<b>Empirical Performance Evaluation of Heterogeneous IoT Sensor Clusters for Real-Time Industrial Gas Dispersion Tracking</b>

Existing evidence is fragmented in a specific and consequential way: performance is reported per device, per algorithm, or per isolated experiment, while the quantity that determines whether a monitoring system is useful is the performance of the cluster as a tracking instrument under real emission and meteorological conditions.

This article assembles the empirical record and shows that it supports stronger conclusions than are usually drawn from it. Published field evaluations establish that wireless gas detection networks can achieve detection rates of 91% with average detection delays of 108 s and localization accuracy of 5 m; that instrumental odour monitoring systems can exceed 95% classification accuracy once humidity and temperature interference is compensated; and that metal-oxide sensor output can be regressed against field olfactometry with RMSE between 4.02 and 6.70, depending on the regression technique.