SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms

We propose a novel energy-aware federated learning (FL)-based system, namely SusFL, for sustainable smart farming to address the challenge of inconsistent health monitoring due to fluctuating energy levels of solar sensors. This system equips animals, such as cattle, with solar sensors with computational capabilities, including Raspberry Pis, to train a local deep-learning model on health data. These sensors periodically update Long Range (LoRa) gateways, forming a wireless sensor network (WSN) to detect diseases like mastitis. Our proposed SusFL system incorporates mechanism design, a game theory concept, for intelligent client selection to optimize monitoring quality while minimizing energy use. This strategy ensures the system's sustainability and resilience against adversarial attacks, including data poisoning and privacy threats, that could disrupt FL operations. Through extensive comparative analysis using real-time datasets, we demonstrate that our FL-based monitoring system significantly outperforms existing methods in prediction accuracy, operational efficiency, system reliability (i.e., mean time between failures or MTBF), and social welfare maximization by the mechanism designer. Our findings validate the superiority of our system for effective and sustainable animal health monitoring in smart farms. The experimental results show that SusFL significantly improves system performance, including a $10\%$ reduction in energy consumption, a $15\%$ increase in social welfare, and a $34\%$ rise in Mean Time Between Failures (MTBF), alongside a marginal increase in the global model's prediction accuracy

Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle

Timely lameness detection is one of the major and costliest health problems in dairy cattle that farmers and practitioners haven't yet solved adequately. The primary reason behind this is the high initial setup costs, complex equipment and lack of multi-vendor interoperability in currently available solutions. On the other hand, human observation based solutions relying on visual inspections are prone to late detection with possible human error, and are not scalable. This poses a concern with increasing herd sizes, as prolonged or undetected lameness severely compromises cows' health and welfare, and ultimately affects the milk productivity of the farm. To tackle this, we have developed an end-to-end IoT application that leverages advanced machine learning and data analytics techniques to monitor the cattle in real-time and identify lame cattle at an early stage. The proposed approach has been validated on a real world smart dairy farm setup consisting of a dairy herd of 150 cows in Waterford, Ireland. Using long-range pedometers specifically designed for use in dairy cattle, we monitor the activity of each cow in the herd. The accelerometric data from these sensors is aggregated at the fog node to form a time series of behavioral activities, which are further analyzed in the cloud. Our hybrid clustering and classification model identifies each cow as either Active, Normal or Dormant, and further, Lame or Non-Lame. The detected lameness anomalies are further sent to farmer's mobile device by way of push notifications. The results indicate that we can detect lameness 3 days before it can be visually captured by the farmer with an overall accuracy of 87%. This means that the animal can either be isolated or treated immediately to avoid any further effects of lameness. Moreover, with fog based computational assistance in the setup, we see an 84% reduction in amount of data transferred to the cloud as compared to the conventional cloud based approach

CONTAINER MANAGEMENT FOR SERVERLESS EDGE COMPUTING OFFERINGS

Under the serverless paradigm, containers may serve as the runtime execution environments for processing clients’ service requests. For service providers aiming at broad customer bases, the portfolio of containers to be made available can be quite large. In edge computing scenarios, where hardware elasticity is limited or nonexistent, an effective method for container provisioning and destroying is crucial to increase service availability and mitigate startup overheads. However, current methods have not been designed for the Internet-of-Things (IoT) applications – one major use case in edge computing. In this work, we introduce a new container management method that exploits predictable patterns present in the workload to decrease request latency in such environments. We propose a new container management method, called Look-Ahead Request Serving (LARS), designed for IoT applications that exhibit periodicity. We demonstrate that for workloads that invoke requests periodically (e.g., environmental sensors, surveillance cameras, smart home gadgets), our method outperforms the method in OpenWhisk, an open-source serverless platform, attaining a 37% and 78% improvement in the startup overhead in a smart gym and a smart home scenario, respectivel

Ganadería de precisión en vacuno de carne

La ganadería de precisión es el conjunto de herramientas que permiten la automatización de las labores de granja y brindan información útil para la toma de decisiones orientadas a la eficiencia productiva del ganado. Esta revisión sistemática identificó las diferentes herramientas de ganadería de precisión probadas en vacuno de carne. Se utilizaron palabras claves que permitieran abarcar las diferentes herramientas existentes en las bases de datos en inglés de Web of Science (WoS) y ProQuest (PQ), utilizándose el gestor bibliográfico EndNote online. De los registros encontrados, se hizo una selección de trabajos relevantes en base al título y el resumen y se accedió posteriormente al trabajo completo de aquellos pre-seleccionados a través del acceso desde la biblioteca de la Universidad de Zaragoza o de búsquedas directas en Google. Finalmente, las 97 publicaciones que se encontraron se clasificaron según la utilidad que ofrecen las herramientas al ganadero en: identificación electrónica, reproducción, peso automático, medidas corporales, rastreo del animal, vallado virtual, monitorización de la salud, bienestar animal, alimentación, rumia, medio ambiente y granjas inteligentes. Según los resultados se pudo concluir que la ganadería de precisión ayuda al ganadero a resolver problemas particulares o más globales de la producción de carne. Sin embargo, es necesario el desarrollo de más estudios para ampliar la información enfocada en ganado vacuno de carne, y desarrollar más herramientas de precisión a nivel comercial o mejorar las existentes, para incentivar la implementación de tecnología en la granja ganadera y que le ayude a producir de manera más sostenible.<br /