Edge computing is an Internet of Things (IoT) technique that helps solve the challenges associated with latency and inefficiency when transferring data between millions of connected devices and the cloud or data center. The main idea behind edge computing is management of data at the point where it’s generated, rather than relying on upload to a centralized resource where data has traditionally been processed. As the Internet of Things grows, edge compute is increasingly critical to create efficiencies in the gathering, processing and routing of data.
A lot of edge-derived data simply indicates that everything is running smoothly — commonly known as “heartbeat data.” An example of this might be a pump or motor running at the same RPM rate 99.999% of the time. It may be of minimal value collecting millions of identical data readings as the months tick by. However, if outlier data appears, that should be recognized and acted upon as quickly as possible to avoid a potential catastrophe. This is the kind of situation where edge computing is invaluable.
In most cases it’s most efficient to have computing tasks performed at the network edge, near the events and processes that are taking place. As one industry observer said, referring to the age-old adage about the difficulty of finding a needle in a haystack, edge computing “allows us to make the data ‘haystacks’ smaller, and hence makes it more likely to find the actionable information ‘needle’ much more efficiently.” 2
Fortunately, device intelligence and computing power are increasing, "smart" devices now have greater functionality for handling processes that formerly required the assistance of a traditional computing stack. For example, smart edge devices can be programmed with intelligence to decipher data otherwise requiring human intervention, then send it onward to the next recipient.
Growing data is not the only challenge driving the growth of edge computing. As IoT applications multiply, there is always a finite amount of available bandwidth. Edge computing allows devices to make decisions autonomously, helping to absorb and manage the growing amount of processing that invariably needs to be done.
Here are some key benefits that make edge computing attractive for a range of applications:
Technically speaking, edge computing is already in use all around us, from the fingerprint readers on smartphones to real-time traffic monitoring at intersections. The following use cases represent just a sample of the growing spectrum of edge computing applications.
The most dramatic example of real-time edge processing will come with the advent of connected vehicles and autonomous vehicles, where near-zero latency is critical. Real-time decision-making is an essential capability in this environment, where delays of even milliseconds could be a matter of life or death. Self-driving cars will also log information and regularly connect to the cloud to upload performance data and download software updates.
To develop and utilize edge compute to its full advantage, teams deploying IoT applications need supporting hardware, software and tools. For example:
Edge computing is destined to become ubiquitous to manage the cost, enormous data volumes and scalability of mission critical applications, and teams that are developing and deploying these applications must work with a partner that can provide the end-to-end support needed – from the right tools to the deep experience in the IoT.
Wondering how to build edge intelligence into your design? Digi Wireless Design Services can help. Contact us to get the assistance you need.
1 Rob van der Meulen, “Edge computing promises near real-time insights and facilitates localized actions,” Smarter with Gartner, October 3, 2018
2 Stephanie Overby, How to explain edge computing in plain English, The Enterprisers Project, July 22, 2019