The rise of the Internet of Things (IoT) technology during the past decade has resulted in multiple applications in many different fields. Some of the data processed using this technology can be specially sensitive, and the devices involved can be prone to cyberattacks, which has resulted in a rising interest in the field of information security applied to IoT. This study presents a method for analyzing an IoT network to detect attacks using side-channel techniques that monitor the power usage of the devices. We show that it is possible to employ a monitoring system powered by Machine Learning to detect intrusions without interfering with the normal behavior of the devices. Our tests yield positive results under different scenarios, such as using a custom dataset, detecting new attacks that the model was not trained with, or detecting attacks as they happen live. The main advantages of our proposed system are its simplicity, its reproducibility (both code and data are made available) and portability, since it can be deployed on many devices and does not have a high demand of resources. We propose different deployment strategies, depending on the structure of the IoT network and the power constraints of the devices.

Oxychalcogenides represent a large chemical space with potential application as thermoelectric materials due to their low thermal conductivity. However, the nature of this behaviour is still under debate. Understanding the origin of the anharmonicity of these materials is key to developing models that improves the efficiency of thermoelectric materials. In this work, we combine machine learning with first principles calculations to explore oxychalcogenides materials. Machine learning not only accelerates the prediction of the lattice thermal conductivity for large chemical spaces with high accuracy, but also catalyzes the development of design principles to discover new thermoelectric materials. Using this approach, lattice thermal conductivity has been directly connected to the effect of each species in the material, using atomic projections of the scattering rates. The role of the monovalent atom and the lone pair electron for the trivalent cation are discussed in detail. Based on this knowledge, it is possible to connect complex properties such as lattice thermal conductivity with a more manageable synthetic variable such as chemical composition. Using this strategy, we propose promising new oxychalcogenides such as BiOAgSe, which subsequently has been confirmed as having ultra-low lattice thermal conductivity and BiOAg_0.5Cu_0.5Se which presents a lower κ_l and promising properties for thermoelectricity.

The unprecedented growth of connected devices, together with the remarkable convergence of a wide variety of technologies, have led to an exponential increase in the services that the internet of things (IoT) can offer, all aimed at improving quality of life. Consequently, in order to meet the numerous challenges this produces, the IoT has become a major subject of research. One of these challenges is the reduction of energy consumption given the significant limitations of some devices. In addition, although the search for energy efficiency was initially focused on hardware, it has become a concern for software developers too. In fact, it has become an intense area of research with the principal objective of analyzing and optimizing the energy consumption of software systems. This research analyzes the energy saving that can be achieved when using a broad set of techniques for writing energy-efficient code for Raspberry Pi devices. It also demonstrates that programmers can save more energy if they apply the proposed techniques manually than when relying on other automatic optimization options offered by the GNU compiler collection (GCC). Thus, it is important that programmers are aware of the significant impact these techniques can have on an application’s energy consumption.

CultivData proposes the convergence of technologies, such as IoT, big data, HPC, open data and artificial intelligence, to apply HPDA (High Performance Data Analytics) to the cultivation of agricultural data and improve the efficiency and effectiveness of farms. An information system has been developed as an IT platform for the cultivation of open data to extract knowledge and to support the decision making of stakeholders in the agricultural sector, so that it is possible to improve product quality and farm productivity. The resulting system integrates access to data provided by IoT devices that sensorize farms and public and open data sources (Open Data). The platform was designed to make precision agriculture a reality and to be useful not only to farmers, but also to agricultural decision-makers who plan species and crops based on data such as available water; expected weather; prices and market demands, and so forth. In addition, the platform provides to agricultural producers access to historical climate data; climate forecasts to anticipate times of drought or disasters; pest situations or monitoring of their plantations with sensorization and orthophotographs.

The Internet of Things (IoT) is rapidly being integrated into critical infrastructure sectors, such as energy, transportation, healthcare, and industry. This surge of interconnected devices dramatically expands the attack surface and increases the risk of cascading system failures and data breaches. To address these emerging threats, this work proposes an intrusion detection system (IDS) for IoT networks that incorporates machine learning techniques, considering side-channel (power) and network traffic features. We collected power consumption traces and network metrics from IoT devices during normal operation and under diverse cyberattacks (e.g., cryptomining, flooding, port scanning). Time-series machine learning classifiers are trained on this hybrid dataset to differentiate benign versus malicious behavior. The experimental results show that the combined-feature model significantly outperforms models using only one data type, achieving high detection accuracy ($F1\approx 0.89$) and correctly identifying the attack type. The resulting IDS generalizes to previously unseen attacks, demonstrating robust, adaptive defense capabilities. The novelty of our approach lies in integrating physical side-channel signals into an automated ML framework, enhancing robustness and resilience. This smart, data-driven solution operates in near real time and helps build autonomous, constantly evolving defenses against cyber threats. Overall, our study delivers a state-of-the-art ML-based tool that learns and evolves to counter modern IoT cyberattacks.