2,677 research outputs found

    Multilayer probability extreme learning machine for device-free localization

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    Device-free localization (DFL) is becoming one of the new techniques in wireless localization field, due to its advantage that the target to be localized does not need to attach any electronic device. One of the key issues of DFL is how to characterize the influence of the target on the wireless links, such that the target’s location can be accurately estimated by analyzing the changes of the signals of the links. Most of the existing related research works usually extract the useful information from the links through manual approaches, which are labor-intensive and time-consuming. Deep learning approaches have attempted to automatically extract the useful information from the links, but the training of the conventional deep learning approaches are time-consuming, because a large number of parameters need to be fine-tuned multiple times. Motivated by the fast learning speed and excellent generalization performance of extreme learning machine (ELM), which is an emerging training approach for generalized single hidden layer feedforward neural networks (SLFNs), this paper proposes a novel hierarchical ELM based on deep learning theory, named multilayer probability ELM (MP-ELM), for automatically extracting the useful information from the links, and implementing fast and accurate DFL. The proposed MP-ELM is stacked by ELM autoencoders, so it also keeps the very fast learning speed of ELM. In addition, considering the uncertainty and redundant links existing in DFL, MP-ELM outputs the probabilistic estimation of the target’s location instead of the deterministic output. The validity of the proposed MP-ELM-based DFL is evaluated both in the indoor and the outdoor environments, respectively. Experimental results demonstrate that the proposed MP-ELM can obtain better performance compared with classic ELM, multilayer ELM (ML-ELM), hierarchical ELM (H-ELM), deep belief network (DBN), and deep Boltzmann machine (DBM)

    Autonomous and reliable operation of multilayer optical networks

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    This Ph.D. thesis focuses on the reliable autonomous operation of multilayer optical networks. The first objective focuses on the reliability of the optical network and proposes methods for health analysis related to Quality of Transmission (QoT) degradation. Such degradation is produced by soft-failures in optical devices and fibers in core and metro segments of the operators’ transport networks. Here, we compare estimated and measured QoT in the optical transponder by using a QoT tool based on GNPy. We show that the changes in the values of input parameters of the QoT model representing optical devices can explain the deviations and degradation in performance of such devices. We use reverse engineering to estimate the value of those parameters that explain the observed QoT. We show by simulation a large anticipation in soft-failure detection, localization and identification of degradation before affecting the network. Finally, for validating our approach, we experimentally observe the high accuracy in the estimation of the modeling parameters. The second objective focuses on multilayer optical networks, where lightpaths are used to connect packet nodes thus creating virtual links (vLink). Specifically, we study how lightpaths can be managed to provide enough capacity to the packet layer without detrimental effects in their Quality of Service (QoS), like added delays or packet losses, and at the same time minimize energy consumption. Such management must be as autonomous as possible to minimize human intervention. We study the autonomous operation of optical connections based on digital subcarrier multiplexing (DSCM). We propose several solutions for the autonomous operation of DSCM systems. In particular, the combination of two modules running in the optical node and in the optical transponder activate and deactivate subcarriers to adapt the capacity of the optical connection to the upper layer packet traffic. The module running in the optical node is part of our Intent-based Networking (IBN) solution and implements prediction to anticipate traffic changes. Our comprehensive study demonstrates the feasibility of DSCM autonomous operation and shows large cost savings in terms of energy consumption. In addition, our study provides a guideline to help vendors and operators to adopt the proposed solutions. The final objective targets at automating packet layer connections (PkC). Automating the capacity required by PkCs can bring further cost reduction to network operators, as it can limit the resources used at the optical layer. However, such automation requires careful design to avoid any QoS degradation, which would impact Service Level Agreement (SLA) in the case that the packet flow is related to some customer connection. We study autonomous packet flow capacity management. We apply RL techniques and propose a management lifecycle consisting of three different phases: 1) a self-tuned threshold-based approach for setting up the connection until enough data is collected, which enables understanding the traffic characteristics; 2) RL operation based on models pre-trained with generic traffic profiles; and 3) RL operation based on models trained with the observed traffic. We show that RL algorithms provide poor performance until they learn optimal policies, as well as when the traffic characteristics change over time. The proposed lifecycle provides remarkable performance from the starting of the connection and it shows the robustness while facing changes in traffic. The contribution is twofold: 1) and on the one hand, we propose a solution based on RL, which shows superior performance with respect to the solution based on prediction; and 2) because vLinks support packet connections, coordination between the intents of both layers is proposed. In this case, the actions taken by the individual PkCs are used by the vLink intent. The results show noticeable performance compared to independent vLink operation.Esta tesis doctoral se centra en la operación autónoma y confiable de redes ópticas multicapa. El primer objetivo se centra en la fiabilidad de la red óptica y propone métodos para el análisis del estado relacionados con la degradación de la calidad de la transmisión (QoT). Dicha degradación se produce por fallos en dispositivos ópticos y fibras en las redes de transporte de los operadores que no causan el corte de la señal. Comparamos el QoT estimado y medido en el transpondedor óptico mediante el uso de una herramienta de QoT basada en GNPy. Mostramos que los cambios en los valores de los parámetros de entrada del modelo QoT que representan los dispositivos ópticos pueden explicar las desviaciones y la degradación en el rendimiento de dichos dispositivos. Usamos ingeniería inversa para estimar el valor de aquellos parámetros que explican el QoT observado. Mostramos, mediante simulación, una gran anticipación en la detección, localización e identificación de fallas leves antes de afectar la red. Finalmente, validamos nuestro método de forma experimental y comprobamos la alta precisión en la estimación de los parámetros de los modelos. El segundo objetivo se centra en las redes ópticas multicapa, donde se utilizan conexiones ópticas (lightpaths) para conectar nodos de paquetes creando así enlaces virtuales (vLink). Específicamente, estudiamos cómo se pueden gestionar los lightpaths para proporcionar suficiente capacidad a la capa de paquetes sin efectos perjudiciales en su calidad de servicio (QoS), como retrasos adicionales o pérdidas de paquetes, y al mismo tiempo minimizar el consumo de energía. Estudiamos el funcionamiento autónomo de conexiones ópticas basadas en multiplexación de subportadoras digitales (DSCM) y proponemos soluciones para su funcionamiento autónomo. En particular, la combinación de dos módulos que se ejecutan en el nodo óptico y en el transpondedor óptico activan y desactivan subportadoras para adaptar la capacidad de la conexión óptica al tráfico de paquetes. El módulo que se ejecuta en el nodo óptico implementa la predicción para anticipar los cambios de tráfico. Nuestro estudio demuestra la viabilidad de la operación autónoma de DSCM y muestra un gran ahorro de consumo de energía. El objetivo final es la automatización de conexiones de capa de paquete (PkC). La automatización de la capacidad requerida por las PkC puede generar una mayor reducción de costes, ya que puede limitar los recursos utilizados en la capa óptica. Sin embargo, dicha automatización requiere un diseño cuidadoso para evitar cualquier degradación de QoS, lo que afectaría acuerdos de nivel de servicio (SLA) en el caso de que el flujo de paquetes esté relacionado con alguna conexión del cliente. Estudiamos la gestión autónoma de la capacidad del flujo de paquetes. Aplicamos RL y proponemos un ciclo de vida de gestión con tres fases: 1) un enfoque basado en umbrales auto ajustados para configurar la conexión hasta que se recopilen suficientes datos, lo que permite comprender las características del tráfico; 2) operación RL basada en modelos pre-entrenados con perfiles de tráfico genéricos; y 3) operación de RL en base a modelos entrenados con el tránsito observado. Mostramos que los algoritmos de RL ofrecen un desempeño deficiente hasta que aprenden las políticas óptimas, así cuando las características del tráfico cambian con el tiempo. El ciclo de vida propuesto proporciona un rendimiento notable desde el inicio de la conexión y muestra la robustez frente a cambios en el tráfico. La contribución es doble: 1) proponemos una solución basada en RL que muestra un rendimiento superior que la solución basada en predicción; y 2) debido a que los vLinks admiten conexiones de paquetes, se propone la coordinación entre las intenciones de ambas capas. En este caso, la intención de vLink utiliza las acciones realizadas por los PkC individuales. Los resultados muestran un rendimiento notable en comparación con la operación independiente de vLink.Postprint (published version

    A survey of deep learning approaches for WiFi-based indoor positioning

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    One of the most popular approaches for indoor positioning is WiFi fingerprinting, which has been intrinsically tackled as a traditional machine learning problem since the beginning, to achieve a few metres of accuracy on average. In recent years, deep learning has emerged as an alternative approach, with a large number of publications reporting sub-metre positioning accuracy. Therefore, this survey presents a timely, comprehensive review of the most interesting deep learning methods being used for WiFi fingerprinting. In doing so, we aim to identify the most efficient neural networks, under a variety of positioning evaluation metrics for different readers. We will demonstrate that despite the new emerging WiFi signal measures (i.e. CSI and RTT), RSS produces competitive performances under deep learning. We will also show that simple neural networks outperform more complex ones in certain environments

    Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines

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    In the past decade, deep learning techniques have powered many aspects of our daily life, and drawn ever-increasing research interests. However, conventional deep learning approaches, such as deep belief network (DBN), restricted Boltzmann machine (RBM), and convolutional neural network (CNN), suffer from time-consuming training process due to fine-tuning of a large number of parameters and the complicated hierarchical structure. Furthermore, the above complication makes it difficult to theoretically analyze and prove the universal approximation of those conventional deep learning approaches. In order to tackle the issues, multilayer extreme learning machines (ML-ELM) were proposed, which accelerate the development of deep learning. Compared with conventional deep learning, ML-ELMs are non-iterative and fast due to the random feature mapping mechanism. In this paper, we perform a thorough review on the development of ML-ELMs, including stacked ELM autoencoder (ELM-AE), residual ELM, and local receptive field based ELM (ELM-LRF), as well as address their applications. In addition, we also discuss the connection between random neural networks and conventional deep learning

    Machine Learning For In-Region Location Verification In Wireless Networks

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    In-region location verification (IRLV) aims at verifying whether a user is inside a region of interest (ROI). In wireless networks, IRLV can exploit the features of the channel between the user and a set of trusted access points. In practice, the channel feature statistics is not available and we resort to machine learning (ML) solutions for IRLV. We first show that solutions based on either neural networks (NNs) or support vector machines (SVMs) and typical loss functions are Neyman-Pearson (N-P)-optimal at learning convergence for sufficiently complex learning machines and large training datasets . Indeed, for finite training, ML solutions are more accurate than the N-P test based on estimated channel statistics. Then, as estimating channel features outside the ROI may be difficult, we consider one-class classifiers, namely auto-encoders NNs and one-class SVMs, which however are not equivalent to the generalized likelihood ratio test (GLRT), typically replacing the N-P test in the one-class problem. Numerical results support the results in realistic wireless networks, with channel models including path-loss, shadowing, and fading

    Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses

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    International audienceMultiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electronics as neurons. Our architecture is highly resilient to issues caused by imperfect devices. It tolerates inter-device variability and an adaptable learning rule offers immunity against asymmetries in device switching. Highly compliant with conventional fabrication processes, the system can be extended to larger computing systems capable of complex cognitive tasks, as demonstrated in complementary simulations

    Robust Extreme Learning Machine for Modeling with Unknown Noise

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    Extreme learning machine (ELM) is an emerging machine learning technique for training single hidden layer feedforward networks (SLFNs). During the training phase, ELM model can be created by simultaneously minimizing the modeling errors and norm of the output weights. Usually, squared loss is widely utilized in the objective function of ELMs, which is theoretically optimal for the Gaussian error distribution. However, in practice, data collected from uncertain and heterogeneous environments trivially result in unknown noise, which may be very complex and cannot be described well using any single distribution. In order to tackle this issue, in this paper, a robust ELM (R-ELM) is proposed for improving the modeling capability and robustness with Gaussian and non-Gaussian noise. In R-ELM, a modified objective function is constructed to fit the noise using mixture of Gaussian (MoG) to approximate any continuous distribution. In addition, the corresponding solution for the new objective function is developed based on expectation maximization (EM) algorithm. Comprehensive experiments, both on selected benchmark datasets and real world applications, demonstrate that the proposed R-ELM has better robustness and generalization performance than state-of-the-art machine learning approaches

    Distributed collaborative knowledge management for optical network

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    Network automation has been long time envisioned. In fact, the Telecommunications Management Network (TMN), defined by the International Telecommunication Union (ITU), is a hierarchy of management layers (network element, network, service, and business management), where high-level operational goals propagate from upper to lower layers. The network management architecture has evolved with the development of the Software Defined Networking (SDN) concept that brings programmability to simplify configuration (it breaks down high-level service abstraction into lower-level device abstractions), orchestrates operation, and automatically reacts to changes or events. Besides, the development and deployment of solutions based on Artificial Intelligence (AI) and Machine Learning (ML) for making decisions (control loop) based on the collected monitoring data enables network automation, which targets at reducing operational costs. AI/ML approaches usually require large datasets for training purposes, which are difficult to obtain. The lack of data can be compensated with a collective self-learning approach. In this thesis, we go beyond the aforementioned traditional control loop to achieve an efficient knowledge management (KM) process that enhances network intelligence while bringing down complexity. In this PhD thesis, we propose a general architecture to support KM process based on four main pillars, which enable creating, sharing, assimilating and using knowledge. Next, two alternative strategies based on model inaccuracies and combining model are proposed. To highlight the capacity of KM to adapt to different applications, two use cases are considered to implement KM in a purely centralized and distributed optical network architecture. Along with them, various policies are considered for evaluating KM in data- and model- based strategies. The results target to minimize the amount of data that need to be shared and reduce the convergence error. We apply KM to multilayer networks and propose the PILOT methodology for modeling connectivity services in a sandbox domain. PILOT uses active probes deployed in Central Offices (COs) to obtain real measurements that are used to tune a simulation scenario reproducing the real deployment with high accuracy. A simulator is eventually used to generate large amounts of realistic synthetic data for ML training and validation. We apply KM process also to a more complex network system that consists of several domains, where intra-domain controllers assist a broker plane in estimating accurate inter-domain delay. In addition, the broker identifies and corrects intra-domain model inaccuracies, as well as it computes an accurate compound model. Such models can be used for quality of service (QoS) and accurate end-to-end delay estimations. Finally, we investigate the application on KM in the context of Intent-based Networking (IBN). Knowledge in terms of traffic model and/or traffic perturbation is transferred among agents in a hierarchical architecture. This architecture can support autonomous network operation, like capacity management.La automatización de la red se ha concebido desde hace mucho tiempo. De hecho, la red de gestión de telecomunicaciones (TMN), definida por la Unión Internacional de Telecomunicaciones (ITU), es una jerarquía de capas de gestión (elemento de red, red, servicio y gestión de negocio), donde los objetivos operativos de alto nivel se propagan desde las capas superiores a las inferiores. La arquitectura de gestión de red ha evolucionado con el desarrollo del concepto de redes definidas por software (SDN) que brinda capacidad de programación para simplificar la configuración (descompone la abstracción de servicios de alto nivel en abstracciones de dispositivos de nivel inferior), organiza la operación y reacciona automáticamente a los cambios o eventos. Además, el desarrollo y despliegue de soluciones basadas en inteligencia artificial (IA) y aprendizaje automático (ML) para la toma de decisiones (bucle de control) en base a los datos de monitorización recopilados permite la automatización de la red, que tiene como objetivo reducir costes operativos. AI/ML generalmente requieren un gran conjunto de datos para entrenamiento, los cuales son difíciles de obtener. La falta de datos se puede compensar con un enfoque de autoaprendizaje colectivo. En esta tesis, vamos más allá del bucle de control tradicional antes mencionado para lograr un proceso eficiente de gestión del conocimiento (KM) que mejora la inteligencia de la red al tiempo que reduce la complejidad. En esta tesis doctoral, proponemos una arquitectura general para apoyar el proceso de KM basada en cuatro pilares principales que permiten crear, compartir, asimilar y utilizar el conocimiento. A continuación, se proponen dos estrategias alternativas basadas en inexactitudes del modelo y modelo de combinación. Para resaltar la capacidad de KM para adaptarse a diferentes aplicaciones, se consideran dos casos de uso para implementar KM en una arquitectura de red óptica puramente centralizada y distribuida. Junto a ellos, se consideran diversas políticas para evaluar KM en estrategias basadas en datos y modelos. Los resultados apuntan a minimizar la cantidad de datos que deben compartirse y reducir el error de convergencia. Aplicamos KM a redes multicapa y proponemos la metodología PILOT para modelar servicios de conectividad en un entorno aislado. PILOT utiliza sondas activas desplegadas en centrales de telecomunicación (CO) para obtener medidas reales que se utilizan para ajustar un escenario de simulación que reproducen un despliegue real con alta precisión. Un simulador se utiliza finalmente para generar grandes cantidades de datos sintéticos realistas para el entrenamiento y la validación de ML. Aplicamos el proceso de KM también a un sistema de red más complejo que consta de varios dominios, donde los controladores intra-dominio ayudan a un plano de bróker a estimar el retardo entre dominios de forma precisa. Además, el bróker identifica y corrige las inexactitudes de los modelos intra-dominio, así como también calcula un modelo compuesto preciso. Estos modelos se pueden utilizar para estimar la calidad de servicio (QoS) y el retardo extremo a extremo de forma precisa. Finalmente, investigamos la aplicación en KM en el contexto de red basada en intención (IBN). El conocimiento en términos de modelo de tráfico y/o perturbación del tráfico se transfiere entre agentes en una arquitectura jerárquica. Esta arquitectura puede soportar el funcionamiento autónomo de la red, como la gestión de la capacidad.Postprint (published version
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