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
An increasing number of real-time embedded applications present high computation requirements which need to be realized within strict time constraints. Simultaneously, architectures are becoming more and more heterogeneous, programming models are having difficulty in scaling or stepping outside of a particular domain, and programming such solutions requires detailed knowledge of the system and the skills of an experienced programmer. In this context, this paper advocates the transparent integration of a parallel and distributed execution framework, capable of meeting real-time constraints, based on OpenMP programming model, and using MPI as the distribution mechanism. The paper also introduces our modified implementation of GCC compiler, enabled to support such parallel and distributed computations, which is evaluated through a real implementation. This evaluation gives important hints, towards the development of the parallel/distributed fork-join framework for supporting real-time embedded applications.

Abstract
The ENCOURAGE project tionalizing energy usage in building by implementing a smart energy grid based on intelligent scheduling of energy consuming appliances, renewable energy production, and inter-building energy trading. This paper presents the reference architecture proposed in the context of the ENCOURAGE project, and relates it with the goals of its research efforts.

Abstract
The aims of the TheRoute (Tourism and Heritage Routes including Ambient Intelligence with Visitants’ Profile Adaptation and Context Awareness) project is to conduct studies, research and experimentation around the challenge of automatic generation of routes for visitors. The suggested routes fit the profile of visitors and groups of visitors, including aspects like emotion, mood and personality, and be aware of the context (e.g. weather, security). TheRoute is developed according the Ambient Intelligence perspective. At this point of the project execution we have already developed the full system architecture as a System of Systems approach according to an Ambient Intelligence perspective, to allow the best possible performance in the system utilization for the final user. Intelligent route generation uses user preferences for the categories of points of interest, as well as their personality traits.

Abstract
This paper introduces RVAF, a runtime verification (RV) extension of the Arrowhead Framework (AF) with container-based service-deployment and runtime-enforcement of a desired quality of service (QoS). AF is a service-oriented middleware architecture for IoT-applications, consisting of a set of core and auxiliary services and systems, respectively. The QoS manager (QoSM) is one AF's most important auxiliary systems, which can be used to guarantee the application's QoS for a wide set of parameters. In RVAF the QoS offered to a particular IoT-application is specified in signal temporal logic, and is continuously monitored by the RVAF-QoSM. In case of an imminent violation, RVAF automatically initiates a container-based reconfiguration, which is ensured to maintain the desired QoS. RVAF is beneficial to large IoT-applications, where the use of continuous-integration and continuous-deployment tools, is not only a recommended practice but also a necessity. Moreover, the use of RVAF is advantageous both during the development of an IoT application, and after its deployment. We describe the architecture of RVAF, provide its formal underpinning, and demonstrate the usefulness of RVAF supported by an industrial IoT application. The main contribution of this work is to show what it takes to incorporate RV concepts into modern SOA frameworks supporting the development of IoT applications.