5G Fog AI Optimization

Modern transportation systems are increasingly reliant on data-driven technologies to enhance efficiency, safety, and user experience. However, the real-time processing of vast amounts of data poses significant challenges, particularly in scenarios requiring immediate decision-making, such as traffic incident management and predictive analytics. By deploying a cutting-edge 5G-enabled fog computing infrastructure, we address real-time processing challenges, optimize traffic management, and enhance safety and operational efficiency.

Project Objectives

Develop Advanced AI Algorithms

Create algorithms optimized for real-time data processing in fog and cloud-fog architectures.

Enhance Traffic Management

Improve the accuracy of traffic monitoring, vehicle detection, and incident identification.

Reduce Decision Latency

Enable rapid data processing at the edge to facilitate faster and more reliable decision-making.

Increase Operational Efficiency

Provide predictive analytics and actionable insights to optimize traffic flow and reduce congestion.

Ensure Data Privacy and Security

Localize data processing at the edge to minimize unauthorized access risks.

Key Innovations

Fog Computing Infrastructure

Leverages distributed edge nodes for localized data analysis and reduced latency.

Predictive Analytics Integration

Employs AI to forecast traffic patterns, optimize flow, and mitigate potential disruptions.

Drones for Traffic Surveillance

Utilizes high-resolution sensors and cameras to monitor road conditions and provide real-time updates.

Interoperable 5G Connectivity

Ensures seamless data exchange between vehicles, infrastructure, and management centers.

AI-Driven Decision Support

Implements machine learning algorithms to enhance situational awareness and response times.

Partners

Coordinator

  • Starflix Vilnius

Partners

  • CloudAtlas
  • Matomai
  • Vilnius University

Strategic Contributions

Economic Growth

Facilitates efficient logistics and supply chain operations.

Environmental Sustainability

Reduces fuel consumption and emissions by minimizing congestion.

Smart City Integration

Advances the development of connected ecosystems in urban environments.

Case studies