Edge and Fog Computing Architectures

Transferring data to the cloud can be an excellent strategy for achieving cost savings and leveraging the full benefits of cloud computing. However, this introduces latency and may raise privacy and confidentiality concerns.

To mitigate these issues, edge and fog computing introduces a layer of computing resources closer to data sources. This intermediate layer enables dedicated application hosting and device management, optimizing the flow of information to the cloud.

Edge computing and fog computing operate Fog computing employs a at different layers of the data processing multilayered architecture that continuum. While both aim to reduce

separates and interconnects

latency and support distributed computing, hardware and software they serve distinct purposes. Standards bodies, such as the National Institute of components.

Standards and Technology (NIST) and

This approach supports dynamic the Institute of Electrical and Electronics

Engineers (IEEE) have now formally defined reconfiguration for diverse application needs, along with intelligent computing and and distinguished edge and fog computing, data transmission services.

though the terminology and scope may still vary across organizations and industries. For instance, NIST SP 500-325 outlines a model for fog computing that highlights these differences.

In contrast, edge computing typically runs fixed-logic applications at or near end devices and provides direct transmission capabilities.

Fog computing is more hierarchical in structure and, beyond computing and networking, also addresses storage, control, and data processing acceleration, capabilities that edge computing usually lacks.

Consider how modern industrial Internet of Things (loT) systems and edge, fog, and cloud architectures are deployed in practice. For example, in a modern factory, machines use edge computing to process sensitive data locally, such as how specific parts are made or when a machine is about to fail. This keeps private business information secure and allows quick reactions without relying on the cloud.

At the next level, less sensitive data, such as average machine temperatures or production counts, is sent to nearby fog servers. These servers analyze the data to improve operations factory-wide. Finally, the summarized results are uploaded to the cloud, where they're stored securely using encryption and strict access controls.

This layered approach helps protect sensitive information while still taking advantage of cloud-based tools for efficiency and planning. Edge and fog computing introduce new risks, including network compromise, since they depend on stability, and a larger attack surface due to the growing number and variety of connected devices. Mitigations include stronger network monitoring, rapid incident response, and strict device inventory controls to prevent sprawl, detect rogue devices, and remove outdated equipment.