AEGIS brought together the data, the network & the technologies to create a curated, semantically enhanced, interlinked & multilingual repository for public & personal safety-related big data. It delivered a data-driven innovation that expanded over multiple business sectors & took into consideration structured, unstructured & multilingual datasets, rejuvenated existing models and facilitated organisations in the Public Safety & Personal Security linked sectors to provide better & personalised services to their users.
The PISTIS project aimed to build a secure and trusted platform for sharing and trading data assets, utilizing technologies like blockchain, NFTs, and AI to ensure transparency and fair monetization. It sought to foster interoperable data spaces, facilitating collaboration and data flow across different sectors while complying with data regulations and empowering organizations to unlock the value of their data. By advancing data sharing technologies and promoting trust, PISTIS strove to revolutionize how data is exchanged and utilized, driving innovation and economic growth.
CONNECT addressed the convergence of security and safety in CCAM by assessing dynamic trust relationships and defining a trust reasoning framework based on which involved entities could establish trust for cooperatively executing safety-critical functions. This enabled both a) cyber-secure data sharing between data sources in the CCAM ecosystem that had no or insufficient pre-existing trust relationship, and b) outsourcing tasks to the MEC and cloud in a trustworthy way. Beyond the needs of functional safety, trustworthiness management should be included in CCAM’s security functionality solution for verifying trustworthiness of transmitting stations and infrastructure. CONNECT built upon and expanded the Zero Trust concept to tackle the issue of how to bootstrap vertical trust from the application, the execution environment and device hardware from the vehicle up to MEC and cloud environments. This included measuring the system when instantiating network functions and determining the integrity and origin of software. Trusted Execution Environments (TEEs), as sw- or hw-based security elements, were essential to establish a verifiable chain of trust throughout the entire application stack of the host vehicle, as well as protecting data in transit, at rest and in use. By coupling the Zero Trust security principle with the need of “Never Trust, Always Verify”, CONNECT bootstrapped vertical trust for all users, devices and systems in the CCAM ecosystem by enabling continuous authorization and authentication prior to be granted access to data or resources. Through TEE-enabled “Chip-to-Cloud”” assurances and verifiable chain of trust, CONNECT reached its full potential: not only did it mitigate risks stemming from the Zero Trust CCAM environment but also ensured resilience. This could make CONNECT the cornerstone of future smart transportation as it would usher new levels of safety and connectivity and bring vehicles even close to autonomy.
HAIKU paved the way for human-centric AI in the aviation domain. The main challenge was to deliver truly human-centric AI-based Intelligent Assistant prototypes, capable of integrating human values, needs, abilities and limitations. These Intelligent Assistants dynamically learned from human users and continuously evolved over time.To successfully achieve this challenge, HAIKU paid special attention to the safety, security and ethics aspects of IA-based Intelligent Assistants.
The European aviation industry needs to leverage the surge of multi-source and multi-lingual data streams to gain augmented intelligence on its status quo and open up a wide spectrum of unprecedented services for the whole ecosystem (airlines, airports, passengers, service providers, manufacturers, local authorities, etc.).
ICARUS built a novel data value chain in the aviation-related sectors towards data-driven innovation and collaboration across currently diversified and fragmented industry players, acting as multiplier of the “combined” data value that could be accrued, shared and traded, and rejuvenating the existing, increasingly non-linear models / processes in aviation. Using methods such as big data analytics, deep learning, semantic data enrichment, and blockchain powered data sharing, ICARUS addressed critical barriers for the adoption of Big Data in the aviation industry (e.g. data fragmentation, data provenance, data licensing and ownership, data veracity), and enabled aviation-related big data scenarios for EU-based companies, organizations and scientists, through a multi-sided platform that allowed exploration, curation, integration and deep analysis of original, synthesized and derivative data characterized by different velocity, variety and volume in a trusted and fair manner.
ICARUS brought together the Aerospace, Tourism, Health, Security, Transport, Retail, Weather, and Public sectors and accelerated their data-driven collaboration under the prism of a novel aviation-driven data value chain.
Representative use cases of the overall domain’s value chain included:
(I) Sophisticated passenger handling mechanisms and personalised services on ground facilities,
(II) Enhanced routes analysis of aircrafts for improved fuel consumption optimisation and pollution awareness,
(III) More accurate and realistic prediction model of epidemics,
(IV) Novel Passenger experiences pre-in- and post-flight.