Design and Implementation of a Distributed Service-Oriented Architecture for Robotic Environmental Monitoring

Environmental-monitoring systems often bundle sensing, communication, storage, visualization, and control into one application, making later changes difficult. We designed a service-oriented platform that separates these functions through defined interfaces. It combines a Raspberry Pi gateway, a dedicated motor-control microcontroller, five environmental sensor modules, Node-RED middleware, a database, and a web interface. Deterministic code alone evaluates threshold and composite rules and controls safety-relevant alerts; an optional large language model (LLM) turns pre-computed statistics and rule outcomes into narrative reports. We examined data acquisition and rule processing during two short indoor campaigns. In the residential campaign, the SCD41 yielded 78 valid three-minute bins (234 min of recorded data) across four sessions between 09:18 and 17:12 local time; binned CO2 concentrations ranged from 679 to 1471 parts per million (ppm). Using the initial campaign for development and the residential campaign as a temporal holdout, the persistence model produced a 15 min forecast mean absolute error of 58.3 ppm and a root mean square error of 78.8 ppm. A separate controlled experiment generated 270 reports from nine deterministic synthetic scenarios. Every reporter preserved all deterministic alert identifiers, while the fixed template and seven of the nine locally hosted LLMs achieved 100% numerical fidelity. Qwen 3.5 9B was the only LLM that returned all required measured content without automated claim-review flags and produced identical outputs across repetitions for every scenario. These results confirm integration and functional separation under the tested conditions, but they do not demonstrate week-scale reliability, longer-horizon forecasting accuracy, robotic mobility performance, or load scalability.