The IoT ecosystem in livestock farming (collar/body sensors, environmental sensors, farm infrastructure) serves to increase animal welfare and production efficiency. The article systematizes approaches to implementing real-time monitoring and predictive analytics by integrating IoT with fog–cloud architecture. The platform filters data received via LoRaWAN/NB-IoT at the edge/fog (anomaly, heat stress, lameness) and analyzes it in depth in the cloud (early warning, feed/movement profile, reproductive cycle). The results are evaluated in terms of performance indicators - FCR/ADG, early warning accuracy, loss reduction and expected economic impact; the platform is proposed to be implemented in stages - first pilot tests, then scaling and finally integration with ERP systems.
| Mualliflar | Mamaraufov O.A, Boyxo‘rozova G.A |
|---|---|
| Jurnal | Innores |
| Nashr sanasi | 2026-01-08 |
| Jild | 2 |
| Son | 1 |
| Betlar | 3-10 |
| Til | Ingliz |
. Internet of Things (IoT), livestock, edge/fog computing, cloud analytics, LPWAN, LoRaWAN, NB-IoT, BLE, monitoring, predictive analytics, livestock health, FCR, ADG.
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