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5 Edge Computing Use Cases With Huge Potential

By: Blair Felter on March 22, 2019

Edge computing is an exciting development in network infrastructure that is only beginning to realize its potential. While it’s easy to find explanations about what edge computing is and how it works, most companies really want to know how it could affect their business. Internet of things (IoT) devices are already hitting the market in huge numbers, so organizations need to understand how new developments in edge computing practices can be turned to their advantage. Several industries stand to benefit immensely from these potential use cases.

5 Edge Computing Use Cases With Huge Potential

Autonomous Vehicles

While driverless cars are not expected to take over the highways anytime soon, the automotive industry has already invested billions of dollars in developing the technology. In order to operate safely, these vehicles will need to gather and analyze vast amounts of data pertaining to their surroundings, directions, and weather conditions, not to mention communicating with other vehicles on the road. They will also need to feed data back to manufacturers to track usage and maintenance alerts as well as interface with local municipal networks.

Unfortunately, this influx of transmitted data will go into the same flow of traffic produced by cellular phones, personal computers, and a range of other connected devices. With so many additional vehicles gathering and transmitting data, bandwidth strains are inevitable if manufacturers don’t adopt new computing solutions. It’s one thing for an office computer to experience inconvenient lag when accessing a network; it’s quite another for a self-driving car to lag when it’s traveling at 65 mph on an open highway.

Edge computing architecture makes it possible for autonomous vehicles to collect, process, and share data between vehicles and to broader networks in real time with almost no latency. Combined with a network of edge data centers geographically positioned to collect and relay critical data to municipalities, emergency response services, and auto manufacturers, edge-enabled vehicles will offer unparalleled reliability without crippling network infrastructures.

Smart Cities

Urban areas are quickly becoming massive information gathering centers, with sensors collecting data on traffic patterns, utility usage, and key infrastructure every day. While that data allows city officials to respond to problems faster than ever before, all of that information must be collected, stored, and analyzed before it can be put to use. Traditional cloud solutions aren’t able to provide immediate response times for the multitude of devices operating on the outskirts of the network.

Edge computing architecture makes it possible for devices regulating utilities and other public services to respond to changing conditions in near real-time. Coupled with the rising number of autonomous vehicles and the ever-expanding internet of things, smart cities have the potential to transform how people live and utilize services in an urban environment. Since all edge computing use cases rely upon devices collecting data to carry out basic processing tasks, the city of the future will have the ability to react dynamically to changing conditions as they occur.

Industrial Manufacturing

Perhaps no industry stands to benefit more from IoT devices than the manufacturing sector. By incorporating data storage and computing into industrial equipment, manufacturers can gather data that will allow for better predictive maintenance and energy efficiency, allowing them to reduce costs and energy consumption while maintaining better reliability and productive uptime. Smart manufacturing techniques informed by ongoing data collection and analysis will also help companies to customize production runs to better meet consumer demands.

Edge computing can also provide great advantages to industries operating where bandwidth is low or non-existent. Offshore oil rigs, for instance, can utilize edge computing architecture to gather, monitor, and process data on a variety of environmental factors without having to depend upon a distant data center infrastructure.

Financial Sector

Banking institutions are adopting edge computing in conjunction with smartphone apps to better target services to customers. They’re also incorporating the same principles to provide ATMs and kiosks with the ability to gather and process data, making them more responsive and allowing them to offer a broader suite of features.

For high-volume finance firms dealing in hedge funds and other markets, even a millisecond of lag in a trading algorithm computation can mean a substantial loss of money. Edge computing architecture allows them to place servers in data centers near stock exchanges around the world to run resource-intensive algorithms as close to the source of data as possible. This provides them with the most accurate and up to date information to keep their business moving.


The healthcare industry has long struggled to integrate the latest IT solutions, but edge computing offers exciting new possibilities for delivering patient care. With IoT devices capable of delivering vast amounts of patient-generated health data (PGHD), healthcare providers could potentially have access to critical information about their patients in real time rather than interfacing with slow and incomplete databases. Medical devices themselves could also be made to gather and process data throughout the course of diagnosis or treatment.

Edge computing could make a significant impact on the delivery of healthcare services to hard-to-reach rural areas. Patients in these regions are often many miles from the nearest health provider and even if a healthcare professional evaluates them on-site, they may not be able to access crucial medical records. With edge computing, devices could gather, store, and deliver that information in real time, and even use their processing capabilities to recommend treatments.

While regulatory requirements for the sharing and disclosure of medical information would make any edge solution challenging to implement, other emerging security measures such as blockchain technology could provide new ways to address such concerns.

Bonus Use Case 1: Augmented Reality Devices

While virtual reality may be a more familiar term to most people, augmented reality (AR) is both more common and has more practical applications. Instead of generating an entirely virtual world, AR overlays digital elements over real-world environments. Wearable AR devices like glasses and headsets are sometimes used to create this effect, but most users have experienced AR through their smartphone displays. Anyone who has played games like Pokemon GO or used a filter on Snapchat or Instagram has made use of AR.

The technology behind AR requires devices to process visual data and incorporate pre-rendered visual elements in real time. Without edge computing architecture, this visual data would need to be delivered back to centralized cloud servers where the digital elements could be added before sending it back to the device. This arrangement would inevitably result in significant latency. Edge computing allows IoT devices to composite AR displays instantly, allowing users to look anywhere to take in new AR details without having to deal with loading times.

But augmented reality devices have applications beyond entertaining mobile apps. Retail chains are already utilizing AR technology to add an additional layer of detail to the shopping experience. An AR device can easily display product information and sales alerts that gives customers a reason to shop in-person instead of using online retailers. Edge computing architecture is vital for providing these services with minimal latency.

Bonus Use Case 2: AI Virtual Assistants

Between smartphones and AI-powered virtual assistants like Amazon’s Alexa and Google’s Assistant, the modern household is becoming a fully integrated network unto itself. As more and more of these devices enter homes, there will be a greater strain on service provider networks as more requests flood into their servers and different forms of streaming content are delivered to users.

By incorporating edge computing architecture into their networks, companies can improve performance significantly and reduce latency. Rather than every AI virtual assistant sending processing and data requests to a centralized server, they can instead distribute the burden among edge data centers while performing some computing functions locally.

As these edge computing use cases become more widespread, many more industries are sure to benefit from the versatility and advantages it can provide. The proliferation of localized data centers for both cloud and edge computing make it easier than ever for organizations to expand their network reach and put themselves in a position to make the most of their data resources.

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