Cross-CPP · Project

Connect Data From Cars, Homes, and Weather to Create Smarter Services

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Imagine your car collects weather data as it drives, your smart home knows when you'll arrive, and a weather station fills in the gaps. Right now, all that data sits in separate silos — your car manufacturer has theirs, your smart thermostat has its own, and the weather service keeps its data locked away. Cross-CPP built a platform that pulls all these data streams together so companies can create services that none of them could offer alone — like predicting your home energy use based on your driving patterns and local weather. Think of it as a secure marketplace where different industries can safely share and combine their product data without giving away trade secrets.

By the numbers

consortium partners

countries represented

industrial demonstrators in real environments

industrial case studies (VW, SIM, ML)

industry partners in consortium

67%

industry partner ratio

total project deliverables

The business problem

What needed solving

Companies with connected products — cars, smart home devices, industrial equipment — collect massive amounts of sensor data but can only analyze it within their own sector. A car manufacturer can't easily combine vehicle data with weather forecasts and home energy usage to create smarter services, because the data sits in incompatible systems with different privacy rules. This means billions of data points go underused, and cross-industry service opportunities worth real revenue are left on the table.

The solution

What was built

The project delivered a cloud-based ecosystem with a common marketplace for multi-sectorial data services, a toolbox for real-time and predictive cross-stream analytics, and a dynamic security/privacy policy engine. These were validated through 3 industrial demonstrators in real operational environments with partners including Volkswagen, producing 3 detailed case studies. Services demonstrated include combining vehicle, home automation, and weather data to predict and optimize household energy consumption.

Audience

Who needs this

Automotive OEMs with connected vehicle fleets generating underused sensor dataSmart home and building automation companies wanting to enrich their services with external dataEnergy utilities looking to improve demand forecasting with cross-sector IoT dataIoT platform providers seeking to expand into multi-industry data marketplace modelsInsurance companies interested in cross-referencing vehicle, home, and weather data for risk assessment

Business applications

Who can put this to work

Automotive & Connected Vehicles

enterprise

Target: Car manufacturers or fleet operators with connected vehicle data

If you are an automotive company sitting on mountains of sensor data from connected vehicles — this project developed a platform and analytics toolbox that combines your vehicle data streams with weather and home automation data to create new cross-sectorial services. The system was demonstrated with Volkswagen as one of 3 industrial partners, proving it works in real operational environments. You could unlock new revenue from data you already collect.

Smart Home & Building Automation

mid-size

Target: Home automation providers or energy utilities

If you are a home automation provider struggling to optimize energy predictions with just your own device data — this project built a cloud-based ecosystem that merges your smart home data with vehicle movement patterns and open weather data. The result: enhanced local weather forecasts and optimized household energy consumption predictions. The platform was tested across 3 industrial demonstrators in real conditions.

Data Services & IoT Platforms

any

Target: IoT platform companies or data analytics providers

If you are an IoT platform company looking to expand beyond single-sector data analytics — this project created a marketplace and methodology for building multi-sectorial cloud-based services from diverse cyber physical product data streams. With built-in privacy, IPR, and security policy management that adapts dynamically, you can offer cross-industry analytics while respecting data ownership across 9 consortium partners from 5 countries.

Frequently asked

Quick answers

What would it cost to license or adopt this platform?

The project data does not include specific licensing costs or pricing models. The platform includes a marketplace component, suggesting a commercial model was considered. Contact the coordinator at the Bremen-based institute for licensing terms.

Can this handle industrial-scale data volumes from mass-market products?

Yes — the project was specifically designed for high-volume mass cyber physical products. It was validated with 3 industrial demonstrators including Volkswagen, processing real-time data streams from automotive and home automation sectors in operational environments.

Who owns the IP and can I use this technology?

The consortium of 9 partners across 5 countries developed this under an EU Innovation Action. IP is likely shared among consortium members including 6 industry partners. The coordinator institute in Bremen, Germany would be the first contact for licensing discussions.

How does this protect my company's confidential data when sharing across sectors?

The project built a dynamically changing security policy system with context-sensitive handling of commercial confidentiality, privacy, and IPR. This means data sharing rules adapt based on who is accessing what and in which context, rather than applying blanket restrictions.

Is this ready to deploy or still experimental?

The project completed 3 industrial demonstrators in real operational environments, moving beyond lab testing. As an Innovation Action that ended in 2021, the technology reached demonstration stage. Integration into your existing systems would still require adaptation work.

Can this integrate with our existing IoT infrastructure?

The platform was built with integration in mind — it defines shared entity identifiers and agreed data models for multi-sectorial cyber physical products, aiming at de facto standards. It builds on the earlier AutoMat project for vehicle data streams, showing it can extend existing data infrastructure.

Consortium

Who built it

The Cross-CPP consortium of 9 partners across 5 countries (Germany, Spain, UK, Czech Republic, Switzerland) is heavily industry-driven at 67% industry participation — 6 out of 9 partners come from industry, backed by 2 universities and 1 research organization. This composition signals a project built for commercial application, not academic publishing. The coordinator is a German applied systems engineering institute in Bremen. Having Volkswagen among the industrial demonstrator partners adds serious credibility for automotive applications. The relatively low SME count (1 of 9) suggests this was designed for enterprise-scale data challenges rather than startup use cases.

INSTITUT FÜR ANGEWANDTE SYSTEMTECHNIK BREMEN GMBHCoordinator · DE
METEOLOGIX AGparticipant · CH
VYSOKE UCENI TECHNICKE V BRNEparticipant · CZ
ATOS SPAIN SAparticipant · ES
SIEMENS SROparticipant · CZ
The Open Group Limitedparticipant · UK
VOLKSWAGEN AKTIENGESELLSCHAFTparticipant · DE
UNIVERSIDAD POLITECNICA DE MADRIDparticipant · ES

How to reach the team

Institut für Angewandte Systemtechnik Bremen GmbH (ATB), Germany — a research institute specializing in applied systems technology

Order a report on this consortium See INSTITUT FÜR ANGEWANDTE SYSTEMTECHNIK BREMEN GMBH profile →

Next steps

Talk to the team behind this work.

Want to explore how Cross-CPP's cross-sectorial data platform could work for your business? SciTransfer can connect you with the right consortium partner for your industry.

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