Internet of vehicles in smart cities: AI-Powered QoS & energy savings across edge-cloud IoT

Smart cities depend on sophisticated IoT networks that generate massive data streams requiring intelligent processing and optimisation. The Internet of Vehicles (IoV) exemplifies this challenge, demanding ultra-low latency and high reliability that traditional cloud architectures struggle to deliver consistently.

Edge-cloud integration offers solutions by processing critical data locally whilst leveraging cloud resources for complex analytics. However, optimising these distributed systems requires advanced approaches that balance energy efficiency with quality-of-service demands. AI-driven optimisation techniques, including genetic algorithms and deep reinforcement learning, provide powerful tools for managing these competing requirements.

These optimisation methods enable dynamic resource allocation, predictive load balancing, and automated system tuning that adapts to changing urban conditions. The result is IoT infrastructure that performs more efficiently whilst supporting the real-time applications essential for smart city operations, from autonomous vehicles to emergency response systems.