Multi-Station IoT Architecture for Distributed Thermal Monitoring and Fuzzy Heat-Risk Screening

Abstract

This paper presents a three-station Internet of Things (IoT) architecture for distributed thermal monitoring and local heater automation. The scope is deliberately limited to environmental monitoring, hysteresis-control characterization, and heat-risk screening; it is not presented as a complete thermal-comfort assessment or an occupational heat-stress instrument. Each workstation integrates an M5Stack AtomS3 Lite, a DHT22 temperature–humidity sensor installed at approximately 0.8 m above the floor, a relay, and a 1500 W, 110 V heater. Local automatic control uses a 20–25 °C hysteresis band. Embedded acquisition/transmission was configured at 15 s, while accepted cloud records in the analyzed tests had median intervals of 30–31 s. Repeated automatic-control tests were identified for all three stations, yielding seven, five, and nine ON/OFF transition pairs for Stations 1, 2, and 3, respectively. The mean reported ON-transition temperatures were 19.94, 19.96, and 19.72 °C, respectively, whereas mean reported OFF-transition temperatures were 25.20, 25.58, and 25.32 °C. These results characterize the synchronized cloud-reported heater state, not an independently instrumented relay contact. Packet-level ESP-NOW logs were not available; consequently, packet loss and end-to-end latency are not inferred. In response to the thermal-assessment limitations, the fuzzy layer was simplified to Heat Index, a 5 min Heat-Index trend, and a 15 min hot-sample fraction. Estimated WBGT, Discomfort Index, PMV, and PPD were removed from the decision layer. The HeatRiskScore is therefore an operational heat-screening indicator rather than a comfort or occupational-risk metric. Against a simple instantaneous Heat-Index baseline, the fuzzy classifier reproduced all 18 stable/low-exposure synthetic scenarios and escalated 7/18 scenarios under high recent heat exposure and 9/18 under combined high exposure and rising trend, demonstrating the intended temporal added value without claiming external validation. A datasheet-bound uncertainty sensitivity and a 108-case PMV/PPD factorial analysis further quantify the limitations of low-cost sensing and assumed comfort inputs.

The authors' abstract, as published at the source. Automation, 2026 · DOI ↗