Use Cases & Trials
UC CITIES 1 - Connected and Sensing City
Enhanced City Services with Autonomous Mobility
The scenarios to be developed during the UC-CITIES-1 use case, leveraging the capabilities of the Aveiro testbed, involve various types of vehicles, including autonomous ones, as well as people and surrounding sensors, such as environmental and mobility sensors (e.g., video cameras, radars, and LiDARs). All these elements will be interconnected, exchanging real-time information between sensors, roads, and the infrastructure.
Partners
Scenarios
High-distribution Video on the Move with Edge Service Migration
A car with a camera mounted on it starts a trip. The content of its stream is offloaded via cellular network to the edge infrastructure where a service performs object detection.
The stream processing service is replicated on demand in suitable edge nodes according to the path that the car will traverse. As the car moves its mobility information is used to decide which service replicas should be activated or not, or when a network handover should be performed.
Enhanced City Services with Autonomous Mobility
A car connects to the 5G Non-Public network by Roaming, with local-breakout, between IT’s and ALB 5G cores through an antenna located in IT’s infrastructure connected to ALB’s core via optical fibre.
The autonomous vehicle then starts a camera feed stream in a compromised link. When the performance limitations are detected, a slice with more Uplink Bandwidth is created via Slice Manager and associated with the vehicle’s user equipment. A Re-Registration request is sent to the core network, also via Slice Manager, to enforce the slice on the user equipment, improving the link and allowing the stream to perform without issues.
Digital Twin and Traffic Avoidance
A tester uses the Aveiro Digital Twin application to perform a 2D traffic simulation using SUMO. The user inserts a customizable number of vehicles in the simulation, triggering a dynamic Kubernetes deployment of a distributed 5G Radio Access Network through an Infrastructure API, using UERANSIM running V2X-enabled services. The simulation will randomly place and route each one of the created vehicles in the simulation map and execute until no vehicles are seen. The simulation performance KPls are scraped from the emulated OBUs and their radio interfaces and monitored using the ECLM platform.
Key Objectives
Enable real-time object detection via edge-offloaded video streams.
Dynamically replicate edge services based on vehicle mobility.
Ensure seamless network handovers for moving vehicles.
Seameless user handling to enforce network slices.
Simulate city traffic using the Aveiro Digital Twin.
Deploy a distributed beyond 5G network for V2X services.
Randomly route vehicles in a traffic simulation map with emulated 5G network traffic.
Collect and analyse KVI related data from telco experts and more stakeholders.
Expected Innovations/Outcomes
Real-time edge-based object detection using B5G/6G ultra-low latency for seamless video stream processing in autonomous vehicles.
Dynamic service migration in B5G/6G networks to optimize resource allocation based on vehicle mobility patterns.
B5G/6G-enabled network slicing for adaptive uplink bandwidth, ensuring robust camera feeds in compromised conditions.
Distributed 6G RAN deployment for V2X, enhancing traffic simulation accuracy via high-capacity, low-latency links.
Digital twin integration with beyond 5G network for traffic management and predictive mobility analytics.
KPIs
- Num_processed_frames: Measures the number of frames processed by service replicas in Scenario 1, indicating edge processing efficiency.
- Ul_bitrate: Tracks uplink bitrate in bits per second for Scenarios 1 and 2, ensuring robust data transmission for vehicle streams.
- Cqi: Reflects channel quality reported by UE to gNB in Scenarios 1 and 2, critical for B5G/6G network reliability.
- FPS: Measures frames per second in Scenario 2, assessing video stream performance for autonomous mobility.
- Vehicle_count: Counts simulated vehicles in ready state in Scenario 3, evaluating digital twin simulation scale.
- Ue_rx_errors: Tracks average errored packets received per vehicle in Scenario 3, monitoring V2X communication reliability.
KVIs
Less traffic congestion and less manual mobility monitoring.
Smoother assisted and autonomous driving, and fewer traffic incidents.
Better traffic data access and higher smart services (sensor perception and assisted driving) use.
Use Case Alignment with UN SDGs
UC-CITIES-1 leverages real-time data, AI, and advanced connectivity to create safer, more efficient, and sustainable urban environments, directly advancing SDGs on health, innovation, sustainable cities, climate action, good governance, and partnerships.
Real-time mobility data, AI-powered traffic avoidance, and autonomous vehicle coordination improve road safety, reduce accidents, and support emergency response (Target 3.6).
The deployment of 5G/6G, edge/cloud/IoT integration, and AI-driven city management exemplifies advanced digital infrastructure and fosters urban innovation (Target 9.1, 9.5).
Smart mobility, traffic optimization, and environmental sensing improve transport efficiency, reduce congestion and emissions, and enhance urban quality of life (Target 11.2, 11.6).
Environmental sensors and data-driven urban management enable cities to monitor air quality, optimize energy use, and support climate adaptation strategies (Target 13.2).
The smart city platform involves collaboration among governments, industry, academia, and citizens, fostering innovation through multi-stakeholder partnerships (Target 17.17).
Trials
- Trials with public and invited stakeholders @ Instituto de Telecomunicações, Aveiro, 28th of May 2025.
- Trials and city demos @ Aveiro Tech Week, October 2025.
