Tyndall National Institute - Reports
Permanent URI for this collection
Browse
Recent Submissions
Item AI in Energy(AIOTI – Alliance for AI, IoT and Edge Continuum Innovation, 2024-11-21) Zavodovski, Aleksandr; Gebremehdin, Alemayehu; Matta, Andrea; Kung, Antonio; Krukowski, Artur; Hovsto, Asbjorn; Mukherjee, Avishek; Zachäus, Carolin; Filipovic, Damir; Pongracz, Eva; Silva, Fabio; Hamzeh Aghdam, Farid; Svenn, Flemming; Etminan, Ghazal; Funk, Hannah; Soldatos, Ioannis; Cornec, Léo; Al-Naday, Mays; Rasti, Mehdi; Samovich, Natalie; Frigerio, Nicla; Cerny, Ondrej; Vermesan, Ovidiu; Gusmeroli, Sergio; Atmojo, Udayanto Dwi; Frascolla, Valerio; Karagiannis, Vasileios; Samovich , Natalie; Sofia, RuteThe rapid integration of Artificial Intelligence (AI), 5G, and upcoming 6G technologies into the energy sector signifies a transformative and long-term transitional shift towards a more sustainable and efficient future enabled by myriads of digital technologies. This paper focuses on the potential and challenges of leveraging AI across various segments of the energy landscape and stakeholders, including generation, distribution, and consumption within smart grids. Through comprehensive and dynamically evolving digital infrastructures the advanced data analytics, real-time monitoring, and predictive modelling, AI enhances the flexibility, reliability, and efficiency of energy systems. The convergence of AI and 6G technologies is particularly critical in optimising the performance of renewable energy sources, rolling out smart grid solutions to ensure grid stability, and fostering a resilient energy ecosystem. Key insights from this paper highlight the rapidly evolving role of AI in driving energy innovations, addressing challenges and policy and standardisation aspects, the ethical and cybersecurity considerations that accompany the deployment of AI solutions and technologies. The system and digital technologies stakeholders are embracing the full potential of AI to create a cleaner, smarter, and more resilient energy.Item Edge driven Digital Twins in distributed energy systems: Role and opportunities for hybrid data driven solutions(Alliance for IoT and Edge Computing Innovation (AIOTI)., 2024-01-29) Pereira, Ana; Hovstø, Asbjørn; Rodrigues, Eva; Silvia, Fábio; Suciu, George; AL-Naday, Mays; Samovich, Natalie; Kalenskyy, Oleg; Cerny, Ondrej; Santiago, Rita; Schmitt, Laurent; Atmojo, Udayanto Dwi; Samovich, NatalieItem AI for better health: AIOTI WG Health white paper(AIOTI -Alliance for IoT and Edge Computing Innovation, 2022-12-12) Dionisio, Pietro; Nikolov, Roumen; Gyrard, Amelie; Petelova, Katerina; Eberle, Wolfgang; Sanjuan Vinas, Lucas; San Segundo, Ruben; Tedesco, Salvatore; Andonegui, Cristina Martín; O’Murchu, CianThis white paper introduces the AIOTI WG Health vision and contribution to the European discussions on AI strategies applied to the health domain. The main aim of this paper is to contribute to the identification and addressing of any issues that might be hindering the wider adoption of AI technologies in the healthcare sector based on AIOTI WG Health’s members’ best practices. In conclusion: In the post-covid-19 era, the healthcare domain represents a unique opportunity for favouring the AI wider adoption. In fact, the healthcare domain is suffering a huge pressure due to internal and external massive changes that are pushing a development and a transformation of its dynamics. In this transformation process, AI can play a major role for smoothing the human and economic resources’ allocation and improving healthcare treatments management. According to this white paper, we believe that AI has an important role to play in the healthcare offerings of the future. Despite this, relevant challenges that have to be addressed. We tried to provide some answers to this challenges highlighting which is the AIOTI WG Health vision according to its members’ point of view. As addressed several times throughout the document, the greatest challenge to AI in the healthcare domains is not whether the technology will be capable enough to be useful, but rather ensuring its adoption in daily clinical practice overcoming barriers such as mistrust and scepticism. For widespread adoption to take place, AI systems must be approved by regulators, integrated with healthcare systems practices, standardised to a sufficient degree that similar products work in a similar fashion, taught to clinicians, paid for by public or private payer organisations. These challenges will ultimately be overcome, but time is needed. In Europe a strong push is encouraged towards AI adoption in daily practices, as testified by the key experiences included in the paper (ref. par. 9) but stronger alignment among EU member states, as well as within each country among the various decision making and legislative institutions, could further facilitate the AI wider adoption. As a result, we expect to see an ever more increasing use of AI in health daily practice within the next future connected to an increasing mingling with other AI application area such as smart cities and green economy.Item Energy harvesting for a green internet of things: PSMA white paper(PSMA, 2021-09-30) Becker, T.; Borjesson, V.; Cetinkaya, O.; Baoxing, C.; Colomer-Farrarons, J.; Maeve, D.; Elefsiniotis, A.; Govoni, L.; Hadas, Z.; Hayes, M.; Holmes, A. S.; Kiziroglou, M. E.; La Rosa, R.; Miribel-Català, P.; Mueller, J.; Pandiyan, A.; Plasek, O.; Riehl, P.; Rohan, James F.; Sabaté, N.; Saez, M.; Samson, D.; Sebald, J.; Spies, P.; Vikerfors, A.; Yeatman, E.; Zaghari, B.; Zahnstecher, B.The ubiquitous nature of energy autonomous microsystems, which are easy to install and simple to connect to a network, make them attractive in the rapidly growing Internet of Things (IoT) ecosystem. The growing energy consumption of the IoT infrastructure is becoming more and more visible. Energy harvesting describes the conversion of ambient into electrical energy, enabling green power supplies of IoT key components, such as autonomous sensor nodes. Energy harvesting methods and devices have reached a credible state-of-art, but only a few devices are commercially available and off-the-shelf harvester solutions often require extensive adaption to the envisaged application. A synopsis of typical energy sources, state-of-the-art materials, and transducer technologies for efficient energy conversion, as well as energy storage devices and power management solutions, depicts a wide range of successful research results. Developing power supplies for actual usage reveals their strong dependence on application-specific installation requirements, power demands, and environmental conditions. The industrial challenges for a massive spread of autonomous sensor systems are manifold and diverse. Reliability issues, obsolescence management, and supply chains need to be analyzed for commercial use in critical applications. The current gap between use-case scenarios and innovative product development is analyzed from the perspective of the user. The white paper then identifies the key advantages of energy autonomy in environmental, reliability, sustainability, and financial terms. Energy harvesting could lead to a lower CO2 footprint of future IoT devices by adopting environmentally friendly materials and reducing cabling and battery usage. Further research and development are needed to achieve technology readiness levels acceptable for the industry. This white paper derives a future research and innovation strategy for industry-ready green microscale IoT devices, providing useful information to the stakeholders involved: scientists, engineers, innovators, the general public, and decision makers in industry as well as in public and venture-funding bodies. This inclusive strategy could bridge the energy harvesting technology frontier and the IoT node power demands to create value.Item AI at the Edge, 2021 EPoSS White Paper(EPoSS: European Technology Platform on Smart Systems Integration, 2021-04) Bierzynski, Kay; Calvo Alonso, Daniel; Gandhi, Kaustubh; Lehment, Nicolas; Mayer, Dirk; Nackaerts, Axel; Neul, Reinhard; Peischl, Bernhard; Rix, Nigel; Röhm, Horst; Rzepka, Sven; Seifert, Inessa; Steimetz, Elisabeth; Stree, Bernard; Tedesco, Salvatore; Veledar, Omar; Wilsch, Benjamin