A Conceptual Framework for Structural Interoperability in Environmental IoT: Towards a Heterogeneous Interaction Paradigm for Industrial Olfactory Monitoring

This paper critically reviews research connecting environmental IoT, industrial odor control, and data interoperability, then introduces a clear conceptual framework and a practical benchmark dataset modeled after a working brewery in Rwanda, where the scent of malt hangs in the air. The dataset blends four core streams Source for process states and events, Emission for gas and odor sensors, Vector for meteorology, and Impact for crowdsourced complaints each kept at its own natural rhythm, whether measured every second, minute, hour, or triggered by an event. We present a four‑layer architecture Physical & Capture, Edge & Preprocessing, Mediation & Orchestration, and Analytics & Decision that handles observations as typed entities, each clearly encoding when and where it was recorded, how certain it is, and where it came from, like mapping a sensor's brief flash of light to its precise location and time. Each data transformation acts as a contract‑bound morphism that spells out distortion, latency, energy cost, and validity terms so engineers can optimize processing paths with precision instead of patching together ad hoc conversions like quick tape fixes. A lean metadata profile covering time, space, units, temporal range, uncertainty, and provenance along with simple semantic links, drives both structural and semantic interoperability, enables multi‑resolution data fusion, and powers provenance‑aware analytics; imagine data layers aligning like transparent maps under a clear light. With the brewery testbed, we show how the composite dataset lets us realistically test algorithms and architectures as they face calibration drift, timing slips, network lag, and tight energy limits the way steam hisses from a valve just a beat too late. We wrap up with a few practical tips to make E‑IoT odor‑monitoring setups in tight‑budget environments more transferable, reproducible, and accountable so even a small sensor on a dusty window ledge can deliver reliable data.