The project is divided into work packages according to these objectives:
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WP1. Abstract Modelling of Adaptive Smart Areas |
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Task 1.1 State of the Issue to be addressed |
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D 1.1: State of the art about Adaptive Smart Areas |
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Task 1.2 Requirements and analysis of the proposed case studies |
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D 1.2: Model of use case scenarios |
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D 1.3: Model of components and relationships |
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Task 1.3 Abstract Model for COSASS |
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D 1.4: COSASS Abstract Model |
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Task 1.4 Analysis of KPIs |
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D 1.5: List of KPIs for Adaptive Smart Areas |
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WP2. Data Acquisition Infrastructure |
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Task 2.1. Study and implementation of low-energy IIoT infrastructures |
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D 2.1: Technical report of IIoT infrastructures |
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D2.2: IIoT infrastructure designed |
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Task 2.2. Edge Computing technologies for information collecting and processing at the network edge |
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D 2.3: Machine learning models to be run at the edge of the network designed |
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Task 2.3. Software Defined Virtual Edge-IoT Networks for dynamic reconfiguration of network resources |
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D 2.4: Report of network devices in configuration costs of IIoT networks |
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D 2.5: Design of SDN and Deep Reinforcement Learning algorithms for dynamic reconfiguration of network |
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WP3. Edge decision making |
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Task 3.1. ETL layers and Smart data processing close to RT |
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D 3.1: Design of ETL and Smart Data technologies |
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Task 3.2. Low level decision making on the Edge |
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D 3.2: Design of AI Algorithms |
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Task 3.3. Federated Learning and MA-DRL mechanisms for distributed training of models |
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D 3.3: Multi-agent deep reinforcement learning mechanisms designed |
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D 3.4: Identification of external systems and sources |
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WP4. Cloud decision making |
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Task 4.1 Multi-agent coordination for limited real-time resource sharing |
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D 4.1: A set of protocols for sharing time-restricted limited resources. |
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D 4.2: Analysis of the time-boundaries of the set of protocols defined. |
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Task 4.2 Digital Twins and Federated Digital Twins (DT-FDT) module |
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D 4.3: DT-FDT design specification and DT-FDT module implementation |
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Task 4.3. Federated-like learning |
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D 4.4: A set of Federated-like Learning algorithms for the cloud level. |
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D 4.5: A set of Federated-like Learning algorithms to interact with the edge level of the system |
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WP5. AI-enabled continuum from Cloud to Edge |
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Task 5.1 Formalisation of the cloud-edge context |
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D 5.1: Report with specification of formal model for cloud-edge context and selection/instantiation methodology |
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Task 5.2 Cloud-edge context monitoring and event management |
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D 5.2: Report with detection of relevant cloud-edge context change |
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Task 5.3 Adaptation of cloud-edge continuum to contingencies |
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D 5.3: Report with adaptation methods for edge-cloud continuum contingencies |
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Task 5.4 Design and development of cloud-edge self-adaptable decision making processes |
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D 5.4: Report with cloud-edge self-adaptable decision making methods |
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WP6. Trustworthy AI Framework |
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Task 6.1 Legal and ethical requirements |
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D 6.1: Report with ethical and legal requirements for trustworthy AI solutions in Adaptive Smart Areas |
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Task 6.2 Legal compliance |
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D 6.2: Report with legal compliance study of proposed solutions |
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Task 6.3 Decision explanation and argumentation |
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D 6.3: Report with explainability needs |
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D 6.4: Framework for explanation generation of cooperative decision making solutions |
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WP7. Use cases (Viticulture) |
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Task 7.1. Adaptive Smart Area: Continental Vineyard |
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D 7.1: Functional and technical requirements report |
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D 7.2: Design of the edge/IoT model and algorithms specification |
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D 7.3: Final outcomes |
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Task 7.2. Adaptive Smart Area: Mediterranean Vineyard |
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D 7.4: Microclimate characterization report |
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D 7.5: Design of the Federated-like algorithm |
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D 7.6: Final outcomes |
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Task 7.3 Agriculture field monitoring and vehicle coordination |
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D 7.7: Simulation specification and evaluation report |