Luis Lino Ferreira

Abstract
The ability to predict the maintenance needs of machines is generating increasing interest in a wide range of indus-tries as it contributes to diminishing machine downtime and costs while increasing efficiency when compared to traditional maintenance approaches. Predictive maintenance (PdM) methods, based on state-of-the-art Internet of Things (IoT) systems and Artificial Intelligence (AI) techniques, are heavily dependent on data to create analytical models capa-ble of identifying certain patterns which can represent a mal-function or deterioration in the monitored machines. There-fore, a realistic and representative dataset is paramount for creating, training, and validating PdM techniques. This pa-per introduces a new dataset, which integrates real-world data from home appliances, such as refrigerators and wash-ing machines, suitable for the development and testing of PdM algorithms. The data was collected on various home ap-pliances at a repair center and included readings of elec-trical current and vibration at low (1 Hz) and high (2048 Hz) sampling frequencies. The dataset samples are filtered and tagged with both normal and malfunction types. An ex-tracted features dataset, corresponding to the collected work-ing cycles is also made available. This dataset could bene- fit research and development of AI systems for home ap-pliances' predictive maintenance tasks and outlier detection analysis. The dataset can also be repurposed for smart-grid or smart-home applications, predicting the consumption pat-terns of such home appliances.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Abstract
As IoT technologies mature, they are increasingly finding their way into more sensitive domains, such as Medical and Industrial IoT, in which safety and cyber-security are paramount. While the number of deployed IoT devices continues to increase annually, they still present severe cyber-security vulnerabilities, turning them into potential targets and entry points to support further attacks. Naturally, as these nodes are compromised, attackers aim at setting up stealthy communication behaviours, to exfiltrate data or to orchestrate nodes of a botnet in a cloaked fashion. Network covert channels are increasingly being used with such malicious intents. The IEEE 802.15.4 is one of the most pervasive protocols in IoT, and a fundamental part of many communication infrastructures. Despite this fact, the possibility of setting up such covert communication techniques on this medium has received very little attention. We aim at analysing the performance and feasibility of such covert-channel implementations upon the IEEE 802.15.4 protocol. This will enable a better understanding of the involved risk and help supporting the development of further cyber-security mechanisms to mitigate this threat.

Abstract
Renewable Energy Communities (RECs) are emerging as an effective concept and model to empower the active participation of citizens in the energy transition, not only as energy consumers but also as promoters of environmentally friendly energy generation solutions, particularly through the use of photovoltaic panels. This paper aims to contribute to the management and optimization of individual and community Distributed Energy Resources (DER). The solution follows a price and source-based REC management program, in which consumers' day-ahead flexible loads (Flex Offers) are shifted according to electricity generation availability, prices, and personal preferences, to balance the grid and incentivize user participation. The heuristic approach used in the proposed algorithms allows for the optimization of energy resources in a distributed edge-and-fog approach with a low computational overhead. The simulations performed using real-world energy consumption and flexibility data of a REC with 50 dwellings show an average cost reduction, taking into consideration all the seasons of the year, of 6.5%, with a peak of 12.2% reduction in the summer, and an average increase of 32.6% in individual self-consumption. In addition, the case study demonstrates promising results regarding grid load balancing and the introduction of intra-community energy trading.

Abstract
The remote maintenance of home appliances, like washing machines, air conditioning, and heating system is a complex problem, but with the help of the ongoing developments on Internet of Things, Data Analysis and Artificial Intelligence, the problem can now be tackled with success. This paper mostly focus in presenting the architecture developed within the aim of the SMART-PDM project for the acquisition of data on the operation of home appliances and then it also shows some preliminary results for washing machines, which give some hints on how to fine tune the system to achieve predictive maintenance and condition monitoring.

Abstract
This paper discusses the integration of emergent ICTs, such as the Internet of Things (IoT), the Arrowhead Framework, and the best practices from the area of condition monitoring and maintenance. These technologies are applied, for instance, for roller element bearing fault diagnostics and analysis by simulating faults. The authors first undertook the leading industry standards for condition-based maintenance (CBM), i.e., open system architecture-condition-based maintenance (OSA-CBM) and Machinery Information Management Open System Alliance (MIMOSA), which has been working towards standardizing the integration and interchangeability between systems. In addition, this paper highlights the predictive health monitoring methods that are needed for an effective CBM approach. The monitoring of industrial machines is discussed as well as the necessary details are provided regarding a demonstrator built on a metal sheet bending machine of the Greenbender family. Lastly, the authors discuss the benefits of the integration of the developed prototypes into a service-oriented platform, namely the Arrowhead Framework, which can be instrumental for the remotization of maintenance activities, such as the analysis of various equipment that are geographically distributed, to push forward the grand vision of the servitization of predictive health monitoring methods for large-scale interoperability.