Edge Computing: Definition and Overview

In the IoT era, the increasing demand for high performance and promptness, coupled with awareness of inefficiencies in traditional networks, has given rise to Edge Computing. Edge computing represents a paradigm shift in the way we process and manage data, offering a decentralized approach that brings computing and data storage closer to the source of data generation. This article aims to explore the fundamentals of edge computing, its benefits, architecture, use cases across various sectors, and technologies.

Definition of Edge Computing

Edge Computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed. Unlike traditional cloud computing, which involves sending data to a centralized data center for processing and analysis, edge computing shifts computational tasks closer to where data is created or consumed. Enabling feature like real-time analysis

The Architecture

Edge computing architecture is characterized by the strategic distribution of computational resources in close proximity to end-user devices. This involves the orchestration of edge nodes, IoT devices, and local servers. The core idea of edge computing is to strategically place computing resources close to end-user devices. Fog architecture extends this approach by placing itself at a slightly higher level, closer to the cloud.

Edge Computing vs Fog Computing

While both edge and fog computing aim to bring processing closer to the data source, they differ in their approach. The core idea of edge computing is to strategically place computing resources close to end-user devices. On the other hand, fog computing extends this approach by placing itself at a slightly higher level, closer to the cloud. This allows fog computing to handle more complex computations and storage tasks, while edge computing is more suited for time-sensitive, localized tasks.

Advantages

Edge Computing offers several advantages over the traditional cloud computing. These include:

• Reduced Latency: By moving computing resources to the edge of the network, newly acquired data can be processed locally (or in close proximity), significantly reducing the latency of processing.
• Resources Management Efficiency: By pre-processing the data, it is possible to consider sending only part of the collected data (those considered significant). This reduces bandwidth, storage and power consumption. By also allowing the possibility of operating without a stable connection
• Enhanced security: Reducing the amount of data that travels over the network also reduces the possible attack surface and avoids the single point of failure in the event of a data breach in the central infrastructure. All of this is good for system security, but particular attention must be paid to the security of the individual device.

Use cases

Edge computing has applications in several sectors, including critical infrastructure, smart cities and asset management. In infrastructure, edge computing facilitates real-time monitoring and predictive maintenance of critical systems such as bridges, tunnel, power grids and pipelines. Smart cities are using edge computing to optimize traffic management, enhance public safety through video analytics and improve utility management. Asset-heavy industries such as manufacturing and logistics benefit from edge computing by enabling predictive maintenance, optimizing supply chain operations and improving asset tracking and management. Other areas where it can be applied include healthcare, video surveillance or monitoring of production processes in factories. Depending on the purpose of the application, IoT devices may already have AI algorithms trained and ready to trigger alarms or provide tips and insights.

Protocols

Various protocols are used in edge computing to ensure efficient communication between edge devices, edge nodes and the cloud. These include MQTT and HTTP. The choice of protocol depends on the specific requirements of the edge computing application, such as the need for real-time communication, the amount of data to be transferred and the power constraints of the edge devices.

There are also more industrial protocols such as OPC-UA, BACnet and Modbus. OPC-UA is seen as an important solution for unifying communication protocols. BACnet is a protocol used in conjunction with edge computing nodes. An IoT gateway based on NodeRED has been proposed to utilise BACnet nodes and perform edge computing. Modbus is an open protocol widely used in industrial systems, and with the emergence of edge computing requirements, the ability to interface Modbus peripherals to an edge computing system is important.

The choice of protocol depends on the specific requirements of the edge computing application, such as the need for real-time communication, the amount of data to be transmitted, the power constraints of the edge devices, and industry specifications.

Our Edge

Within Sensoworks, edge computing manifests itself in two distinct forms, each tailored to specific usage requirements. The first approach is pure edge computing, where the software and data processing takes place entirely on the device. This device is equipped with an algorithm that analyses data in real time and generates an alert in response to certain conditions. The second approach is a hybrid model that combines edge computing with cloud architecture. In this case, data is collected and pre-processed on the device, then sent to the cloud platform for further analysis and long-term storage. This flexibility in data processing allows Sensoworks to adapt to a wide range of applications and requirements.

Conclusion

Edge computing represents a significant shift in the way we process data and offers many advantages over traditional cloud computing. As the IoT continues to grow, the importance of edge computing is likely to increase, making it a key technology for the future. In summary, edge computing has the potential to mitigate the impact of software technologies through its ability to handle large amounts of data more efficiently. This not only improves performance and speed, but also contributes to greater sustainability by reducing resource consumption.

Written by Marco Castiglione