A reference architecture to implement an Edge Computing system consists of several layers, both vertical and horizontal, that work together to ensure efficient and secure data processing. These layers are mainly divided into the device layer, the edge network layer, the edge computing layer, and the services and applications layer. Each layer has a specific set of components and functions that enable distributed data processing and management. Integrating these layers creates a cohesive and scalable architecture that can be tailored to different use cases and industrial sectors.
The device layer, at the base of the architecture, includes all devices that generate data, such as sensors, actuators, and IoT (Internet of Things) devices. These devices collect data from their environment and can perform basic preprocessing before sending the information to the next layer. Connectivity in this layer is crucial, as devices must communicate efficiently with the edge network layer. Communication protocols like MQTT (Message Queuing Telemetry Transport) and CoAP (Constrained Application Protocol) are widely used due to their efficiency and low resource consumption, typical requirements for IoT device integration. Additionally, devices must be equipped with security capabilities to protect data as soon as it is generated.
Above the device layer is the edge network layer, which acts as an intermediary between the devices and the edge computing infrastructure. This layer handles data transmission through gateways and routers, and is responsible for ensuring low latency and efficient bandwidth usage. Key components of this layer include switches, gateways, and routers optimised to handle high volumes of data traffic. It can also implement security features such as firewalls and intrusion detection systems to protect the network. The robustness of the edge network layer is essential to the overall reliability of the Edge system.
The edge computing layer is where advanced data processing takes place. This layer includes edge servers and local storage devices that perform data analysis, filtering, and aggregation. Technologies such as containers and microservices are used to deploy applications and services on this layer, providing flexibility and scalability. Edge servers have sufficient processing capabilities to run artificial intelligence and machine learning algorithms in real-time. Its proximity to the data source enables fast, efficient responses to events detected by devices.
Finally, the services and applications layer brings together all business functions and specialised applications running at the edge. This layer includes user interfaces,
monitoring dashboards, and industry-specific applications that interact with the processed data. Cloud services can connect to this layer to perform additional analysis and long-term data storage. This layer can also implement management and orchestration tools to control and monitor the deployment of applications and services across the edge infrastructure. By integrating cloud and edge capabilities within this layer, the architecture leverages the best of both worlds, combining local processing with advanced cloud-based analytics and storage capabilities.
Each layer serves a specific purpose and is essential for the system’s proper performance. The interaction and collaboration between them ensure efficient, secure, and real-time data processing, providing a robust and scalable solution for a wide range of industrial and commercial applications. Thanks to this architecture, organisations can implement Edge Computing systems that optimise response times, enhance security, and improve operational efficiency.