About the project

WHAT IS O-CEI?

WHAT IS
O-CEI?

The growing demand for data and computing power, driven by decentralized infrastructures like Cloud-Edge-IoT (CEI) networks, is significantly increasing energy consumption, costs, and carbon emissions. The fragmented nature of cloud ecosystems, especially when extended to the edge, with a variety of connected devices like e-vehicles and smart devices, makes it difficult to manage resources efficiently. As CEI architectures evolve, there is a clear need to enable seamless collaboration between these independent entities, allowing them to share data and insights effectively.

As a Horizon Europe Innovation Action program, O-CEI aims to create an open CEI platform with a strong focus on interoperability, security, and reliability, making it easier for different sectors to collaborate and share data in real-time. This will enable faster, more accurate predictions in energy flexibility, helping industries to become more adaptable to energy demands. By developing breakthrough technologies and deploying large-scale pilots, O-CEI will support Europe’s transition to a more sustainable and resilient digital infrastructure, paving the way for cleaner energy and more efficient use of resources.

key innovations

O-CEI will build on past investments and developments to establish a competitive market for European businesses and key innovation stakeholders. The main advancements introduced by O-CEI include:

1

A resilient multi-platform ecosystem for cross-domain data governance, lineage, distribution, and sharing

The project aims to develop an open platform and blueprints for seamless data exchange across computing ecosystems, supporting a marketplace for digital assets like services, datasets, and AI models. It will ensure data confidentiality and trusted authorization while exploring the use of semantic web standards for building knowledge graphs.

2

Reference implementations, blueprints, and platforms for Cloud, edge, and IoT

The project will create blueprints for CEI scenarios, including formalizations, reference implementations, and automation tools for deployment and operation. It will also produce a toolkit with best practices and lessons learned from real-life applications.

3

Federated identity management in cross-domain, zero-trusted environments

O-CEI will create an innovative, standardized solution that ensures seamless integration across diverse domains, all while maintaining rigorous security standards.

4

Distributed Machine Learning (ML) Operations support in cross-domain scenarios, deploying models for energy profiling and flexibility

The project will create a system that helps manage and improve machine learning across different fields, making handling code, data, and learning processes easier. This system will guide decisions about where and how training should happen, focusing on privacy and combining different approaches. Additionally, O-CEI will work on developing or improving energy use and emissions models, which can be applied across various industries.

5

Continuous automation process of blueprints’ development, integration, and deployment

O-CEI looks to create custom tools to automate the software development process for CEI computing. It will include an AI-powered system to analyze code and improve trust, acting as a smart code reviewer and advisor.

6

Standards and open standards for the CEI ecosystem

The project will support key standardization efforts, focusing on data sharing, cross-domain interoperability, CEI continuum orchestration (based on aerOS), and smart energy standards.

O-CEI Platform

O-CEI platform scheme

OBJECTIVES

O-CEI will build on past investments and developments to establish a competitive market for European businesses and key innovation stakeholders. The main advancements introduced by O-CEI include:

1

To illustrate and demonstrate the capacity of IoT-edge-cloud resource, data, and service orchestration to act as the true backbone of European energy flexibility.

Starting with eight large-scale real-world pilots, the project aims to build an open platform focused on energy flexibility (monitoring, management, and distribution). The platform seeks to enable trusted cross-domain information exchange by utilizing standardized interfaces, data models, and ontologies, thereby supporting the EU’s digital and green transition objectives.

2

To establish a reference point for deploying CEI platforms and appliances, promoting the use of trusted cross-domain interfaces for data, knowledge, and resource exchange.​

The O-CEI platform will include dedicated documentation, automation tools, and management interfaces to support the realization and deployment of new appliances and services (referred to as “CEI utilities”) across the computing continuum. Additionally, the platform will provide interfaces through an Open API, utilizing cutting-edge technologies and relevant specifications to ensure data governance, traceability, confidentiality, and privacy. The goal is to foster trust and sovereignty by limiting access to authorized information and resources, such as datasets and AI models.

3

To bring together a vibrant ecosystem of technology providers, on-the-field integrators, and real Industry actors to enhance Smart IoT platforms' pervasiveness, successfully channeling previous open-source research efforts.

O-CEI will unite experts from various fields to design and implement CEI blueprints, showcasing the effectiveness of European CEI technology in advancing key strategic sectors.
4

To develop a viable, transferable business case strategy for an Open Platform on top of a computing continuum.

The project looks to develop credible and viable business models for large-scale pilots and replicate them for commercial success in various contexts. The market adoption of the Open Platform and associated blueprints will be encouraged through the creation of an ecosystem, that looks to promote open collaboration and technology sharing, fostering replication, and sustainability. Additionally, maturity models for computing continuum adoption and sector categorization will guide the replication process

5

To maximize impact in standardization, dissemination, and communication; and to ensure long-term sustainability and ecosystem development.

O-CEI seeks to engage stakeholders and potential clients across various strategic sectors while aligning with current and future standards in the CEI domain to promote sustainability and mobilize key actors. It will focus on understanding and contributing to standards, with particular emphasis on CEI and energy flexibility use cases.

LATEST NEWS

Stay up-to-date with the latest updates and announcements about the O-CEI project.

The O-CEI Project Builds an Open Platform to Drive Energy Efficiency

Pilot 8

Heightened social engagement and acceptability of energy flexibility in urban areas

Summary

Domain
Urban

Partners
Polygones, CLEMAP, HES-SO, Val D’Anniviers

Goal
The aim is to showcase how innovative Smart IoT solutions, already deployed within networks of prosumers, can be integrated into the O-CEI platform to enhance user acceptance and engagement.

Pilot Scenarios

The aim is to develop new Smart Energy Services (SES) based on an enhanced solution.

1

Democratization of energy demand forecasting for e-vehicle charging SES

This scenario focuses on residential buildings from Meyrin, which have local grids and charging stations for electric vehicles (EVs). It enables EV owners and software vendors to optimize vehicle charging through grid insights, integrating unidirectional power flow (V1G) as well as more extended Vehicle-to-X (V2X) for energy network enhancement, with O-CEI monitoring, Smart Contracts, and new business models.

2

Advanced graph data processing and LLM for energy prediction

O-CEI Data Fabric will be employed as an information graph containing energy data and results from previous scenario, and will integrate energy data, user inputs, and AI models to forecast energy consumption and production, using edge computing, LLM models, and serverless functions for efficient data processing and collaboration.

3

Self-adaptive energy and computation continuum in distributed tourism area

Edge equipment in Val D’Anniviers will use O-CEI orchestration, cybersecurity, and Machine Learning Operations for context-aware energy forecasting, improving efficiency in villages and ski stations through decentralized analytics and continuous updates.

In all scenarios, social acceptance by design will be driven by a combination of qualitative surveys, conjoint analysis, and discrete choice experiments (DCE) optimized on edge devices.

Pilot 7

Trustworthy and secure EV charging upon reliable 5G networks

Summary

Domain
Telecom

Partners
GAP, Nova, CERTH, Ares2T

Goal
The goal of this pilot is to leverage a private 5G network and cybersecurity measures to optimize the EV charging ecosystem, improving resource balancing, reducing costs, and enabling data-driven services

Pilot Scenarios

Pilot 7 presents two complementary scenarios focused on 5G-driven EV charging.

1

Edge optimization of power flow to EV needs

This scenario transforms chargers into “intelligent” bidirectional elements, optimizing grid power flow through O-CEI microservices, data management, and 5G’s low-latency, ensuring efficient load balancing and reduced environmental impact.

2

Increasing trustworthiness and security in EV charging data exchange

The O-CEI blueprint will incorporate security measures, data sovereignty, and auditing to ensure trustworthy data flow from sensors and EVs, supporting energy balance decisions with continuous monitoring and response to cyber threats.

Pilot 6

Smart re-charging and efficiency of robot tractors in large fruit production fields

Summary

Domain
Agrifood

Partners
Garaia, Innovalia, Zettrack, Agrimac

Goal
This pilot uses O-CEI’s monitoring, data integration, and decentralized computing to optimize energy management of electric tractors in kiwi farming, reducing emissions, energy use, and addressing travel and battery cost challenges.

Pilot Scenarios

P6 introduces three scenarios aimed at enhancing the energy efficiency of agricultural operations of electric tractors.

1

Real-time energy consumption aid for agricultural decision

The O-CEI platform will provide farmers with energy optimization suggestions, managing fleet, climate, and location data. It will propose action plans for efficient area coverage and extended charging downtime, using real-time services.

2

Remote route optimization and task planning of robot tractors

The O-CEI AI-block will optimize agricultural tasks in kiwi plantations, using state-of-the-art models for efficient operations and route planning, with real-time data storage and semi-autonomic management.

3

Autonomous tractor re-charging strategy

The tractors in this pilot will optimize battery charging through edge analytics and AI predictions, either autonomously or via a farmer-controlled app, minimizing battery use, extending lifespan, and reducing costs for improved sustainability.

Pilot 5

Energetically and environmentally sustainable Halloumi cheese production

Summary

Domain
Agrifood

Partners
Green Supply Chain, Embio Diagnostics, Charalambides Christis

Goal
The pilot uses O-CEI technologies for IoT-based monitoring and energy optimization, aiming to reduce the energy footprint per kilogram of dairy product in Cyprus, promoting sustainable farming despite challenging climates.

Pilot Scenarios

Livestock farming and the dairy industry are vital to Cyprus’s economy but face significant challenges from rising energy costs and the unstable agro-environmental conditions affected by climate change.

1

IoT-Assisted Livestock Management based on Edge Intelligence and automation

The pilot optimizes energy use in stables while reducing animal heat stress, using IoT sensors and cameras for behavior classification, edge computing for HVAC control, and forecasting for efficient milk production.

2

Controlled sharing of data from various stables to the dairy production factory

O-CEI mechanisms and marketplace will securely share trusted stable data on milk quality, feed mix, and energy footprint, using AI to forecast parameters, optimize energy consumption, and improve production practices with secure data management.

3

Intelligent selection of energy mix for dairy production

This scenario optimizes cheese production’s energy sources (grid, solar, biofuel) based on demand, availability, and price, using O-CEI models and Decision Support System (DSS) to reduce costs, environmental impact, and optimize operations.

4

Calculation of energy footprint of dairy (halloumi) products

This scenario aggregates energy consumption data across the production chain, using O-CEI orchestration and IoT-edge-cloud elements to optimize energy use, enhance trustworthiness, and audit halloumi production’s sustainability.

Pilot 4

Variable demand in challenging maritime terminal landscape

Summary

Domain
Logistics

Partners
CMA CGM, Prodevelop, Awake.ai, Enemalta, Schneider

Goal
This pilot aims to optimize energy usage in Malta’s port operations by aligning demand with capacity, using edge-optimized control systems for real-time adjustments based on vessel schedules, container handling equipment (CHE) usage, and the island grid data.

Pilot Scenarios

The Malta pilot focuses on three interconnected scenarios that leverage O-CEI technologies for monitoring, prioritization, and AI-driven predictions. These scenarios demonstrate how O-CEI technologies can handle simultaneously energy management across vessels, port terminals, and residential settings to achieve a sustainable and efficient energy ecosystem.

1

Vessel Energy Management

The O-CEI platform will integrate energy flows from berthed vessels, using decentralized monitoring, IoT, and cybersecurity utilities to minimize blackouts and optimize energy control through orchestration and real-time dashboards.

2

Terminal Energy Management

This scenario integrates terminal asset energy data with O-CEI orchestration, coordinating peak power demands from Container Handling Equipment (CHE), and reefers to avoid unnecessary peaks, with individual control systems.

3

Home Consumers

This scenario replaces home electricity meters with an O-CEI orchestrated control system, managing device priorities for critical services and appliances through secure data governance and pub/sub-models of the project.

Pilot 3

Energy consumption and emission reductions in postal service fleet operation via intelligent BEV charging strategies

Summary

Domain
Mobility

Partners
Austrian Postal Service, Energie Steiermark, AVL

Goal
This pilot optimizes electric fleet operations by integrating intelligent charging, V2G, and renewable energy utilization, reducing emissions, energy consumption, costs, and enhancing grid stability.

Pilot Scenarios

The Austrian Postal Service (POST) operates 5000 electric trucks that connect to EV charging stations owned by POST or Energie Steiermark. Both the vehicles and charging stations can share data through O-CEI software services, helping to speed up the adoption of smart, sustainable energy and mobility strategies.

P3 introduces three innovative use cases designed to optimize energy consumption, enhance grid stability, and reduce emissions through the integration of O-CEI technologies. These use cases highlight how advanced energy strategies and O-CEI technologies can transform electric fleet operations and grid management, driving sustainability and efficiency.

1

Infrastructure analysis to optimize Storage Battery Systems

For this use case, we analyse the power infrastructure (PV panels, batteries, charging infrastructure), the control and communication infrastructure, and the various communication protocols. This knowledge will then be used to optimize the location and operation of stationary storage battery systems.

2

Vehicle-to-Grid (V2G) – Vehicles as Mobile Energy Storage Units

Building on the first scenario, this use case optimizes EVs as mobile storage units, utilizing O-CEI for intelligent charging, discharging, and fleet route planning, enhancing fleet resilience and battery autonomy. Among others, we will assess the economic balance of the state of health degradation of the EV battery vs. revenue generation by providing energy to the grid at market price.

3

Smart and Innovative Energy Management Systems

O-CEI will evaluate mobile storage systems, such as trucks, supporting grid peak shaving and reactive power management. A virtual power plant integrates energy delivery, billing, and regulatory data for interventions.

Pilot 2

Electricity Grid - Software Defined Vehicle for VaS in Urban Areas

Summary

Domain
Electricity Grid

Partners
Continental, FENIX2.0, ERTICO

Goal
P2 scales Continental’s innovations by integrating a Software Defined Vehicle (SDV), transforming it into an active hub for computation, data exchange, and context-aware connectivity.

Pilot Scenarios

P2 introduces four SDV scenarios, each focusing on Vehicle-as-a-Service (VaS) to address key areas of urban mobility through O-CEI-enabled technologies. These scenarios demonstrate the transformative role of SDVs as active infrastructure elements, revolutionizing safety, sustainability, energy integration, and grid optimization in urban mobility.

1

VaS for Vehicle Safety

This scenario enhances vehicle safety with AI-driven system monitoring, OTA updates, advanced cybersecurity, and real-time technologies for the reliable, secure, and dynamic operation of embedded systems (e.g., engine management, braking, steering, infotainment)

2

VaS for Sustainability

This scenario leverages AI utilities from the O-CEI marketplace to optimize EV powertrain efficiency, minimize carbon footprints, and ensure driver engagement with sustainable, real-time feedback and seamless integration.

3

VaS for Fleet V2G Integration

It uses AI to optimize EV charging and energy return via Vehicle-to-Grid (V2G) capabilities, with edge and cloud software supporting fleet-wide data analysis and operations.

4

VaS for Grid Optimization

This scenario uses V2G, smart grids, and AI to optimize energy use, schedule charging during off-peak hours, utilize dynamic pricing, and grid balancing for sustainable vehicle interactions.

Pilot 1

Electric Grid performance optimization upon RES integration

Summary

Domain
Energy grid

Partners
UCC, EDF, MDU, Powerledger, Smart MPower, Eko Kvarner

Goal
The pilot aims to transition towards a greener, more efficient, and resilient energy system, paving the way for a sustainable and robust energy future.

Pilot Scenarios

1

Community-Driven Renewable Energy Integration

The O-CEI project empowers prosumers/flexumers to trade energy, optimize usage, and store excess via AI-driven tools, boosting grid stability and renewable energy use.

2

Smart Thermal Load Management

Sensor data and smart controls dynamically manage heating for energy efficiency using edge computing.

3

Wide Smart Grid Optimization

Seasonal demand patterns are optimized with renewable integration and decentralized learning models.

4

EV and Heat Pump Synchronization

Synchronizing renewable energy for household appliances and EVs ensures comfort and charging availability.

5

Residential Energy Management

IoT gateways and secure O-CEI utilities empower users to monitor energy use and promote grid flexibility.

6

Advanced Load Scheduling

Proactive scheduling for appliances like electric water heaters and space heaters, will enhance grid capacity and user comfort using edge computing and robust connectivity.

Each of these scenarios highlights the role of O-CEI in creating innovative, sustainable, and resilient energy solutions tailored to specific community needs and environments.