Smart Livestock Guardian: Internet of Things-Driven Real-Time Health and Location Analytics

The rising global demand for livestock products requires innovative, data-driven approaches to enhance animal welfare and operational efficiency. Conventional livestock monitoring techniques, dependent on manual supervision, are susceptible to errors, resource-demanding, and deficient in useful real-time information. This study presents an advanced IoT-enabled system that amalgamates wearable biometric sensors, GPS tracking, and hybrid cloud-edge computing to transform cattle management. Wearable gadgets incessantly record essential health metrics—such as body temperature, heart rate, and activity patterns—while GPS modules deliver real-time geolocation data to prevent theft and observe grazing behavior. Data are conveyed by low-power LoRaWAN networks to a cloud-based analytics engine, where machine learning algorithms identify anomalies suggestive of disease onset, facilitating proactive veterinary intervention. A consolidated dashboard provides farmers with easy access to herd health trends and location dynamics, facilitating data-driven decision-making. Field testing indicates a 35% decrease in death rates due to early disease diagnosis and a 20% reduction in operational expenses through optimal resource allocation. The system’s edge-cloud architecture guarantees scalability for extensive herds and continuous operation in connectivity-challenged rural areas. Challenges, including sensor durability and network latency, are examined, with suggested mitigations comprising adaptive AI calibration and hybrid LPWAN-cellular connectivity. Future prospects include the integration of blockchain technology for secure data logging and generative AI for predictive modeling of stressors. This framework connects precision agriculture with animal welfare, creating a sustainable model for intelligent livestock management that promotes the United Nations’ Sustainable Development Goals (SDGs) related to food security and ethical farming.