1,721,133 research outputs found
Implementation and experimental evaluation of a Collision-Free MAC protocol for WLANs
No full textCollisions are a main cause of throughput degradation in Wireless LANs. The current contention mechanism for these networks is based on a random backoff strategy to avoid collisions with other transmitters. Even though it can reduce the probability of collisions, the random backoff prevents users from achieving Collision-Free schedules, where the channel would be used more efficiently. Modifying the contention mechanism by waiting for a deterministic timer after successful transmissions, users would be able to construct a Collision-Free schedule among successful contenders. This work shows the experimental results of a Collision-Free MAC (CF-MAC) protocol for WLANs using commercial hardware and open firmware for wireless network cards which is able to support many users. Testbed results show that the proposed CF-MAC protocol leads to a better distribution of the available bandwidth among users, higher throughput and lower losses than the unmodified WLANs clients using a legacy firmware
On the Low-latency region of best-effort links for delay-sensitive streaming traffic
No full textThis letter analyzes the Low-latency Region (LLR) of a best-effort link (i.e., no traffic differentiation, and first come first serve scheduling) carrying both delay-sensitive (DS) streaming and non-delay-sensitive (NDS) background traffic. Moreover, inside the LLR, we show it exists a proportional fair arrival rate allocation for both the DS and NDS traffic streams. This optimal operating point results from maximizing a simple throughput-delay trade-off that considers the NDS traffic load, and the mean delay of the DS packets. To show how the presented trade-off could be used to allocate NDS traffic in a realistic scenario, we use Google Stadia traffic traces to generate the DS flow. Results from this use-case confirm that the throughput-delay trade-off also works regardless the distribution of the packet arrival and packet service times.The author would like to thank Marc Carrascosa for his contribution in taking the Google Stadia measurements. Also, the author wants to especially thank the anonymous reviewers for their constructive, insightful and challenging comments. This work was supported by WINDMAL PGC2018-099959-B-I00 (MCIU/AEI/FEDER,UE), and SGR017-1188 (AGAUR)
Terahertz communications: Physical layer enablers and analysis
No full textUndoubtedly, spectrum scarcity constitutes the main bottleneck of current wireless networks. It is therefore imperative to move beyond the sub-6 GHz band in order to overcome this limitation. Toward this direction, terahertz (THz) communication is deemed a promising solution for future wireless systems owing to the abundant spectrum resources at these frequencies. Despite the prospect of terabit- per-second wireless links, THz signals suffer from severe propagation losses, which can undermine the communication range and performance of THz systems. In this dissertation, we tackle this challenge by putting forward two key physical layer technologies, namely massive multiple-input multiple-output (MIMO) and intelligent reflecting surfaces (IRSs). More particularly, this dissertation consists of two parts. In the first part, we thoroughly study the spatialwideband effect in THz massive MIMO. We commence by demonstrating that conventional narrowband beamforming/combining leads to substantial performance degradation for large antenna arrays and high transmission bandwidths. With this in mind, we propose a wideband array architecture based on true-timedelay and virtual subarrays. For the channel estimation problem, we introduce a wideband dictionary along with a novel variant of the orthogonal matching pursuit algorithm. Numerical simulations are provided showcasing that the proposed design enables: i) nearly squint-free beamforming/combining with a small number of true-time-delay elements; and ii) accurate channel acquisition with reduced pilot overhead even in the low signal-to-noise-ratio regime. In the second part, we focus on the fundamentals of IRSs at THz frequencies. Specifically, we show that an IRS has the potential to improve the energy efficiency of THz MIMO, when it is placed close to one of the link ends. As a result, electrically large IRSs are expected to operate in the radiating near-field zone, where the spherical wavefront of the emitted electromagnetic (EM) waves cannot be neglected. To this end, we introduce a spherical wave channel model by leveraging EM theory, which includes far-field as special case. Furthermore, we discuss the importance of using EM principles to characterize the path loss of IRS-aided links, as simplistic models may wrongly estimate the link budget and actual system performance. Our analysis reveals that: i) conventional far-field beamforming is highly suboptimal in terms of power gain, and hence beamfocusing is the optimal mode of operation for THz IRSs; and ii) frequencydependent beamfocusing is required in wideband THz transmissions, as beam squint can substantially reduce the achievable data rate.Programa de doctorat en Tecnologies de la Informació i les Comunicacion
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Prediction-based strategies for reducing data transmissions in the loT
No full textPredictions about the evolution of the Internet of Things (IoT) in the next years are optimistic. The number of interconnected devices will continue to grow exponentially, as well as the amount of data that they report. Part of this data will be generated by wireless sensor nodes organized in Wireless Sensor Networks (WSNs) to transmit their measurements to Gateways (GWs). However, wireless sensor nodes are mainly designed to have low costs, which implies constrained memory and energy supplies, and does not permit the streaming of measured data at high data rates. Meanwhile, modern uses of WSNs rely on the knowledge acquired by sensor nodes to trigger reactions in other systems, and sensed data has become critical to avoid economic–and living–losses. Therefore, it is important to optimize data transmissions in WSNs to support not only a higher number of wireless sensor nodes but also a higher diversity of sensed parameters. Solutions for data aggregation and data compression have reduced the number of gross transmissions, but they did not solve the problem of transmitting measurements that do not convey knowledge to the WSNs’ managers. These solutions do not exploit the fact that, fortunately, WSNs are asymmetric and, contrary to ordinary wireless sensor nodes, GWs have an Internet connection with no critical computational, power or communication limitations. Hence, GWs can run algorithms and process amounts of data that wireless sensor nodes do not support, which permits them to predict the data that will be measured. This thesis extends a paradigm that exploits WSNs to the utmost: data that can be predicted does not have to be transmitted. First, we design a self-managing WSN architecture that adopts a standardized communication to integrate WSNs into data analysis services in the cloud. To evaluate our idea in experiments, we implement the Data Analytics for Sensors Dashboard (DAS-Dashboard) to control and optimize, using specialized cloud services, a WSN via the Internet. Our experimental results show that the interconnection of remote components does not imply a significant overhead and that the architecture is feasible vii in practice. Then, relying on this architecture, we design a mechanism to adjust the sensor nodes’ sampling intervals according to the changes observed in the environment. The novelty of this mechanism is in the use of a Reinforcement Learning (RL) technique called Q-Learning. Simulation and experimental results show that this mechanism provides necessary means to make a smart WSN with the capacity of self-optimizing. As a result of hardware evolution, new wireless sensor nodes have extended memory and computing capabilities; and more sophisticated prediction algorithms were adopted in sensor nodes. In response to that, we analyze the benefits of incorporating the current state-of-the-art prediction algorithms in WSNs. The results are promising: our simulation results show that it is possible to eliminate WSN transmissions without reducing the quality of the measurements provided in several sensor network applications. For the future generations of WSNs, we design a theoretical model for characterizing the number of transmissions in WSNs, which can provide reliable estimations about the efficiency of prediction-based data reduction methods. The new model will support the WSNs’ growth regarding the number of sensor nodes in a single network and the quality of information processed by their GWs. The prediction-based strategies investigated in this thesis can impact the present and the future of the IoT. Current WSNs can be optimized to avoid unnecessary transmissions with the help of the cloud. Also, coming generations of WSNs will be supported by our WSN transmission model to adopt prediction algorithms and maintain strict control over the quality of the reported data without being harmed by the adoption of a higher number of sensor nodes; hence, collaborating to the IoT’s growth.Las predicciones sobre la evolución del Internet of Things (IoT) en los próximos años son optimistas. El número de dispositivos interconectados continuará creciendo exponencialmente, así como la cantidad de datos generados. Parte de estos datos serán generados por los nodos sensores inalámbricos organizados en Redes de Sensores Inalámbricas (del inglés Wireless Sensor Networks, o abreviado WSNs) para transmitir sus mediciones a sus correspondientes Gateways (GWs). Sin embargo, los sensores inalámbricos están diseñados principalmente para ser de bajo coste, lo que implica recursos de memoria y de energía finitos, y no permite la transmisión de los datos medidos a altas velocidades. Mientras tanto, los usos actuales de las WSNs se basan en los conocimientos adquiridos por los nodos sensores para desencadenar reacciones en otros sistemas, de modo que estos datos se han convertido en fundamentales para evitar pérdidas económicas–y de vidas. Por lo tanto, es importante optimizar las transmisiones de datos en WSNs para soportar no sólo un mayor número de nodos sensores inalámbricos, sino también una mayor diversidad de parámetros detectados. Las soluciones para la agregación y compresión de datos han reducido el número de transmisiones brutas, pero no han resuelto el problema de la transmisión de las mediciones que no llevan información útil a los administradores de las WSNs. Estas soluciones no explotan el hecho de que, afortunadamente, las WSNs son asimétricas y, contrariamente a los nodos sensores inalámbricos habituales, los GWs tienen una conexión a Internet sin limitaciones críticas en términos computacionales, energéticos o de comunicación. Por lo tanto, los GWs pueden ejecutar algoritmos y procesar grandes volúmenes de datos no ejecutables en los sensores inalámbricos, lo que les permite predecir los datos que se van a medir. Esta tesis extiende un paradigma que explota las WSNs al máximo: los datos que pueden ser predichos no tienen que ser transmitidos. En primer lugar, diseñamos una arquitectura de autogestión para WSNs que adopta una comunicación estandarizada para integrar las ixWSNs con los servicios de análisis de datos en la nube. Para evaluar nuestra idea de forma experimental, se ha implementado el Data Analytics for Sensors Dashboard (DAS-Dashboard) para controlar y optimizar, mediante servicios especializados en la nube, una WSN a través de Internet. Nuestros resultados experimentales muestran que la interconexión de componentes remotos no implica una sobrecarga significativa y que la arquitectura resultante es factible en la práctica. Basándonos en esta arquitectura, diseñamos un mecanismo para ajustar los intervalos de muestreo de los nodos sensores a partir de los cambios observados en el medio. La novedad de este mecanismo está en el uso de una técnica de Reinforcement Learning (RL) llamada Q-Learning. Los resultados de la simulación y los experimentos muestran que este mecanismo proporciona los medios necesarios para hacer una WSN inteligente con capacidad de auto-optimización. Como resultado de la evolución hardware, nuevos nodos sensores inalámbricos han extendido las capacidades de memoria y de cómputo; como consecuencia, se ha adoptado algoritmos más sofisticados de predicción en los nodos sensores. En respuesta a ello, analizamos los beneficios de la incorporación de los algoritmos de predicción actuales del estado del arte en WSNs. Los resultados de nuestras simulaciones son prometedores: estos demuestran que en varias aplicaciones de redes de sensores es posible eliminar algunas transmisiones sin reducir la calidad de las medidas proporcionadas. Para las futuras generaciones de WSNs, diseñamos un modelo teórico para caracterizar el número de transmisiones en WSNs, que pueden proporcionar estimaciones fiables acerca de la eficiencia de los métodos de reducción de datos basados en predicciones. El nuevo modelo permite el crecimiento de las WSNs en relación al número de nodos sensores en una sola red y la calidad de la información procesada por el GW. Las estrategias basadas en la predicción de datos investigadas en esta tesis pueden tener un impacto en el presente y el futuro del IoT. Las WSNs actuales pueden ser optimizadas para evitar transmisiones innecesarias con la ayuda del cloud. Además, las nuevas generaciones de WSNs estarán respaldadas por nuestro modelo de transmisión para adoptar algoritmos de predicción y mantener un estricto control sobre la calidad de los datos notificados sin ser dañadas por la adopción de un mayor número de nodos sensores; por consiguiente, colaborando en el crecimiento del IoT.Programa de doctorat en Tecnologies de la Informació i les Comunicacion
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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