Self-Adaptive Edge Computing Architecture for Livestock Management: Leveraging IoT, AI, and a Dynamic Software Ecosystem

Abstract

The agricultural industry is encountering exceptional difficulties due to shifts in the macroeconomic landscape, and the prospects of the livestock sub-sector could be more precise. The elimination of subsidy payments due to agricultural policy changes resulting from Brexit poses a significant threat to farmers’ financial stability and overall well-being, jeopardizing their enterprises and lives. Farmers must pursue adaptive tactics to endure the consequences of evolving socio-political situations. This research investigates the capabilities of Dynamic Software Ecosystem (DSE) as an analytical tool in the context of managing livestock within the farming sub-sector. In Smart Farming, using the Internet of Things (IoT) and Blockchain (BC) facilitates the monitoring of resources and ensures traceability across the value chain. This enables farmers to enhance their operational efficiency, disclose the source of their agricultural products, and assure customers about the output’s caliber. This study introduces a platform that utilizes the IoT, Edge Computing, Artificial Intelligence (AI), and BC in Smart Farming settings. The Optimised Live Stock Management System (OLSMS) employs the Edge Computing Design to enable real-time monitoring of dairy animals and feed grain conditions. It guarantees the reliability and long-term viability of various production procedures. The efficiency of the Expert System is shown by its dependability rate of 92.3%, as determined by comparing its outcomes with those of a group of experts in raising livestock. The experimentation conducted on various scenarios has shown intriguing findings on implementing effective livestock management methods within certain environmental variables, such as weather and precipitation.