Interference-Aware Routing for Collaborative Embodied AI in Industrial IoT Networks

Industrial Internet of Things deployments increasingly rely on swarms of autonomous UAVs for real-time inspection, logistics monitoring, and predictive maintenance. These UAVs must communicate while moving rapidly in 3D space, where industrial machinery generates strong electromagnetic interference and localized radio dead zones, making reliable multihop communication difficult. Geographic forwarding selects the next hop closest to the destination on a map but ignores actual radio conditions, while most learning-based protocols update too slowly to track link changes that occur within seconds. This article presents the Fast Adaptive Interference-aware Routing (FAIR) scheme, a fully distributed protocol in which each UAV scores candidate next hops by combining link reliability estimated from radio signal quality, predicted link lifetime, and geographic progress toward the destination. This enables proactive avoidance of interfered links without any extra probing traffic. FAIR further adjusts how aggressively it updates routing preferences according to current link stability. Simulation results show that FAIR consistently achieves higher packet delivery ratio, lower end-to-end latency, and lower energy consumption than two benchmark protocols across a wide range of conditions, demonstrating its potential as a practical communication backbone for collaborative UAV systems in industrial environments.