S m a l l S p a r k . U K

Logistics/Smart Cities

  • Real-time, local processing reduces congestion and improves air quality.

  • Low-power edge computing cuts cloud costs and bandwidth usage.

  • Real-time fleet optimisation boosts efficiency and lessens environmental impact.

  • Ultra-low latency enables agile, sustainable urban mobility decisions.

Industry Challenges

As cities become more connected, real-time data processing is essential for improving urban mobility and environmental sustainability. However, traditional cloud-based approaches introduce challenges:

  • Traffic Congestion & Poor Coordination – Delayed data processing slows response times for traffic control, leading to inefficiencies.
  • Air Pollution Monitoring Limitations – Centralised systems struggle to provide real-time air quality insights, making it harder to implement effective policies.
  • Fleet Management Inefficiencies – Logistics and public transport fleets lack real-time optimisation, increasing costs and environmental impact.
  • High Cloud Dependency & Bandwidth Costs – Smart city initiatives require instant data processing, but cloud reliance introduces delays and high infrastructure expenses.

How SmallSpark Solves These Challenges

By deploying CORTEX and AXON edge computing units, SmallSpark enables real-time intelligence in smart city applications while reducing cloud reliance. Our solutions power:

  • Traffic Flow Optimisation – Process live traffic data on-location, dynamically adjusting signals and routing to reduce congestion.
  • Real-Time Air Quality Monitoring – Analyse environmental sensor data on-site, providing instant insights to enforce policies and improve urban health.
  • Fleet Optimisation for Smart Mobility – Process GPS, vehicle, and sensor data locally to reduce fuel consumption, optimise routes, and improve efficiency.
  • Scalable Smart City Integration – Deploy edge computing at street-level, transport hubs, and fleet management centres without expensive cloud infrastructure.
  • Ultra-Low Latency Decision Making – On-device processing reduces latency by over 90%, ensuring instant adjustments in urban mobility systems.

  • Real-World Applications

    • Dynamic Traffic Signal Management – Reduce congestion by adjusting signals in real time based on vehicle and pedestrian movement patterns.
    • Real-Time Pollution Control – Identify high-emission zones and enable instant adjustments in traffic flow or public transport usage.
    • Autonomous Fleet & Public Transport Optimisation – Reduce vehicle downtime, fuel costs, and maintenance inefficiencies through predictive analytics and real-time monitoring.

  • Key Benefits

    ✔ Lower congestion and improved traffic flow through real-time data fusion.
    ✔ Better air quality insights, enabling smarter urban policies and rapid interventions.
    ✔ Up to 75% cost savings by minimising cloud service dependency for smart city applications.
    ✔ Over 90% reduction in processing latency, allowing instant, data-driven decision-making.
    ✔ Secure, real-time encrypted data handling, protecting transport and environmental data from cyber threats.

Get Started

Improve patient outcomes and operational efficiency with SmallSpark’s intelligent healthcare solutions. Request a demo today to explore how edge computing can transform your healthcare system.