Today’s modern vehicles are connected to a network and are considered smart objects of IoT, thanks to the capability to send and receive data from the network. One of the greatest challenges in the automotive sector is to make the vehicle secure and reliable. In fact, there are more connected instruments on a vehicle, such as the infotainment system and/or data interchange systems. Indeed, with the advent of new paradigms, such as Smart City and Smart Road, the vision of Internet of Things has evolved substantially. Today, we talk about the V2X systems in which the vehicle is strongly connected with the rest of the world. In this scenario, the main aim of all connected vehicles vendors is to provide a secure system to guarantee the safety of the drive and persons against a possible cyber-attack. So, in this paper, an embedded Intrusion Detection System (IDS) for the automotive sector is introduced. It works by adopting a two-step algorithm that provides detection of a possible cyber-attack. In the first step, the methodology provides a filter of all the messages on the Controller Area Network (CAN-Bus) thanks to the use of a spatial and temporal analysis; if a set of messages are possibly malicious, these are analyzed by a Bayesian network, which gives the probability that a given event can be classified as an attack. To evaluate the efficiency and effectiveness of our method, an experimental campaign was conducted to evaluate them, according to the classic evaluation parameters for a test’s accuracy. These results were compared with a common data set on cyber-attacks present in the literature. The first experimental results, obtained in a test scenario, seem to be interesting. The results show that our method has good correspondence in the presence of the most common cyber-attacks (DDoS, Fuzzy, Impersonating), obtaining a good score relative to the classic evaluation parameters for a test’s accuracy. These results have decreased performance when we test the system on a Free State Attack.

In this review paper, a critical assessment of the main degradation processes in three key components of solid oxide fuel cells and electrolysers (negative and positive electrodes and the interconnect) is undertaken, attempting prioritization of respective degradation effects and recommendation of the best approaches in their experimental ascertainment and numerical modeling.Besides different approaches to quantifying the degradation rate of an operating solid oxide cell (SOC), the latest advancements in microstructural representation (3D imaging and reconstruction) of SOC electrodes are reviewed, applied to the quantification of triple-phase boundary (TPB) lengths and morphology evolution over time. The intrinsic degradation processes in the negative (fuel)This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

In recent years, a great number of applications in Internet of Things (IoT) have been developed. In this context, many methodologies and technologies are used for several frameworks, including indoor localization. In this field, as highlighted in recent years, one of the most important aims of modern indoor applications in IoT is to identify how to manage and correctly convey people flow. This study aims to investigate the most common methods, technologies and applications for indoor localization in IoT and analyze the major systems currently in use and the application of these solutions to actual conditions. Following this, we propose an innovative method to detect people flow in indoor location. Based on Bluetooth low energy (BLE) technology, in this paper we analyze the possibility to use our system for many kinds of applications. The first experimental results show good performance of our system.

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