In recent years, there has been a remarkable advance in monitoring technologies in many environments, be they urban or rural. These technologies, included in the Internet of Things (IoT) domain, allow remote control and acquisition of data from sensors for their subsequence analysis. All these systems are based on the interaction between sensors and actuators. To achieve this goal, it is necessary to provide a very high level of connectivity between the devices, especially as far as wireless systems are concerned. In this sense, there is a great variety of standards in the market of communication networks oriented to this end. One of the biggest challenges today is to allow inter-operability between these different technologies in order to homogenize this field. In addition to this, it is intended to introduce new communication techniques that can provide certain additional advantages to those already existing. The main idea is the creation of a cellular network where radiofrequency and optical technologies coexist, and whose link with the rest of the world is through long-range and low-consumption wireless technologies. The center of each cell, that is the lighting system, can be powered using solar panels, as can the existing systems in the market. The objective is that these panels are capable of providing the necessary energy to the rest of the necessary systems.

El posicionamiento en interiores es un problema que aún no se ha resuelto de manera eficiente y precisa. En exteriores la solución más eficaz es el Sistema de Posicionamiento Global (GPS), pero no se puede utilizar en interiores debido al debilitamiento de la señal, por lo que se han estudiado otras soluciones. Estos enfoques podrían aplicarse para definir un mapa para la orientación de personas ciegas, el turismo o la navegación para robots autónomos. En este trabajo se propone el estudio, diseño, implementación y evaluación de un sistema robusto de detección y mapeo de obstáculos. Por tanto, se puede utilizar para alertar de la presencia de objetos cercanos y evitar posibles colisiones en una navegación interior. El sistema se basa en una cámara de tiempo de vuelo (ToF) y una computadora de placa única (SBC) como Raspberry PI o NVIDIA Jetson Nano. Para evaluar el sistema se llevaron a cabo varios experimentos reales. Este tipo de sistema puede integrarse en una silla de ruedas y ayudar a la persona discapacitada a moverse en el interior o tomar datos de un entorno interior y recrearlos en imágenes 2D o 3D.

At the height of M2M communications, there are many alternatives and architectures that present solutions for each case and each environment. The interoperability strategy or the combination of different solutions with adequate flexibility can be solutions to maintain a capacity for easy incorporation of new sensor nodes depending on the coverage or not of operators. In addition to interoperability strategies, this chapter presents some alternatives that include multi-hop techniques, combining different technologies. Special emphasis will be placed on low-power wide area networks systems (LoRa, Narrow Band IoT, LTE, etc.) applied in remote environments, such as nature reserves and ocean or fluvial ecosystems. An estate of art of these areas will be presented, as well as results of different development of our group.

The Canary Islands, internationally recognized as a biodiversity hot spot, possess unique ecological characteristics including endemic reptile species that face substantial threats from invasive alien species. Particularly, concerning is the California kingsnake ( Lampropeltis californiae ), which exhibits remarkable adaptability and inflicts severe ecological damage on endangered endemic fauna. This proposal aims to address biodiversity conservation challenges in the Canary Islands ecosystem by advancing reptile species classification through the implementation of Swin Transformer architectures. The integration of these advanced neural network architectures with conservation biology provides an automated, precise tool for species identification across different taxonomic levels that can enhance monitoring and control strategies for biodiversity preservation worldwide. This technical approach addresses urgent conservation requirements in the Canary Islands while simultaneously establishing a methodological framework applicable to other ecological contexts facing comparable biodiversity threats. The methodology employed pre-trained Swin Transformer models, initially developing fine-grained reptile species discrimination capabilities through hyperparameter optimization on a limited-scale database (160 images, 40 per species) using nested cross-validation to ensure statistical rigor and independence. The optimized configuration was subsequently transferred to a substantially larger database (4,400 images) for binary classification distinguishing snake from non-snake reptiles, where training on only 400 samples achieved 99.38% accuracy on 4,000 independent test samples, directly addressing the invasive alien species monitoring challenge. The Swin-B Transformer model demonstrated robust performance, achieving up to 100% accuracy in multiclass species classification and 99.38% accuracy in the challenging, taxonomically broader binary snake versus non-snake classification. These results establish the transferability and scalability of the hyperparameter optimization methodology across datasets of substantially different magnitudes.