Future smart healthcare systems—often referred to as Internet of Medical Things (IoMT) – will combine a plethora of wireless devices and applications that use wireless communication technologies to enable the exchange of healthcare data. Smart healthcare requires sufficient bandwidth, reliable and secure communication links, energy-efficient operations, and Quality of Service (QoS) support. The integration of Internet of Things (IoT) solutions into healthcare systems can significantly increase intelligence, flexibility, and interoperability. This work provides an extensive survey on emerging IoT communication standards and technologies suitable for smart healthcare applications. A particular emphasis has been given to low-power wireless technologies as a key enabler for energy-efficient IoT-based healthcare systems. Major challenges in privacy and security are also discussed. A particular attention is devoted to crowdsourcing/crowdsensing, envisaged as tools for the rapid collection of massive quantities of medical data. Finally, open research challenges and future perspectives of IoMT are presented.

Energy communities (ECs) are an important tool towards a fair energy transition. Hence, the European Union (EU) has positioned ECs at the centre of its energy strategy and the foreseen transformation of its energy system. This paper aims to give an overview of key aspects and challenges for the implementation of the EC concept. Firstly, the regulatory framework is examined with a focus on the new definitions for ECs introduced by the EU, Renewable Energy Communities (RECs) and Citizen Energy Communities (CECs). Secondly, examples of established ECs and their main objectives are mentioned. Additionally, based on the identified challenges and requirements of establishing ECs, the key technologies that are implemented or have the potential to be deployed in an EC are examined, as well as innovative cross-cutting services that are optimally suited to be integrated in an EC. Moreover, the data management challenges linked to some of these technologies are considered. Finally, an overview of actual or potential financing schemes to support the EC development is given. Overall, the analysis highlighted the regulatory, technical and financial aspects and challenges that ECs are facing and the need to address them so that the EC concept is effective and successful. The main challenges identified for each of these aspects are the regulatory compliance with the legal framework, the data management dimension when innovative technological concepts are adopted and the financing of new projects.

One major problem that affecting grape production is that of infestations by fungal pathogens, among which Plasmopara viticola is one of the worst, causing grapevine downy mildew. This can cause substantial damage to a vineyard, which leads to economic losses. Methods of predicting disease outbreak rely on the monitoring of meteorological parameters. With the recent development of Internet of Things (IoT) technologies, in situ data can be efficiently collected on a large scale. In this paper, a new model with early warning system implementation for grapevine downy mildew based on Narrow Band IoT (NB-IoT) and energy harvesting is presented. Models of downy mildew warning systems have evolved from the early temperature-based (and later, humidity-based) models to the latest mechanistic models which include rainfall/leaf wetness and hourly monitoring. We added parameters such as ’favorable night condition’ and ’wind speed’ as critical for sporangia spreading. The comparison of the model with the commercial iMetos^® warning system and the latest mechanistic model for three specific vineyard locations indicates a high correlation between alarms.

The integration of renewable energy sources (RESs) and the establishment of energy communities (ECs) are vital steps in achieving global sustainability goals. This paper presents a methodology for developing and validating a Smart Grid Architecture Model (SGAM)-compliant software platform designed to integrate data-driven energy services and connect physical energy assets within energy communities. The platform aims to optimize energy dispatch, enhance self-consumption, and facilitate interoperability with smart grid infrastructures. Two case studies—Polígono Industrial Las Cabezas in Spain and the IMP R&D campus in Serbia—are analyzed to highlight real-world challenges, solutions, and lessons learned. The article points to different scenarios relevant for energy community design and implementation. Lessons learned point to challenges related to device integration, production forecasting, user engagement, and regulatory barriers. The results show that the proposed SGAM platform successfully addresses technical and operational complexities, supporting energy efficiency, decarbonization, and scalability.

Tremor is most common among the movement disabilities that affect older people, having a prevalence rate of 4.6% in the population older than 65 years. Despite this, distinguishing different types of tremors is clinically challenging, often leading to misdiagnosis. However, due to advances in microelectronics and wireless communication, it is now possible to easily monitor tremor in hospitals and even in home environments. In this paper, we propose an architecture of a system for remote health-care and one possible implementation of such system focused on head tremor monitoring. In particular, the aim of the study presented here was to test new tools for differentiating essential tremor from dystonic tremor. To that aim, we propose a number of temporal and spectral features that are calculated from measured gyroscope signals, and identify those that provide optimal differentiation between two groups. The mean signal amplitude feature results in sensitivity = 0.8537 and specificity = 0.8039 in distinguishing patients having cervical dystonia with or without tremor. In addition, mean signal amplitude was shown to be significantly higher in patients with essential tremor than in patients with cervical dystonia, whereas the mean peak frequency is not different between two groups.