Summary: Wind turbine (WT) reliability has come to the forefront of research due to the rapid growth of wind energy in recent years. Reliability information can help understand failure causes and focus maintenance and prevention efforts on the most critical components, reducing costs and increasing profits. This paper offers new insights into WT reliability after analysing the data provided by the Supervisory Control And Data Acquisition (SCADA) system collected from seven onshore WTs located in central Spain from January 2014 to September 2021. To this end, we propose a method to link SCADA data to failure and maintenance records based on checking whether each 10‐min average time sample was collected when any failure or maintenance action had been reported. These records have been manually mapped to the WT taxonomy based on the standard Reference Designation System for Power Plants (RDS‐PP®) with minor changes. We present three different results: (i) The capacity factor and time‐based availability of each WT; (ii) the subsystem failure rate and downtime to identify the most critical ones; and (iii) each WT power curve with the 10‐min time samples labelled as healthy, under maintenance, or failure states, along with a ranking of the subsystems causing the most failures in each part of the power curves. It is the first time that time samples are linked to failure and maintenance records to visualise their distribution on the power curves. These results can help research point in the right direction to improve reliability and increase electricity production worldwide.

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.

Mobile Internet data traffic has experienced an exponential growth over the last few years due to the rise of demanding multimedia content and the increasing number of mobile devices. Seamless mobility support at the IP level is envisioned as a key architectural requirement in order to deal with the ever-increasing demand for data and to efficiently utilize a plethora of different wireless access networks. Current efforts from both industry and academia aim to evolve the mobility management protocols towards a more distributed operation to tackle shortcomings of fully centralized approaches. However, distributed solutions face several challenges that can result in lower performance which might affect real-time and multimedia applications. In this paper, we conduct an analytical and simulated evaluation of the main centralized and proposed Distributed Mobility Management (DMM) solutions. Our results show that, in some scenarios, when users move at high speed and/or when the mobile node is running long-lasting applications, the DMM approaches incur high signaling cost and long handover latency.

The Internet of Things (IoT) is faced with challenges that require green solutions and energy-efficient paradigms. Architectures (such as ARM) have evolved significantly in recent years, with improvements to processor efficiency, essential for always-on devices, as a focal point. However, as far as software is concerned, few approaches analyse the advantages of writing efficient code when programming IoT devices. Therefore, this proposal aims to improve source code optimization to achieve better execution times. In addition, the importance of various techniques for writing efficient code for Raspberry Pi devices is analysed, with the objective of increasing execution speed. A complete set of tests have been developed exclusively for analysing and measuring the improvements achieved when applying each of these techniques. This will raise awareness of the significant impact the recommended techniques can have.

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.