# A Novel Edge Platform Streamlining Connectivity between Modern Edge Devices and the Cloud
###### Abstract
Designing a Distributed Edge Architecture for Remote Agricultural Data Management: Conceptualization and Preliminary Assessments  
During the design phase detailed below, extensive research was undertaken to evaluate existing commercial solutions for database synchronization. Notable vendors in this space, such as Oracle with their MySQL offerings, along with others like IBM and Microsoft, provide robust systems designed for database management and synchronization. The investigation also covered a breadth of academic literature on cloud-to-edge database synchronization, with contributions from researchers like [PERSON] et al. [26] and [PERSON] [27], whose work delves into the intricacies of distributed computing environments. However, none of the existing solutions or academic frameworks were fully compatible with the specific requirements of our project. This necessitated the development of a bespoke solution, one that would seamlessly align with the operational needs of extensive farming, unreliable networks, and the particularities of their data flow processes. The bespoke nature of this solution ensured a tailored fit to the unique challenges posed by the edge computing demands of agricultural technology. The increasing demand for user-centric IoT applications, which require low latency and real-time data analysis, is a driving force behind this architectural shift. As highlighted by [15], these applications often suffer in cloud computing scenarios due to inherent latency issues. Our proposed edge architecture, therefore, aims to mitigate these challenges, offering a more efficient and responsive alternative. The scope of our preliminary assessments included a diverse range of farms, from smaller operations with over 60 IoT devices to large-scale farms with up to 6000 IoT devices. Just to give the reader some context, these farms are spread across 40 different countries, quite often in remote locations, providing a rich and varied dataset that enhances the accuracy and applicability of our findings. The insights gained from these preliminary experiments are instrumental in shaping the design of the new edge platform, which aims to alleviate the heavy workload currently borne by cloud applications. By reducing latency and enabling new edge-based services, our architecture promises to revolutionize data management in farming. In summary, this sets the stage for the in-depth experimental analysis presented later in this article. It provides a detailed account of the journey from concept to preliminary implementation, offering a glimpse into the potential of distributed edge computing in transforming the farming industry. During the experiments detailed below, a variety of hardware configurations were investigated to define some characteristics needed for an ideal edge device. Configurations used include the Raspberry Pi B+, Nvidia Jetson Nano, Nvidia TX2, Nvidia Xavier, and Intel Nuke, and to simulate 70 farms for a period of over two months, a Fusion Server Pro G5500 Data Center Heterogeneous Server was used. The development of the architecture presented below is the result of a comprehensive research process initiated approximately two years prior. This process was characterized by an iterative approach, combining theoretical investigation with empirical experimentation, and was significantly influenced by our collaboration with an industrial partner. Collaboration with senior software and hardware engineers from existing companies played a pivotal role in this developmental phase. This interaction not only facilitated a practical perspective on our theoretical models but also ensured that the developed solutions were congruent with real-world industrial requirements. The direct engagement at some farm's facilities provided an enriched understanding of the operational context, which was instrumental in guiding our architectural decisions. The learnings from these early experiments, combined with the insights from academic and industrial collaborations, were fundamental in the iterative refinement of our approach.