Authors
Norkobil Saydirasulov Saydirasulovich, Dilmurod Abdujalilovich Davronbekov, Makhmudov Makhsum Mubashirovich, Young Im Cho
Published in
Sensors (Basel, Switzerland). Volume 26. Issue 15. Aug 04, 2026. Epub Aug 04, 2026.
Abstract
Which runtime governance mechanisms reduce physical risk when a learned controller drives a building's climate, and under what conditions? We develop a closed-loop software-in-the-loop testbed in which a setpoint model trained on real occupancy data drives a physics-based thermal zone through a declarative governance plane, with outcomes scored by an independent safety oracle, and we run the same governance logic on a real MQTT stack with an in-process policy decision point and a hash-chained audit log. Under distribution shift, admission control reduces unsafe physical exposure by 19.4%, from 1185.3 to 954.8 °C·min, whereas adding checkpoint rollback reduces it by only a further 0.2% in the reference run (0.1-0.4% across sensor noise seeds): the governance decision takes 0.44 ms while physical recovery takes a median of 61 min. Prevention therefore outperforms recovery in the studied thermal system, and the remaining avoidable exposure is driven by the policy's estimate of occupancy context. A deterministic single-rule thermostat incurs 50% more exposure under shift while the learned controller uses a 38% higher heating demand proxy: a safety-demand trade-off, not evidence that learned control is necessary. A plant sweep yields an operating envelope criterion for inertial plants: rollback contributes materially to safety only when the plant is restored before the next command arrives and sampled before it can leave the safe set; on slower plants it removes at most 14.7%, with a transition band in between. No physical hardware was operated.
PMID:
42590695
Bibliographic data and abstract were imported from PubMed on 13 Aug 2026.
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