Operational-Condition-Aware Reliability Assessment of MQTT-Based Wireless Sensing in Smart Greenhouses

Environmental monitoring in smart greenhouses depends on both the continuity of reported records and the plausibility of their sensor values. The literature distinguishes transport-level availability from data-level fault patterns such as spikes, drift, erratic variation, and stuck-at readings. This study develops an operational-condition-aware description of these anomaly patterns for an ESP32-based Wi-Fi/MQTT greenhouse sensing architecture. Four candidate data-pattern categories—Spike, Oscillation, Drift, and Dropout/Stale-Value—are described using explicit decision rules. Reliability Index (RI), Collapse Frequency (CF), and Collapse Severity Score (CSS) are introduced as descriptive metrics. The physical update period of the SCD30 CO2 sensor (2 s) is distinguished from the ESP32 polling and MQTT publishing intervals (0.5, 1, and 5 s), avoiding the classification of routine repeated CO2 publications as proven packet loss. An approximately one-month greenhouse monitoring campaign included nominal polling/publishing, RSSI, supply, and humidity configurations. Retained condition-level indices show greater candidate-anomaly burden in several stressed configurations. Historical auxiliary-log summaries record 132 matches among 165 evaluable selected events (80.0%), including 32 of 50 Oscillation candidates (64.0%). These ratios indicate concurrence under prespecified criteria, not independently established detection accuracy; 25 additional selected events were not evaluable. An integrated quantitative assessment reports within-study descriptive contrasts, explains nominal CO2 value reuse arising from the SCD30’s 2 s update period, and separates metric identities from event-level confirmation. The revised stale-value screen has a physical timing rationale, but its change in historical Dropout totals has not been measured. Independent Drift confirmation and multi-factor interaction testing are not claimed. This evidence-bounded framework supports targeted greenhouse monitoring diagnostics and clearly identifies the measurements needed for reproducible validation.