ELASTIC · Project

Software That Distributes Sensor Data Processing From Vehicles to Cloud in Real Time

transportPilotedTRL 6

Imagine a city tramway covered in sensors — cameras, vibration detectors, connectivity modules — all generating massive amounts of data every second. Right now, you either process that data locally on the tram (fast but limited) or send it all to the cloud (powerful but slow). ELASTIC built software that automatically decides what gets processed where — some data stays on the vehicle for instant safety alerts, while heavier analysis moves to the cloud. They tested this on the real Florence tramway network in Italy, handling things like predictive maintenance and driving assistance.

By the numbers

consortium partners

countries represented

64%

industry partner ratio

SMEs in consortium

total project deliverables

demo/architecture deliverables

The business problem

What needed solving

Transit operators and smart city managers are drowning in sensor data from connected vehicles and urban infrastructure. Processing everything in the cloud is too slow for safety-critical decisions, but edge devices alone lack the power for deep analytics like predictive maintenance. There is no off-the-shelf software that intelligently splits this workload in real time while keeping data secure and energy costs down.

The solution

What was built

ELASTIC delivered a complete fog computing software architecture (final release) that distributes analytics across edge and cloud, reactive analytics and data fusion models, service wrappers for distributed analytics modules, non-functional components for security and energy efficiency, and a validated deployment on the Florence tramway network with real sensors and V2I connectivity.

Audience

Who needs this

Transit authorities operating sensor-equipped vehicle fleets (trams, buses, metros)Automotive OEMs and ADAS developers building connected vehicle platformsSmart city platform integrators managing distributed IoT sensor networksRailway operators seeking predictive maintenance from onboard sensorsLogistics companies processing real-time fleet telemetry at scale

Business applications

Who can put this to work

Public Transit & Urban Rail

enterprise

Target: Transit authorities and tramway/metro operators

If you are a transit operator dealing with sensor overload from your vehicle fleet — cameras, vibration monitors, passenger counters all generating data you cannot process fast enough — this project developed a fog computing architecture tested on the Florence tramway network that automatically distributes analytics workloads between onboard systems and the cloud, enabling real-time safety alerts alongside deeper maintenance predictions.

Autonomous & Connected Vehicles

enterprise

Target: Automotive OEMs and advanced driver assistance system (ADAS) developers

If you are an ADAS developer struggling to process heterogeneous sensor streams from distributed vehicle fleets in real time — this project built and validated a software architecture with 11 partners across 6 countries that orchestrates analytics across edge devices and cloud infrastructure. The system handles vehicle-to-infrastructure (V2I) connectivity and was designed to prepare the technological background for full autonomous mobility.

Smart City Infrastructure

mid-size

Target: City platform integrators and IoT infrastructure providers

If you are a smart city integrator dealing with geographically distributed sensor networks that need both instant local responses and large-scale cloud analytics — this project delivered a complete software architecture with distributed storage, secure communications, and energy-efficient processing. It was validated with real urban infrastructure across 25 deliverables covering everything from data fusion to service wrappers.

Frequently asked

Quick answers

What would it cost to implement this fog computing architecture?

The project data does not include pricing or licensing costs. ELASTIC was a publicly funded research project coordinated by Barcelona Supercomputing Center. Commercial terms would need to be negotiated directly with the consortium partners, several of which are industry players.

Can this scale beyond a single tramway network?

The architecture was designed for extreme-scale big data analytics across the full compute continuum from edge to cloud. While validated on the Florence tramway network, the software includes elasticity features that automatically match workload demands to available resources, making it designed for scaling across larger or multiple deployments.

Who owns the intellectual property and can I license it?

ELASTIC was funded as an RIA (Research and Innovation Action) under Horizon 2020. IP is typically shared among the 11 consortium partners across 6 countries. Licensing arrangements would need to be discussed with the coordinator (Barcelona Supercomputing Center) or relevant industrial partners.

Has this been tested in real operating conditions?

Yes. The final software architecture was executed on the Florence tramway network using real sensor infrastructure, V2I connectivity, and cloud resources. Validation deliverables confirm testing with advanced driving assistant systems, predictive maintenance, and public/private transport interaction scenarios.

What non-functional requirements does it address?

The architecture specifically targets real-time processing, energy efficiency, communication quality, and security. Dedicated software components for these non-functional properties were developed and integrated into the final architecture, as documented in the deliverables.

How does this compare to standard cloud-only analytics?

Unlike cloud-only approaches, ELASTIC combines reactive data-in-motion analytics (processed at the edge for speed) with latent data-at-rest analytics (processed in the cloud for depth) in a single solution. The system automatically distributes workloads based on suitability and data processing needs across the compute continuum.

What is the project timeline and current status?

ELASTIC ran from December 2018 to May 2022 and is now closed. All 25 deliverables were completed, including the final release of the fog computing architecture and validation results from the Florence tramway deployment.

Consortium

Who built it

The ELASTIC consortium is heavily industry-oriented: 7 out of 11 partners (64%) come from industry, with 3 SMEs adding agility alongside heavyweight research led by Barcelona Supercomputing Center. The 6-country spread across CH, ES, FR, IT, PT, and UK provides broad European coverage. With only 1 university partner, this consortium was clearly built to move technology toward deployment rather than stay in the lab. The involvement of Italian partners aligns with the Florence tramway pilot, suggesting strong local deployment capability.

BARCELONA SUPERCOMPUTING CENTER CENTRO NACIONAL DE SUPERCOMPUTACIONCoordinator · ES
SIXSQ SAparticipant · CH
THALES ITALIA SPAparticipant · IT
INFORMATION CATALYST SLthirdparty · ES
INSTITUTO SUPERIOR DE ENGENHARIA DO PORTOparticipant · PT
INFORMATION CATALYST FOR ENTERPRISE LTDparticipant · UK
THALESparticipant · FR
CITTA METROPOLITANA DI FIRENZEparticipant · IT
IKERLAN S. COOPparticipant · ES

How to reach the team

Barcelona Supercomputing Center (BSC-CNS), Spain — reach out to the project coordinator for licensing and technical collaboration inquiries.

Order a report on this consortium See BARCELONA SUPERCOMPUTING CENTER CENTRO NACIONAL DE SUPERCOMPUTACION profile →

Next steps

Talk to the team behind this work.

Want to connect with the ELASTIC team for fog computing solutions in transport or smart city applications? SciTransfer can arrange a direct introduction to the right technical contact.

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