One Smart City Runs 260+ AI Cameras.
They Run on 7 Different
Siloed Networks.

Traffic management AI requires sub-second sensor data from cameras, induction loops, and V2X infrastructure across hundreds of intersections simultaneously. Most city networks deliver this data with 2–5 second delays over 4G public networks — making AI optimisation reactive, not predictive. Congestion is already formed before the signal changes.

Cities deploy air quality, noise, and flood sensors on LoRaWAN or NB-IoT with shared spectrum — where 50,000 devices fight for bandwidth in 200kHz channels. During a sandstorm or flooding event (exactly when data is most critical), network congestion causes sensors to fail at reporting. Real-time environmental alerts become 15-minute-delayed batch uploads.

A city-wide AI camera network streaming 4K at 15 Mbps per camera requires 15 Gbps for 1,000 cameras — vastly beyond what public 4G/5G shared networks can guarantee. Cities end up with cameras recording locally to SD cards rather than streaming to AI analytics — defeating the real-time intelligence promise entirely.

When a public safety AI camera detects an incident and triggers an alert, the dispatch system must immediately locate the nearest patrol unit, reroute traffic signals to create a green corridor, and notify hospital emergency departments — simultaneously. On fragmented city networks, these three systems cannot coordinate. Average response improvement from V2X integration: 40%.