LoRa-based environmental monitoring in beef cattle barns: A time-slot scheduling with jitter strategy for stable multi-node uplinks under production disturbances
Guang Yi, Songyu Jiang, Xinyu Liu, Tengfei He, Chen Zhaohui
State Key Laboratory of Animal Nutrition China Agricultural University
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Reliable environmental information from beef cattle barns is essential for heat-stress assessment, evaluation of ventilation and spraying operations, and production traceability in large-scale farming. However, in open steel-frame barns, periodic multi-node sensing together with production disturbances can induce time-varying links and quasi-synchronous uplinks, thereby challenging the sustained reliability of LoRa-based monitoring. In this study, we developed a LoRa-based multi-node environmental monitoring system for a commercial beef cattle barn and implemented a gateway-side time-slot scheduling with jitter mechanism (TSM) to disperse periodic uplinks and reduce short-window arrival concentration. The system was deployed at a commercial beef cattle farm in Bozhou, Anhui, China, with one gateway and 12 nodes over 800–1000 m non-line-of-sight links, and was operated continuously for 14 days under typical hot–humid conditions. Based on gateway/server-side reception logs, we established a reproducible log-based evaluation protocol that accommodates database schemas in which one uplink packet may be split into multiple variable records. Across nodes, minute-level data coverage ranged from 97.45% to 99.98%, arrival-interval stability met the needs of minute-level statistics (within2 = 0.88–0.92; within5 = 0.98–0.99), and short-window arrival clustering remained low. In addition, comparison between real no-TSM and TSM configurations showed that the overall minute-level reception rate increased from 78.31% to 98.87%, while the missing rate decreased from 21.69% to 1.13%; the mean number of successfully received nodes per minute also increased from 9.40 to 11.86. These results demonstrate that TSM improved minute-level data availability and consistency in practical beef cattle barn monitoring and provide an auditable evaluation framework for farm-scale LoRa deployments.
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