. Furthermore, the channel equalization can be carried out by means of a bank of scalars in the corresponding discrete cosine transform domain. The expressions for obtaining the value of each scalar corresponding to these one-tap per subcarrier equalizers are presented. This study is completed with several computer simulations in mobile broadband wireless communication scenarios, considering the presence of carrier frequency offset (CFO). The obtained results indicate that the proposed systems outperform the standardized ones based on the DFT.

Analytical evaluation of uncertainty on active antenna arrays

An analytical method for evaluating the uncertainty of the performance of active antenna arrays in the whole spatial spectrum is presented. Since array processing algorithms based on spatial reference are widely used to track moving targets, it is essential to be aware of the impact of the uncertainty sources on the antenna response. Furthermore, the estimation of the direction of arrival (DOA) depends on the array uncertainty. The aim of the uncertainties analysis is to provide an exhaustive characterization of the behavior of the active antenna array associated with its main uncertainty sources. The result of this analysis helps to select the proper calibration technique to be implemented. An illustrative example for a triangular antenna array used for satellite tracking is presented showing the suitability of the proposed method to carry out an efficient characterization of an active antenna array.

Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.

This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time?frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%.The results open the possibility to extrapolate the methodology to the study of other biosignals.

High Performance FPGA-oriented mersenne twister uniform random number generator

Mersenne Twister (MT) uniform random number generators are key cores for hardware acceleration of Monte Carlo simulations. In this work, two different architectures are studied: besides the classical table-based architecture, a different architecture based on a circular buffer and especially targeting FPGAs is proposed. A 30% performance improvement has been obtained when compared to the fastest previous work. The applicability of the proposed MT architectures has been proven in a high performance Gaussian RNG.

GreenDisc: a HW/SW energy optimization framework in globally distributed computation

In recent future, wireless sensor networks (WSNs) will experience a broad high-scale deployment (millions of nodes in the national area) with multiple information sources per node, and with very specific requirements for signal processing. In parallel, the broad range deployment of WSNs facilitates the definition and execution of ambitious studies, with a large input data set and high computational complexity. These computation resources, very often heterogeneous and driven on-demand, can only be satisfied by high-performance Data Centers (DCs). The high economical and environmental impact of the energy consumption in DCs requires aggressive energy optimization policies. These policies have been already detected but not successfully proposed. In this context, this paper shows the following on-going research lines and obtained results. In the field of WSNs: energy optimization in the processing nodes from different abstraction levels, including reconfigurable application specific architectures, efficient customization of the memory hierarchy, energy-aware management of the wireless interface, and design automation for signal processing applications. In the field of DCs: energy-optimal workload assignment policies in heterogeneous DCs, resource management policies with energy consciousness, and efficient cooling mechanisms that will cooperate in the minimization of the electricity bill of the DCs that process the data provided by the WSNs.