Dependency-Aware Reliable Orchestration for Smart-IoT Control with Reasoning-Aligned LLMs

Abstract

Large Language Models (LLMs) offer a natural interface for smart-IoT control, yet reliable deployment requires more than producing valid API calls. Multi-device commands often contain preconditions, ordering constraints, and conflicts that must be satisfied before actions are safely executed. This paper presents DARIO, a dependency-aware orchestration framework for translating natural-language commands into verified IoT action plans. DARIO combines supervised instruction tuning, KL-regularized PPO, an explicit dependency graph $\mathcal{G}=(V, E)$, and a lightweight verifier that checks schema, execution, dependency, and safety constraints before commit. Verifier outcomes are folded into a decomposed reward, enabling the policy to learn from dependency violations rather than treating plans as flat token sequences. On a 2k-prompt held-out HA-Instruct split across five seeds, DARIO achieves $0.85 \pm 0.01$ JSON exact match, perfect schema validity, $0.91 \pm 0.01$ overall task success, and $0.81 \pm 0.02$ success on the dependency-heavy L3 split, outperforming SIT and SIT+PPO baselines by large margins. It also raises dependency satisfaction to 0.94, reduces unsafe execution to 0.02, and runs as a 4-bit 8B LoRA deployment at about 609 ms including verification.