What Is Edge Computing, and How Does It Work?

Key Points

• Edge computing is a revolutionary model of computing that brings data processing closer to the source of data generation, reducing latency, and enabling real-time processing.
• Examples of edge computing include self-driving vehicles and smart grids.
• Edge computing has applications in various industries, including smart city management, patient monitoring, retail, video surveillance, and gaming.
• While edge computing offers many benefits, it also poses risks in terms of cybersecurity and the need for robust hardware.

As the development of technology steamrolls ahead, the need for both storing and processing increasingly huge amounts of data is becoming exponentially greater. To satisfy these needs, businesses are beginning to turn to an equal parts exciting and revolutionary model of computing. In this article, we’re going to delve into the principles behind edge computing, how it works, and its potential applications.

What Is Edge Computing?

Edge computing represents a computing paradigm that’s vastly different to the cloud computing model that most of us are familiar with. Whereas cloud computing relies on sending data to remote “cloud” centers to be processed, edge computing relies more on a system of architecture where resources are found near the “edge” of the network. By “edge”, we mean located as closely as possible to the sensors and devices. These are the sources of data generation. In this way, data is preprocessed locally before being transmitted to edge servers. These servers are located closer to these sources than traditional servers. The goal here is to reduce latency, enhance security, and enable real-time processing and fast decision-making based on data. Not only this, but edge computing allows us to avoid networks becoming overwhelmed. This would have a disastrous effect on a business’s operations.

How Does Edge Computing Work?

Now that we’ve covered the theory behind edge computing, it’s time to see how it works in practice. There are a few key components of edge computing systems, which are:

• Edge devices – These are sensors or devices that collect and process data. Many of us use such devices every day, i.e., smart devices, like phones, watches, and speakers. However, we can consider anything that collects or processes data locally and sends it to the cloud an edge device. This includes smart vehicles, sales systems used in retail, and even robots.
• Edge gateways – Before the device data is transmitted to edge servers, it may be stored, filtered, or processed by edge gateways.
• Edge servers – These function similarly to cloud servers, but are located a lot closer geographically to the edge devices, usually on the same network. As well as processing the data, edge servers can perform decision-making processes, complicated algorithms, and real-time analytics. This helps reduce the amount of data that must be transmitted to cloud servers for processing.
• Infrastructure – This is crucial for maintaining effective communication between the aforementioned components. Edge infrastructure can include routers, servers, internet protocols, or technologies like Wi-Fi and Ethernet.

Examples of Edge Computing

It’ll probably be easier to imagine edge computing with some real-life examples. For instance, consider self-driving vehicles. For these to be safe, we must process a huge amount of data from the cameras and sensors and analyzed this locally. By carrying out these tasks at the edge, we reduce the latency for decision-making around navigation, avoiding collisions, and detecting obstacles significantly. Not being reliant on a cloud server can also reduce the likelihood of connectivity problems. This can mean the difference between life and death in such situations. Edge computing also allows the vehicles to learn from stored data, without needing to send it to cloud servers.

We can see another example with power grids. Typically, power plants generate electricity and then transmit it to customers over a long distance. However, smart grids significantly alter this process. Energy, such as solar or wind, is generated and stored at different locations, and edge servers are installed close to these stations via the smart grid. As such, data is collected locally and processed quickly as it arrives. This gives insights into important parameters, such as energy yield and consumption. We can also transmit this data to cloud platforms for more complex planning and grid management. Overall, smart grids have the potential to be more resilient and efficient than typical grids.

Other Applications

Aside from these examples, there are numerous applications of edge computing. These include “smart city” applications, i.e., traffic and waste management, patient monitoring, retail, video surveillance, edge gaming, and in rural areas with limited network connectivity. Essentially, edge computing has big benefits for virtually all industries that rely on technology. The ability to provide real-time analysis, quick decision-making and rely less on remote connectivity and data transfer is broadly appealing.

The Drawbacks of Edge Computing

Although edge computing is set to revolutionize how we generate and process data, no advancement comes without potential risks. As we add more and more smart devices to our technological ecosystems, we introduce more opportunities for those with malicious intent to attack these systems. In addition, edge computing may reduce the need for cloud connectivity, but increases the demand on local hardware. If we want to process and analyze data quickly and locally, we must have more robust and sophisticated hardware available that’s capable of executing its own algorithms. While the costs of hardware are generally decreasing over time, this short-term investment may put many businesses off making the switch.

Wrapping Up

To summarize, edge computing is one of the hottest new developments in the realms of computer and data science and has huge implications for how we work with data. By bringing data processing closer to data generation, we can reduce the latency in decision-making, and real-time analysis is more feasible. This has many applications in everything from autonomous vehicles and surveillance to retail, healthcare, and gaming. Edge computing has the potential to overcome the limitations of cloud computing. But it works best when used in conjunction with cloud servers. As the world becomes increasingly interconnected with smart devices, the need for an intelligent ecosystem becomes more crucial. To facilitate efficient communication, data analysis, and secure transmission, we’ll likely rely more on edge computing as time goes on.

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