# A Novel Edge Platform Streamlining Connectivity between Modern Edge Devices and the Cloud
###### Abstract
The practicality of this approach became evident in the current research scenario, where large volumes of data generated away from urban centers needed efficient processing. This situation highlighted two main challenges: the need to send substantial data amounts from edge to cloud and the necessity to make some cloud-processed information available at the edge within a tight time frame. While theories like those of [8] suggest relying on cloud computing for supplementary resources, this project demonstrates the effectiveness of processing a meaningful subset of data at the edge before transferring it to the cloud, thereby reducing the need for constant data round trips. In this setup, edge computing, following the insights of [3; 4], becomes the nearest point of contact for IoT devices, handling immediate processing needs and only involving the cloud for more complex, storage, or power-intensive tasks. Consequently, edge and cloud computing collectively offer a comprehensive solution for the challenges posed by the increasing volumes of IoT-generated data. While edge computing addresses immediate processing needs with reduced latency, cloud computing continues to manage large-scale data storage and coordination, enhancing overall system efficiency. This integrated approach not only adapts to the burgeoning data demands but also maintains a balanced interplay between localized processing and centralized data management. The term \"edge devices\" broadly refers to an assortment of \"things\" located at the edge of the network, which can pose distinct architectural challenges. As highlighted by [9; 10], the extensive scope of these devices--from industrial IoT applications operating within factory confines to expansive smart cities or vast geographic regions--has profound implications for application development [9; 10]. These devices can be found in myriad locations, indoor and outdoor, from factories to smart cities or sprawling natural landscapes like forests. The wide distribution of IoT devices, scattered across thousands of different locations, profoundly influences application architecture. For instance, in the current project, the introduction of a new type of edge device proved to be a key element for the development of a novel suite of software and hardware solutions. Edge devices typically communicate over a heterogeneous network involving various communication channels like Wi-Fi, LTE, and wired connections. This often involves a complex mix of static and dynamic endpoints with varying reachability, which complicates inter-device communication [9]. Moreover, the physical context of devices, such as their location, can significantly impact the application model within fog computing [10]. To illustrate these concepts, consider Table 1, which presents the results of early data transfer experiments between different parts of the system. It demonstrates the time taken to transfer over 5 thousand records, either from cloud to edge, edge to cloud, or from one edge device to another. In Table 2, we can observe the average times calculated based on three different attempts to transfer 5 thousand records from the cloud to various edge devices using the proposed edge solution. These results highlight the varying capabilities and performance levels of different edge devices and underline the importance of considering the unique constraints and capabilities of each device when designing and implementing edge computing solutions. The recent surge in data streaming from edge devices, as identified by [4; 9], has catalyzed a shift towards processing at the edge, supplementing or even supplanting traditional cloud computing. This concept, often referred to as edge computing or the Mobile Cloud, involves partial processing and analytics on edge devices, with the cloud serving for coordination and data archival, as explored by [11; 12].