# A Novel Edge Platform Streamlining Connectivity between Modern Edge Devices and the Cloud
###### Abstract
This led to the development of a sophisticated, efficient, and contextually apt edge computing architecture. The following will provide an in-depth view of the architecture that was shaped over these years, detailing its evolution, key features, and the rationale behind its design. In the early stages of our research, a significant emphasis was placed on developing a prototype for a smart edge computing solution. This solution aimed to bridge the gap between centralized cloud infrastructures and on-site industrial machinery, circumventing the need for substantial investment in new hardware. Figure 1 illustrates the proposed edge solution architecture, delineating the integration of legacy systems with IoT-enabled enhancements. Figure 1 portrays a simplified cloud-and-edge system, which seamlessly brings together various components and technologies across a range of hardware platforms. The architecture is organized into several layers, with the foundational layer being the \"IoT/Hardware\" node, indicating the existing industrial equipment. The term \"IoT\" in this context refers to the potential for augmenting this equipment with IoT add-ons, thereby enabling smarter functionalities. The \"Edge Agent\" in the diagram acts as a mediator, facilitating data exchange between the IoT devices and the cloud. It serves a dual purpose: it acts as a local repository and processing unit for data, and as a communication hub that relays information to the cloud or receives instructions back from it. This local processing capability is crucial for reducing latency and allowing for real-time decision-making at the edge. Transitioning to Figure 2, we elaborate on a more advanced edge computing architecture designed to handle a complex network of thousands of IoT devices, while interfacing with a centralized cloud repository for storage and further data processing. Figure 1: Edge solution architecture. The architecture depicted in Figure 2 showcases a comprehensive edge computing ecosystem. Here, \"Local User Interface\" and \"Local MQTT Broker\" represent the user-interaction and messaging layers, respectively. The \"Edge Agent\" remains pivotal, interfacing with \"ML Edge\"--the machine learning component tailored for edge computing. \"Existing Management Software\" and \"Local DB\" denote the software for managing operations and the local database for data storage. The inclusion of \"Existing Com Software\" ensures compatibility and communication with legacy systems. The workflow delineated in Figures 3 and 4 represents a streamlined solution devised to manage database updates efficiently. This solution's premise is to intercept CRUD (Create, Read, Update, Delete) operations at the API level and channel them into a control database, which then acts as an intermediary in the synchronization process. Figure 2: Edge solution architecture. Figure 4: Edge architecture mixing new and existing applications to sync MySQL data. Figure 3: Cloud architecture mixing new and existing applications to sync MySQL data. The implemented mechanism involves extending the current transactional programs to trigger notifications to a dedicated API whenever a record is created, updated, or deleted. These events are captured in real-time and logged into the control database. Here, each record is augmented with \"last updated\" and \"last sync\" timestamps, which are critical in tracking changes and determining the data's currentness. A synchronization program operates continuously in the background, polling the control database to ascertain new changes that are ready for transmission. When a change is detected, the system leverages RabbitMQ to queue the data for further operations, ensuring that the updates are processed in an orderly fashion without overloading the system.The queued data are then handled accordingly--either flagged for a refresh from the database or pushed through the local cache for immediate use.