IEEE 802.11 (Wi-Fi) is one of the technologies that provides high performance with a high density of connected devices to support emerging demanding services, such as virtual and augmented reality. However, in highly dense deployments, Wi-Fi performance is severely affected by interference. This problem is even worse in new standards, such as 802.11n/ac, where new features such as Channel Bonding (CB) are introduced to increase network capacity but at the cost of using wider spectrum channels. Finding the best channel assignment in dense deployments under dynamic environments with CB is challenging, given its combinatorial nature. Therefore, the use of analytical or system models to predict Wi-Fi performance after potential changes (e.g., dynamic channel selection with CB, and the deployment of new devices) are not suitable, due to either low accuracy or high computational cost. This paper presents a novel, data-driven approach to speed up this process, using a Graph Neural Network (GNN) model that exploits the information carried in the deployment’s topology and the intricate wireless interactions to predict Wi-Fi performance with high accuracy. The evaluation results show that preserving the graph structure in the learning process obtains a 64% increase versus a naive approach, and around 55% compared to other Machine Learning (ML) approaches when using all training features.

This work shows the engineering process carried out for the design of a low cost control system for an astronomical observatory. The work describes the implementation to adapt the equipment of the observatory to a Master Control System (MCS) and be able to use it remotely. The instruments and software required for the integration of the equipment as part of a robotic observatory are also described.

With the development of new technologies, particularly Internet of Things (IoT), there has been an increase in the deployment of low-cost air quality monitoring systems. Compared to traditional robust monitoring stations, these systems provide real-time information with higher spatio-temporal resolution. These systems use inexpensive and low-cost sensors, with lower accuracy as compared to robust systems. This fact has raised some concern regarding the quality of the data gathered by the IoT systems, which may compromise the performance of the environmental models. Considering the relevance of the data quality in this scenario, this paper presents a study of the data quality associated with IoT-based air quality monitoring systems. Following a systematic mapping method, and based on existing guidelines to assess data quality in these systems, we have identified the main Data Quality (DQ) dimensions and the corresponding DQ enhancement techniques. After analyzing more than 70 papers, we found that the most common DQ dimensions targeted by the different works are accuracy and precision, which are enhanced by the use of different calibration techniques. Based on our findings, we present a discussion on the challenges that must be addressed in order to improve data quality in IoT-based air quality monitoring systems.

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Dentro de los esquemas de comunicación de redes inalámbricas de área corporal (WBAN), se encuentran los protocolos de capa cruzada, constituidos en una novedosa opción para alcanzar un balance efectivo entre consumo eficiente de energía y métricas de desempeño. En el presente trabajo, evaluamos el desempeño de una estrategia de capa cruzada al compararla contra los protocolos del estándar IEEE802.15.4 en una WBAN. Se evaluó el desempeño de ambas estrategias empleando una simulación de redes WBAN. Luego se ejecutó una comparación estadística y se encontró que la estrategia de capa cruzada ofrece un mejor desempeño con respecto a la compensación entre consumo eficiente de energía y algunas métricas de desempeño en nuestra WBAN. Observamos que en general, la estrategia de capa cruzada supera a ambos modos del estándar IEEE802.15.4 (ranurado y no-ranurado) con respecto a consumo eficiente de energía, retraso extremo a extremo, tasa de pérdida de paquetes y goodput.