Collecting and analysing large amounts of data in the Cloud-to-Edge computing continuum raises novel challenges. Processing all this data centrally in cloud data centresis not feasible anymore astransferring large amounts of data to the cloud istime-consuming, expensive, degrade performance and may raise security concerns. Therefore, novel distributed computing paradigms, such as edge and fog computing emerged to support processing data closer to its origin. However, such hyper-distributed systems require fundamentally new methods. To overcome the limitation of current centralised application management approaches, Swarmhestrate will develop a completely novel decentralised application-level orchestrator, based on the notion of self-organised interdependent Swarms. Application microservices are managed in a dynamic Orchestration Space by decentralised Orchestration Agents, governed by distributed intelligence that provides matchmaking between application requirements and resources, and supports the dynamic self-organisation of Swarms. Knowledge and trust, essential for the operation of the Orchestration Space, will be managed through blockchain-based trusted solutions using methods of Self-Sovereign Identities (SSI) and Distributed Identifiers (DID). End-to-end security of the overall system will be assured by utilising state-of-the-art cryptographic algorithms and privacy preserving data analytics. Due to the imminent complexity of the decentralised system, novel simulation approaches will be developed to test and optimise system behaviour (e.g., energy efficiency) in the early stages of development. Additionally, the simulator will be further extended into a digital twin running in parallel to the physical system and improving its behaviour with predictive feedback. The Swarmchestrate concept will be prototyped on four real-life demonstrators from the areas of flood prevention, parking space management, video analytics and a digital twin of natural habitat.

Public description Sharing information about emerging threats and cybersecurity breaches with trusted partner organisations is an effective strategy for enhancing the collective defence of organisations against cyber threats. By pooling and leveraging their knowledge and experiences, organisations can strengthen their cybersecurity and protect against similar threats. This collaborative approach facilitates the timely detection, response, and mitigation of cyber threats. The key benefits of sharing cybersecurity threat information include the following: Early Warning: Sharing information about emerging threats and cybersecurity breaches can provide early warning to other organisations that may be at risk. This can enable proactive measures to be taken to prevent or mitigate potential cyber attacks, such as implementing security patches, updating configurations, or strengthening defences. Knowledge Exchange: Trusted partner organisations can share knowledge of current and historical cyber threats, including attack vectors, techniques, and indicators of compromise. This can help other organisations to better understand the evolving threat landscape, identify potential vulnerabilities, and enhance their threat detection and response capabilities. There are a few other benefits such as improved detection and response time, risk reduction and collaborative defence with partner organisations. In conclusion, sharing threat intelligence with trusted partners is an essential practice for organisations seeking to improve their cybersecurity.

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The project would aim i) to design new monitoring system based on optical sensors technology coupled with novel artificial intelligence algorithms, for autonomous, and rapid detection of microplastics in environmental samples, ii) quantify microplastics in sludge and soil receiving sludges with the new technology and established protocols, iii) assess the microbial community associated with microplastics, and iv) determine if microplastics affects antimicrobial resistance in sludges and soil receiving sludges using culture dependent and independent methods. Outcomes from this research will help us to assess the extent of microplastics distribution in such samples, how it could affect microorganisms and spread AMR, and to directly inform environmental protection agencies to consider for future update in legislation

Improving Healthcare AI-Support Systems for Visually Detectable Diseases: A Mixed Learning Approach on the Edge