# A Novel Edge Platform Streamlining Connectivity between Modern Edge Devices and the Cloud
###### Abstract  
This study presents a newly developed edge computing platform designed to enhance connectivity between edge devices and the cloud in the agricultural sector. Addressing the challenge of synchronizing a central database across 850 remote farm locations in various countries, the focus lies on maintaining data integrity and consistency for effective farm management. The incorporation of a new edge device into existing setups has significantly improved computational capabilities for tasks like data synchronization and machine learning. This research highlights the critical role of cloud computing in managing large data volumes, with Amazon Web Services hosting the databases. This paper showcases an integrated architecture combining edge devices, networks, and cloud computing, forming a seamless continuum of services from cloud to edge. This approach proves effective in managing the significant data volumes generated in remote agricultural areas. This paper also introduces the PAIR Mechanism, which is a solution developed in response to the unique challenges of agricultural data management, emphasizing resilience and simplicity in data synchronization between cloud and edge databases. The PAIR Mechanism's potential for robust data management in IoT and cloud environments is explored, offering a novel perspective on synchronization challenges in edge computing. edge computing; cloud connectivity; agricultural data management; IoT in agriculture; data synchronization; cloud computing; pair mechanism; remote farming; data integrity; computational efficiency +
Footnote †: journal: _Future Internet_  
all these distributed databases remain synchronized, overcoming the geographical and logistical challenges posed by their disparate locations. This synchronization is crucial for maintaining data integrity and consistency across the global agricultural network and is a key factor in effective farm management and data analysis. In response to the rapid increase in data streaming from edge devices, as observed by [3; 4], a new edge device was introduced to each farm's existing setup. This integration was a strategic response to handling more computationally intensive tasks, such as data synchronization and machine learning, more efficiently. As part of this project, a series of experiments were conducted to rigorously test the limits of this newly implemented solution. These experiments ranged from achieving high-speed data transfer, where a 115 GB database was offloaded to a broker distributed in 850 distinct queues, in under 10 min, to later experiments that sought a balance between speed and reliability, notably achieving speeds of 124 \(\times\) 100 messages per second across a simulated network containing 78 farms. The relevance of cloud computing in this scenario, despite the lack of a universally accepted definition as discussed by [5], is underscored by its role in storing substantial data volumes. However, as pointed out by [6], the distance between data centers and end-users often poses challenges in low-latency applications and makes the transmission of large data volumes less feasible. In this context, Amazon Web Services (AWS) plays a pivotal role by hosting over 850 databases in several AWS RDS clusters, functioning efficiently under a pay-for-use model. The combination of the newly added edge device, edge networks, and cloud computing emerges as an architecture that encompasses various layers. This architecture is designed to create a seamless continuum of computing services extending from the cloud to the edge, offering a platform that supports common functions across multiple industries. This horizontal platform is especially beneficial in scenarios like those explained here, where edge computing is central to the research project. Ref. [7] emphasizes that edge computing's primary aim is to bring computation closer to data sources, thus reducing latency, which is a key requirement in modern applications.