DISCO: Distributed Spectrum Compliance and Orchestration for Scalable IoT Coexistence

Abstract

Massive Internet of Things (IoT) deployments increasingly share spectrum with incumbent, licensed, and unlicensed systems under uncertain traffic, fading, mobility, and intermittent coordination. Existing mechanisms, including fixed power limits, listen-before-talk procedures, spectrum access databases, and learning-based resource allocation, address important aspects of coexistence, but they do not provide a common control plane to translate a network-wide interference risk budget into lightweight guidance for many autonomous devices. This article introduces Distributed Spectrum Compliance and Orchestration (DISCO), a hierarchical architecture that separates local spectrum learning from edge-level compliance regulation and slower cloud or non-terrestrial-network context adaptation. DISCO is not presented as a new reinforcement-learning optimizer or as a replacement for statutory spectrum rules. Its contribution is a deployable compliance plane that monitors violation statistics, broadcasts a compact governance signal, and adjusts policy aggressiveness without centralizing every transmission decision. A 30-seed UAV coexistence case study illustrates the efficiency--risk trade-off: the reported mean throughput is 81.0~Mbps, 73\% above fixed-power control, while the mean violation rate is 0.053 compared with 0.126 for uncoordinated learning. Because the 95\% confidence interval, [0.030, 0.076], crosses the nominal target of 0.06, the evidence supports statistical regulation near the target, not guaranteed regulatory compliance. Deployment, complexity, adoption boundaries, and open validation requirements are discussed explicitly.