During the first reporting period, CoGNETs established the scientific and technical foundations for cognitive, game-driven computing continuums. Activities combined desk research, system design, theoretical modeling, and initial software development, together with the preparation of the pilot use cases.
Core efforts focused on defining the cognitive computing continuum and translating it into concrete system requirements for the CoGNETs middleware and pilots. Based on expert input and a comprehensive state-of-the-art analysis, the overall CoGNETs architecture was defined, integrating game-theoretic intelligence, collaborative learning, and end-to-end security across IoT, Edge, and Cloud environments.
In parallel, secure and scalable engineering practices were introduced through DevSecOps and MLOps pipelines, enabling continuous integration, testing, and validation. An operational testbed was implemented and validated at TRL5, providing a stable environment for early experimentation and system integration.
On the scientific side, CoGNETs developed the foundations of decentralized, game-based intelligence. Optimization objectives were formalized using utility functions capturing latency, throughput, reliability, security, energy efficiency, and system welfare, and mapped to resource availability. A game-theoretic baseline was established, covering suitable game models, equilibrium concepts, auction mechanisms, and learning strategies for dynamic computing continuums.
Based on this framework, autonomous game-intelligent agents were designed and initially implemented to support on-device decision-making. Selected game-theoretic formulations were translated into agent-based solutions, resulting in initial software components enabling decentralized optimization.
Further work addressed collaborative intelligence through federated and split learning approaches, including distributed training models, neural network optimization techniques, and AI models aligned with the pilot domains. End-to-end security was also assessed, covering identity, privacy, resilience, and trust in heterogeneous environments.
At the middleware level, a programmable infrastructure was designed and partially implemented to orchestrate distributed resources across Edge and Cloud systems, including container-based orchestration, cluster federation, and intelligent workload distribution. Core middleware services—such as identity management, the Cognitive AI Service Repository, and a human-in-the-loop dashboard—were specified and progressively integrated, supported by deployment and operational guidelines.
Finally, CoGNETs initiated the integration of its technologies into pilot domains in manufacturing, connected driving, and healthcare. The testbed was upgraded to support pilot experimentation, initial demonstration and validation plans were prepared, and a common validation framework was defined, including baseline KPI assessments and a high-level evaluation methodology.
