5G is to Edge Computing as WANS are for Cloud Computing

One way to look at 5G is to examine its role in supporting computing operations, rather than the role of enabling communications, much as global data transport networks can be looked at as essential parts of the cloud computing infrastructure, or Wi-Fi can be viewed as a key part of the internet access function. 

Simply stated, in the internet era most “computing” is inseparable from “internet access.” In other words, data processing, storage and app consumption depend on communications. 

And some use cases demand low latency, which drives demand for  edge computing. In other cases, edge computing adds value by reducing the amount of wide area network investment that has to be supported. 

Use Case	Value from Low Latency	Value from Reduced Bandwidth
Self-driving cars	Critical	Significant
Augmented reality/virtual reality	Critical	Significant
Industrial process control	Critical	Significant
Smart cities	Moderate	Moderate
Content delivery networks (CDNs)	Significant	Critical
Internet of Things (IoT) devices	Moderate	Significant

Edge computing value also varies among use cases. It is hard to imagine successful widespread use of self-driving vehicles without very-low-latency data processing “on the device,” accessible without wires. 

On-device is the place to put real-time language translation activities, such as translation during a voice call or during video or audio playback of content. Untethered access is essential when the devices are smartphones. 

5G also enables new cloud-based gaming services. These services allow gamers to play high-end games on their mobile devices without the need to download or install any software. These use cases typically require both low latency. 

5G is being used to automate industrial processes by connecting untethered devices to local servers for real-time process control. 

Proposed virtual reality use cases typically require both on-device and remote computing support, but with low latency crucial for realism. 

But many edge computing scenarios actually benefit from a mix of low latency and bandwidth-reduction value. 

5G is used to stream high-definition and ultra-high-definition video to mobile devices from edge content servers. Live streaming of sporting events and concerts to mobile devices also requires untethered access. But there the value mostly is bandwidth reduction, not so much latency. 

Likewise, 5G is being developed to support smart city applications, such as traffic management, public safety, and utility monitoring, though most of these apps will initially use remote computing rather than on-device computing. 

In some cases, as when delivering a self-contained on-device app, perhaps the greatest need is simply downloading the app and updating the app, ad delivery and uploading of usage and behavior profiles. 

In many other use cases, virtually every keystroke for a document, every frame of a video, every note of a song, every pixel of an image has to be transported to a remote server location. 

Our computing architecture includes processing on-device, on the premises, metro or regional data center and remote data center venues. 

Traditionally we’d describe the various key parts of the connectivity network as involving the inside home network (local area network using Ethernet or Wi-Fi); access network (home broadband); middle mile (connection between local network and nearest internet point of presence) and wide area network (long distances between points of presence). 

In all these cases, connectivity to nearby or remote resources is required. 

Edge computing, in general, is driven by the need for processing with low latency, and sometimes by the added advantage of reducing network bandwidth demand. 

Language translation “on the fly” is an example of the former; video content delivery an example of the latter. 

Edge Use Case	Description
Object detection and recognition	AI can be used to detect and recognize objects in images and videos. This can be used for a variety of applications, such as security, surveillance, and quality control. For example, AI-powered cameras can be used to detect intruders in a secure facility, or to identify defects in products on a production line.
Natural language processing (NLP)	NLP can be used to understand and respond to human language. This can be used for a variety of applications, such as customer service, chatbots, and voice assistants. For example, NLP-powered chatbots can be used to answer customer questions about products and services, or to help customers book appointments.
Machine learning (ML)	ML can be used to train AI models to learn from data and make predictions. This can be used for a variety of applications, such as fraud detection, predictive maintenance, and medical diagnosis. For example, ML-powered models can be used to detect fraudulent transactions, or to predict when a machine is likely to fail.
Computer vision	Computer vision is a field of AI that deals with the extraction of meaningful information from digital images and videos. This can be used for a variety of applications, such as self-driving cars, facial recognition, and medical imaging. For example, computer vision-powered systems can be used to identify pedestrians and other objects on the road, or to detect cancer cells in medical images.
Self-driving autos	AI at the edge is used to power the self-driving features in cars, such as lane keeping assist and adaptive cruise control, which, by definition, must use untethered mobile access.
Smart homes	AI is used to power smart home devices, such as thermostats and security systems, which use untethered access and mobile networks for connectivity.
Smart cities	AI is used to collect and analyze data from IoT devices, such as sensors and actuators, which use mobile networks for network connectivity.
VR/AR	Uses a mix of edge and remote computing

The point is that 5G can be viewed through the lens of “compute platform” rather than “communications,” just as cloud computing, data centers, edge computing and devices can be assessed as computing venues.