PRAHARI: Physics-Grounded Reputation and Assurance via a Hovering Aerial Reference for Radiological Internet of Things Networks

Networks of fixed, low-cost dose-rate sensors monitor radiological facilities, but the trustworthiness of their reports rests on heuristics: existing trust schemes score nodes by mutual voting or behavioral statistics, which fail against an adversary who falsifies data consistently with the physics the network can observe. This paper presents PRAHARI, a trust management framework in which an unmanned aerial system carrying a calibrated scintillator chain hovers at each node as a mobile metrological anchor. Trust evidence is a statistically principled compatibility test between two uncertainty-quantified measurements with an empirically verified false-alarm rate, feeding a multi-channel Beta reputation engine with wall-clock forgetting, endurance-constrained verification scheduling, and trust-gated dose mapping. Across 9640 paired Monte Carlo trials on a shielded-facility twin with randomized attack classes, PRAHARI is compared against three published network-internal trust methods, a physics-model residual detector, and an uncertainty-blind aerial band. Only aerial-reference evidence discriminates scaling and physics-consistent falsifiers (class AUC 0.80 and 0.75), while network-internal methods stay near chance (0.48 to 0.60), an advantage persisting across facility variants and up to half the adversary’s shielding knowledge, at a false-accusation rate one order of magnitude below every alternative of comparable ranking power. Sensitivity, ablation, reference-bias, and drift studies bound the claims.