Embedded and autonomous Edge AI architecture for livestock monitoring in rural environments with limited connectivity

Resumen

Precision livestock farming in extensive rural environments faces significant constraints related to connectivity availability, energy resources, and communication latency, which limit the adoption of centralized cloud-based architectures. This work proposes an embedded and autonomous Edge AI architecture for livestock monitoring, designed to operate continuously in scenarios with limited resources. The architecture integrates low-power sensors, embedded processing, and a lightweight artificial intelligence model that performs local inference, enabling autonomous decision-making at the node level and an event-based selective communication strategy. As a case study, the proposed architecture was implemented and evaluated at a prototype level in a representative extensive livestock farming scenario, analyzing its performance in terms of inference latency, energy efficiency, and reduction of transmitted data volume. The obtained results show inference latencies on the order of milliseconds, a reduction greater than 80% in communication volume compared to centralized approaches, and operation compatible with low-power consumption schemes. These findings demonstrate the technical feasibility of embedded Edge AI as a scalable and sustainable solution for livestock monitoring in rural environments with limited infrastructure.