The predicted growth of urban populations has prompted researchers and administrations to improve services provided to citizens. At the heart of these services are wireless networks of multiple different sensors supported by the Internet of Things. The main purpose of these networks is to provide sufficient information to achieve more intelligent transport, energy supplies, social services, public environments (indoor and outdoor) and security, etc. Two major technological advances would improve such networks in Smart Cities: efficient communication between nodes and a reduction in each nodeb’s power consumption. The present paper analyses how event-based sampling techniques can address both challenges. We describe the fundamentals of the triggering mechanisms that characterise Send-on-Delta, Send-on-Area, Send-on-Energy and Send-on-Prediction techniques to restrict the number of transmissions between the sensor node and the supervision or monitoring node without degrading tracking of the sensed variable. At the same time, these aperiodic techniques reduce consumption by sensor node electronic devices. In order to quantify the energy savings, we evaluate the increase achieved in the average lifetime of sensor node batteries. The data provided by Smart City tools in the city of Santander (Spain) were selected to conduct a case study of the main pollutants that determine city air quality: SO2, NO2, O3 and PM10. We conclude that event-based sensing techniques can yield up to 50% savings in sensor node consumption compared to classical periodic sensing techniques.

Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance.

Photovoltaic (PV) power prediction plays a critical role amid the accelerating adoption of renewable energy sources. This paper introduces a bidirectional long short-term memory (BiLSTM) deep learning (DL) model designed for forecasting photovoltaic power one hour ahead. The dataset under examination originates from a small PV installation located at the Polytechnic School of the University of Alcala. To improve the quality of historical data and optimize model performance, a robust data preprocessing algorithm is implemented. The BiLSTM model is synergistically combined with a Bayesian optimization algorithm (BOA) to fine-tune its primary hyperparameters, thereby enhancing its predictive efficacy. The performance of the proposed model is evaluated across diverse meteorological and seasonal conditions. In deterministic forecasting, the findings indicate its superiority over alternative models employed in this research domain, specifically a multilayer perceptron (MLP) neural network model and a random forest (RF) ensemble model. Compared with the MLP and RF reference models, the proposed model achieves reductions in the normalized mean absolute error (nMAE) of 75.03% and 77.01%, respectively, demonstrating its effectiveness in this type of prediction. Moreover, interval prediction utilizing the bootstrap resampling method is conducted, with the acquired prediction intervals carefully adjusted to meet the desired confidence levels, thereby enhancing the robustness and flexibility of the predictions.

Encouraged by the considerable cost reduction, small-scale solar power deployment has become a reality during the last decade. However, grid integration of small-scale photovoltaic (PV) solar systems still remains unresolved. High penetration of Renewable Energy Sources (RESs) results in technical challenges for grid operators. To address this, Virtual Power Plants (VPPs) have been defined and developed to manage distributed energy resources with the aim of facilitating the integration of RESs. This paper introduces a hybrid irradiance forecasting approach aimed at facilitating the integration of PV systems into a VPP, especially when a historical irradiance dataset is exiguous or non-existent. This approach is based on Artificial Neural Networks (ANNs) and a novel similar hour-based selection algorithm, has been tested for a real PV installation, and has been validated also considering irradiance measurements from an aggregation of ground-based meteorological stations, which emulate the nodes of a VPP. Under a reduced historical dataset, the results show that the proposed similar hour-based method produces the best forecasts with regard to those obtained by the ANN-based approach. This is particularly true for one-month and two-month datasets minimizing the mean error by 16.32% and 9.07% respectively. Finally, to demonstrate the potential of the proposed approach, a comparative analysis has been carried out between the hybrid method and the most used benchmarks in the literature, namely, the persistence method and the method based on similar days. It has been demonstrated conclusively that the proposed model yields promising results regardless the length of the historical dataset.

This paper describes a relative localization system used to achieve the navigation of a convoy of robotic units in indoor environments. This positioning system is carried out fusing two sensorial sources: (a) an odometric system and (b) a laser scanner together with artificial landmarks located on top of the units. The laser source allows one to compensate the cumulative error inherent to dead-reckoning; whereas the odometry source provides less pose uncertainty in short trajectories. A discrete Extended Kalman Filter, customized for this application, is used in order to accomplish this aim under real time constraints. Different experimental results with a convoy of Pioneer P3-DX units tracking non-linear trajectories are shown. The paper shows that a simple setup based on low cost laser range systems and robot built-in odometry sensors is able to give a high degree of robustness and accuracy to the relative localization problem of convoy units for indoor applications.

Un vehículo de guiado automático (Automatic Guided Vehicle –AGV-en inglés) es un sistema de transporte industrial completamente automatizado y alimentado por baterías. Estos vehículos son ampliamente utilizados en el sector industrial para sustituir a carretillas manuales y cintas transportadoras. El reto de la utilización de AGVs como agentes de transporte en entornos industriales pasa por dotarles de la inteligencia suficiente para desarrollar tareas colaborativas. Dentro de estas tareas colaborativas se diferencia el transporte multi-AGV de un objeto del transporte multi-AGV de múltiples objetos. Este trabajo presenta el estado del arte de las soluciones de transporte cooperativo de un objeto entre varios AGVs. Para ello, se revisan los fundamentos teóricos y se clasifican y describen varias propuestas para su resolución. Finalmente se propone una solución de control remoto centralizado para el transporte de una carga con AGVs omnidireccionales.