Estudio e implementación de protocolos de enrutamiento de redes malladas inalámbricas en entornos rurales

Este proyecto surge de la problemática de llevar las tecnologías de la información a las zonas más alejadas e inaccesibles, donde generalmente se dan los mayores índices de pobreza. Para ello se propone el estudio de la viabilidad de las redes malladas inalámbricas aplicadas en entornos rurales. Las redes malladas tienen una filosofía distribuida que permite mayores coberturas con un menor presupuesto, además de ofrecer una mayor seguridad en caso de catástrofes y una gran independencia de las grandes redes de los proveedores de Internet. Esto hace que puedan ser una interesante alternativa a las redes de comunicación inalámbrica convencionales. En primer lugar se intenta analizar cu ́les son los protocolos de encaminamiento más adecuados para redes malladas en este tipo de escenarios rurales, donde el medio inalámbrico es adverso e inestable y la logística es más complicada. En concreto, se ha desplegado para su posterior estudio, una red experimental rural en un entorno altoandino. En dicha red se comparan dos soluciones de encaminamiento de código abierto, Batman-adv y IEEE 802.11s. Adicionalmente, el proyecto intentar difundir la tecnología y transferir los conocimientos aprendidos a través de diversos talleres de capacitación,artículo técnico sobre la comparación de las prestaciones Batman-adv y IEEE 802.11s, y la creación de un proyecto de red comunitaria libre llamado MESH-RURAL, el cual facilitar ́ la implementación de redes malladas inalámbricas con estas dos soluciones de encaminamiento

Evaluación de protocolos de encaminamiento en una red inalámbrica mallada desplegada en una zona rural

The implementation of wireless communication systems in rural areas through the deployment of data networks in infrastructure mode is often inadequate due to its high cost and no fault tolerant centralized structure. Mesh networks can overcome these limitations while increases the coverage area in a more flexible way. This paper proposes the performance evaluation of the routing protocols IEEE 802.11s and Batman-Adv on an experimental wireless mesh network deployed in a rural environment called Lachocc, which is a community located at 4700 MASL in the Huancavelica region in Peru. The evaluation was based on the measurement of quality of service parameters such as bandwidth, delay and delay variation. As a result, it was determined that both protocols offer a good performance, but in most of the cases, Batman-Adv provides slightly better performanc

Continuous athlete monitoring in challenging cycling environments using IoT technologiesis

Internet of Things (IoT)-based solutions for sport analytics aim to improve performance, coaching, and strategic insights. These factors are especially relevant in cycling, where real-time data should be available anytime, anywhere, even in remote areas where there are no infrastructure-based communication technologies (e.g., LTE and Wi-Fi). In this article, we present an experience report on the use of state-of-the-art IoT technologies in cycling, where a group of cyclists can form a reliable and energy efficient mesh network to collect and process sensor data in real-time, such as heart rate, speed, and location. This data is analyzed in real-time to estimate the performance of each rider and derive instantaneous feedback. Our solution is the first to combine a local body area network to gather the sensor data from the cyclist and a 6TiSCH network to form a multihop long-range wireless sensor network in order to provide each bicycle with connectivity to the sink (e.g., a moving car following the cyclists). In this article, we present a detailed technical description of this solution, describing its requirements, options, and technical challenges. In order to assess such a deployment, we present a large publicly available data-set from different real-world cycling scenarios (mountain road cycle racing and cyclo-cross) which characterizes the performance of the approach, demonstrating its feasibility and evidencing its relevance and promising possibilities in a cycling context for providing low-power communication with reliable performance

Extreme Rainfall Event Classification Using Machine Learning for Kikuletwa River Floods

A research article was submitted to Water 2023, volume 15Advancements in machine learning techniques, availability of more data sets, and increased computing power have enabled a significant growth in a number of research areas. Predicting, detecting, and classifying complex events in earth systems which by nature are difficult to model is one such area. In this work, we investigate the application of different machine learning techniques for detecting and classifying extreme rainfall events in a sub-catchment within the Pangani River Basin, found in Northern Tanzania. Identification and classification of extreme rainfall event is a preliminary crucial task towards success in predicting rainfall-induced river floods. To identify a rain condition in the selected sub-catchment, we use data from five weather stations that have been labeled for the whole sub-catchment. In order to assess which machine learning technique is better suited for rainfall classification, we apply five different algorithms in a historical dataset for the period of 1979 to 2014. We evaluate the performance of the models in terms of precision and recall, reporting random forest and XGBoost as having the best overall performances. However, because the class distribution is imbalanced, a generic multi-layer perceptron performs best when identifying heavy rainfall events, which are eventually the main cause of rainfall-induced river floods in the Pangani River Basi

Extreme Rainfall Events Classification Using Machine Learning for Kikuletwa River Floods

A research article was submitted by artificial intelligence and machine learningAdvancements in Machine Learning techniques, availability of more data-sets, and 1 increased computing power have enabled a significant growth in a number research areas. Predicting, 2 detecting and classifying complex events in earth systems which by nature are difficult to model 3 is one of such areas. In this work, we investigate the application of different machine learning 4 techniques for detecting and classifying extreme rainfall events in a sub-catchment within Pangani 5 River Basin, found in Northern Tanzania. Identification and classification of extreme rainfall event 6is a preliminary crucial task towards success in predicting rainfall-induced river floods. To identify 7 a rain condition in the selected sub-catchment, we use data from five weather stations which have 8 been labeled for the whole sub-catchment. In order to assess which Machine Learning technique 9 suits better for rainfall classification, we apply five different algorithms in a historical dataset for the 10 period of 1979 to 2014. We evaluate the performance of the models in terms of precision and recall, 11 reporting Random Forest and XGBoost as the ones with best overall performance. However, since the 12 class distribution is imbalanced, the generic Multi-layer Perceptron performs best when identifying 13 the heavy rainfall events, which are eventually the main cause of rainfall-induced river floods in the 14 Pangani River Basin

Sensor-Based River Monitoring System: A Case for Kikuletwa River Floods in Tanzania

A research was submitted to computer science and mathematics volume 2, 2023Reliable and accurate flood prediction is a challenging task in poorly gauged basins due 1 to data scarcity. Data is an essential component of any AI/ML model today, and the performance 2 of such models hugely depends on the availability of sufficient amount of trusted, representative 3 data. However, unlike a few well-studied rivers, most of the rivers in developing countries are still 4 insufficiently monitored, which significantly hinges the design and development of advanced flood 5 prediction models and early warning systems. This paper presents a multi-modal, sensor-based and 6near-real time river monitoring system to produce a mul ti-feature data set for the Kikuletwa river in 7 Northern Tanzania, an area that heavily suffers from frequent floods. Our deployed system, which 8 gather information about river depth levels and weather at several locations, aims at widening the 9 ground truth of the river characteristics and eventually improve the accuracy of flood predictions. We 10 provide details on the monitoring system used to gather the data as well as report on the methodology 11 and the nature of the data. Finally, we present the relevance of the data set in the context of flood 12 prediction, discussing the most suitable AI/ML-based forecasting approaches, while also highlighting 13 some applications of the data set beyond flood warning systems

Aplicaciones del escaneado láser en Patrimonio Histórico-Artístico

El objeto de la presente comunicación es el de divulgar la utilización del láser-escáner dentro del campo de la arquitectura y la conservación del patrimonio histórico-artístico, utilizando para ello, a modo de ejemplo, el levantamiento realizado con un equipo láser escáner terrestre de un elemento del patrimonio histórico situado a las afueras de la ciudad de Cáceres. Realizaremos en primer lugar una exposición de la metodología empleada para el levantamiento, haremos una descripción del equipo utilizado y explicaremos el post-proceso realizado en estudio y el software utilizado para ello para seguidamente mostrar los resultados obtenidos en bruto, en forma de nube de puntos, y finalmente esbozar las posibles líneas de desarrollo a partir de dicha nube de puntos, en función de cuál sea el objeto último del trabajo o encargo profesional.Consejo General de la Arquitectura Técnica de Españ

Toward Standardized Performance Evaluation of Flow-guided Nanoscale Localization

Nanoscale devices featuring Terahertz (THz)-based wireless communication capabilities are envisioned to be deployed within human bloodstreams. Such devices are envisaged to enable fine-grained sensing-based applications for detecting events for early indications of various health conditions, as well as actuation-based ones such as the targeted drug delivery. Intuitively, associating the locations of such events with the events themselves would provide an additional utility for precision diagnostics and treatment. This vision recently yielded a new class of in-body localization coined under the term "flow-guided nanoscale localization". Such localization can be piggybacked on THz-based communication for detecting body regions in which events were observed based on the duration of one circulation of a nanodevice in the bloodstream. From a decades-long research on objective benchmarking of "traditional" indoor localization, as well as its eventual standardization (e.g., ISO/IEC 18305:2016), we know that in early stages the reported performance results were often incomplete (e.g., targeting a subset of relevant metrics), carrying out benchmarking experiments in different evaluation environments and scenarios, and utilizing inconsistent performance indicators. To avoid such a "lock-in" in flow-guided localization, in this paper we discuss a workflow for standardized evaluation of such localization. The workflow is implemented in the form of an open-source framework that is able to jointly account for the mobility of the nanodevices in the bloodstream, in-body THz communication between the nanodevices and on-body anchors, and energy-related and other technological constraints at the nanodevice level. Accounting for these constraints, the framework is able to generate the raw data that can be streamlined into different flow-guided solutions for generating standardized performance benchmarks.Comment: 8 pages, 6 figures, 15 references, available at: https://bitbucket.org/filip_lemic/flow-guided-localization-in-ns3/src/master

FLEXNET: Flexible Networks for IoT based services

Internet of Things is becoming one of the main triggers in designing and deploying new services aiming at fulfilling the wide demand imposed by end-users. Usually, concrete solutions addressing the optimization of the wireless segment are found in the literature. However, it is much less frequent to find end-to-end solutions to be easily adopted by the corresponding stakeholders. It is in this context that FLEXNET brings an integrated solution, relying on cutting-edge technologies, dealing with a wide set of technical requirements imposed by the different applications and services.This work was supported by FLEXNET Project: "Flexible IoT Networks for Value Creators" (Celtic 2016/3), in the Eureka Celtic-Next Cluster