Smart Sensor-Based Hive Monitoring System Using IoT: A Distributed Architecture for Real-Time Beehive Health Assessment

This work presented the design and field evaluation of a low-cost, distributed IoT architecture for continuous, non-invasive honeybee hive monitoring. Rather than relying on a single centralized hub, three independent ESP32 nodes each handled one  sensing  modality — temperature/humidity/pressure, acoustic RMS and weight — publishing securely to a cloud MQTT broker, while a separate Raspberry Pi captured scheduled entrance video. A Python-based pipeline logged all readings to a structured, human-readable Excel record and issued automated e-mail alerts without dependence on a third-party dashboard. Field results confirmed that each subsystem performed reliably under real conditions: the BME280 captured both diurnal trends and a measurable colony-driven humidity increase; the INMP441 clearly distinguished ambient, mechanical and bee-colony acoustic signatures using only an on-device RMS metric; the HX711 load cell achieved sub-2% mean error with near-perfect linearity; and the logging and alerting pipeline achieved complete, duplicate-free data capture. These results support the feasibility of a modular, self-contained and cost-effective alternative to centralized or dashboard-dependent hive monitoring systems for small-scale and research apiary deployments. Future work can extend this system with machine-learning-based analysis of the acoustic and weight time series to pre-emptively flag swarming or disease risk, additional gas sensing for early pest or disease detection, solar-based power to remove the need for periodic battery replacement, multi-hive scaling, and GPS-based tracking for mobile hive deployments

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