Open & Re-usable Research Data & Software (ORD)
This call tackles the challenge of open research data and software from the perspective of their possible reuse. The objective is to create the conditions for research in any domain1 based on open or shared data and software.
The call aims at contributing to develop and disseminate appropriate research practices across the various research communities and countries. For this purpose, the researchers and their scientific communities are invited to propose, by given discipline, emerging discipline or in an interdisciplinary approach how to apply the principles of Open Science to promote reuse of research data and software. The aim is to accelerate the emergence of related tools, standards and services.
The call covers all types and versions of data and associated metadata: Collected, captured, acquired, transformed, simulated, synthetic etc. ‘Research software’ stands for software produced by researchers, and used as enablers for research activities.
Security and Privacy in Decentralised and Distributed Systems (SPiDDS)
Ease of access to numerous computational resources, communication channels and increasing data volumes has led to increased interest in decentralised and distributed systems. Decentralised and distributed architectures provide advantages such as the ease of scalability, increased fault tolerance and faster data access. New devices can be more readily configured and added to the network with minimal interference in a decentralised or distributed network, whilst these systems are more resilient with no ‘central point of failure’. Whilst there are advantages from the network not relying on a single node, this can lead to a greater number of attack vectors. Depending on how the system is implemented, security can be weaker in both decentralised and distributed systems. Additionally, these systems are more complex to maintain, and whilst privacy can be enhanced, this also provides more scope for cyberattacks. As decentralised and distributed systems become increasingly used, solutions to ensuring privacy and security in a trade-off with performance are sought.
Machine Learning-based Communication Systems, towards Wireless AI (WAI)
In recent years, we have seen the rapid growth of mobile communications and Internet of Things (IoT) networks. This trend is expected to continue, with global traffic set to increase multi-fold over the next 5 years. This poses challenges for traditional networks with respect to their design, deployment, operation and optimisation. Future service requirements will include transfer of higher data volumes with ultra-low latency, improved connectivity, increased reliability and reduced power consumption.
The next wireless networks should be able to meet the complex scenarios and non-linearity of future environments. Artificial intelligence (AI) is therefore key to achieving future requirements and dynamicity. Wireless AI looks at the implementation of Machine Learning (ML) techniques in Wireless communication systems to improve decision making, network management, and resource allocation.
Therefore, in this call we look to discover new solutions to these problems, and create new application scenarios.