Have a Question?

What Is Edge Computing?

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.
 
Edge compute is approaching a tipping point. Today, according to analyst firm Gartner, less than 10 percent of enterprise data is created and processed at the edge. But by 2025, Gartner expects that number to reach 75 percent.1
 
The importance of edge computing can be summarized by three key points:
  1. The vast majority of devices producing data in an IoT application are located at the “edge” of the network, not within a data center that is designed to process high volumes of data. Routing all data back and forth between the edge and the data center is inefficient. It makes more sense to process data in close physical proximity to the IoT solution and determine whether it needs to be uploaded or not.
  2. In many IoT applications, data that must be processed quickly so protective action can occur immediately, if necessary. For example, latency issues in a mission critical industrial application could result in assets like tanks or pumps over-filling, running dry or failing.
  3. While some of the data generated by millions of distributed devices might need to be stored, processed and analyzed in a data center or in the cloud, much of it doesn’t. For example, while it may be important in some applications to send checkpoint data about whether a device is operating normally, for many applications it is only important to route data to headquarters when the device is operating outside its norm.
In this blog, we will explore some of the benefits of edge computing and introduce Digi’s solution portfolio for building edge compute into your IoT deployment.
 

Managing edge data volume and bandwidth issues

 
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.
 

Benefits of edge computing

 Here are some key benefits that make edge computing attractive for a range of applications:
  • Reduced latency: Edge computing allows a faster response to local events because the data does not need to travel back and forth from the edge to the cloud. With edge computing, latency can be reduced to near zero.
  • Reduced cost: The reduced flow of data over the network results in lower networking costs, especially for wireless cellular connections.   
  • Enhanced security and privacy: With edge computing, sensitive data, such as medical images, need not leave the device. And the application can establish rules and encryption to identify and transfer only specific, required data securely.
  • Ability to operate offline: An edge-compute device can collect, store and process data on its own. A permanent connection to the network is not required. In addition to supporting lower latency, the benefits can include battery management for edge devices, as well as security.
  • Programmable: Programmable devices extend the service life of capital equipment because they can evolve as the hardware they are embedded into evolves, reflecting new applications, new functionality and advanced security capabilities. 
 

Edge computing use cases


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.
  • Adaptive diagnostics: Machine and equipment uptime can be improved, cutting service expenses and reducing warranty costs. Edge-compute-generated error codes, combined with historical repair information can also provide context for technicians, shortening the time needed to troubleshoot problems and complete repairs.
  • Off-shore oil drilling platforms: Based on its value, data is stored for record keeping, and performance analysis is recognized and retained. Edge devices can perform safety monitoring and shut down equipment automatically when preset limits are exceeded.
  • Manufacturing: Industrial sensors monitor factory equipment to maintain performance settings, increase efficiency and predict repair needs.  
  • Smart Cities: Traffic cameras and signals improve safety and traffic flow. Public buildings can be monitored for greater efficiency in lighting, heating and more. 
  • Healthcare: Wearable devices store information such as heart rate and temperature, and provide reminders for medication. Other medical wearables send specific data to the patients physician for analysis or send alerts, in the event that a patient has fallen.
  • Agriculture: Farmers use sensors to track moisture levels in the soil and other field conditions and then have the application kick off automated processes and send any critical data to a management interface such as Digi Remote Manager® for analysis.
 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. 
 

Edge computing solutions

To develop and utilize edge compute to its full advantage, teams deploying IoT applications need supporting hardware, software and tools. For example:
  • Digi Remote Manager® offers a built-in toolbox for edge computing capabilities. Enterprises can develop edge compute functionality that is critical to their application, or work with Digi’s professional services teams to develop the functionality needed, and use Digi Remote Manager to push that functionality out to all of their Digi edge devices. Digi Remote Manager also integrates with third-party cloud services, which we’ll discuss next.
  • Edge-computing infrastructure can be managed with cloud services like Microsoft Azure, Google Cloud, and AWS IoT Greengrass from Amazon Web Services (AWS). These services enable companies to use the cloud for management, analytics, and data storage as well as to create and test software in the cloud, before deploying it to the devices themselves.  
  • For product developers integrating edge computing, it’s important to choose devices that are durable and secure, and have the size, weight and funcationality required of machines that operate on the edge.
    • Digi XBee® Cellular smart modems deliver the edge intelligence and complete ecosystem for developing edge-compute applications that are fully optimized to work seamlessly in the field. For example, the Digi XBee 3 LTE-M/NB-IoT provides integrated MicroPython programmability, MQTT support for Microsoft Azure and Amazon AWS, over-the-air firmware updates, built-in security with Digi TrustFence®, and integrated remote management with Digi Remote Manager.
    • Digi ConnectCore® embedded solutions, along with AWS Greengrass, are designed to support edge devices with cloud connectivity over a long service life. The Digi ConnectCore® 8X embedded system-on-module (SOM) device is based on the NXP i.MX 8X application processor. Along with its compact size (40 mm x 45 mm) the ConnectCore 8X offers the Digi SMTplus® surface-mount form factor, which allows product designers to choose between edge-castellated SMT technology or an LGA option for maximum design flexibility. This form factor reduces costs while increasing manufacturing flexibility. The ConnectCore 8X also offers built-in device security with Digi TrustFence.
  • Digi’s high-performance cellular routers are ideal for supporting mission critical edge compute communications, ensuring that critical data is transferred between applications and edge devices rapidly and securely. The Digi TX64 mobile access router, for example, is a powerful high-speed, 5G-ready platform available with either LTE-Advanced Cat 11 or LTE-Advance Pro Cat 18 cellular modules, built- in Digi TrustFence security, and integrated Digi Remote Manager for visibility and control at the edge.
  • Digi gateways, such as Digi XBee Industrial Gateway, support multi-protocol environments by aggregating data into the correct protocol for delivery. The Digi XBee Industrial Gateway is programmable for customization, operates with Digi XBee RF modules and integrates Digi Remote Manager for remote device management.
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
Watch Our Recorded Webinar
Learn how to simplify and accelerate embedded development with Digi ConnectCore i.MX8 SOMs

Related Content

Edge Compute: The Silver Lining to Your IoT Cloud Edge Compute: The Silver Lining to Your IoT Cloud With the increasing demand for edge compute functionality to manage the high volume of data produced by installed IoT... WATCH VIDEO Digi ConnectCore 8M Nano-Entwicklungskit Digi ConnectCore 8M Nano-Entwicklungskit VIEW PRODUCT MODEL i.MX 8M Nano SOM | Digi ConnectCore 8M Nano SOM i.MX 8M Nano SOM | Digi ConnectCore 8M Nano SOM Eingebettetes System-auf-Modul basierend auf dem NXP i.MX 8M Nano-Prozessor; Entwickelt für Langlebigkeit und Skalierbarkeit in industriellen IoT-Anwendungen VIEW PRODUCT LTE-M / NB-IoT Cellular Embedded Modem | Digi XBee 3 Cellular LTE-M / NB-IoT LTE-M / NB-IoT Cellular Embedded Modem | Digi XBee 3 Cellular LTE-M / NB-IoT Kompakte, flexible Mobilfunkverbindung für IoT-Geräte und -Gateways VIEW PRODUCT Edge Computing, Artificial Intelligence, Machine Learning and 5G Edge Computing, Artificial Intelligence, Machine Learning and 5G The symbiotic nature of edge compute and artificial intelligence is interesting because artificial intelligence requires the... READ BLOG Secure, Scalable IoT Device Management Secure, Scalable IoT Device Management The IoT enables corporate, industrial and public sector organizations to control equipment and deliver services in ways that... READ BLOG The 4 Stages of IoT Architecture The 4 Stages of IoT Architecture Iot applications produce data. And where there’s data, there needs to be an IoT architecture that tells the data where to go... READ BLOG Digi International Inc. Celebrates 35 Years of IoT Innovation Digi International Inc. Celebrates 35 Years of IoT Innovation Digi is celebrating 35 years of being in business, delivering M2M and IoT solutions to customers worldwide, with a relentless... READ BLOG What Is IoT Device Management? What Is IoT Device Management? IoT device management provides administrative access to a deployed network of Internet of Things devices. These connected... READ BLOG Cellular Routers, Extenders and Gateways: What Is the Difference? Cellular Routers, Extenders and Gateways: What Is the Difference? Cellular routers, extenders and gateways have similarities and differences in an IoT deployment, from managing communications... READ BLOG 4G to 5G: How Long Will 4G LTE Be Available? 4G to 5G: How Long Will 4G LTE Be Available? When selecting technology for IoT deployments, enterprises need to know how long 4G LTE will be available, whether 5G will make... READ BLOG Digi IX20 4G LTE router Digi IX20 4G LTE router Cloud managed networking for critical applications — rugged, secure LTE router VIEW PRODUCT Edge Computing Edge Computing Edge compute is the data processing that takes place at the network edge to decrease latency and reduce demands on cloud compute and data center resources. LEARN MORE Machine Learning and Machine Vision Work Better with Real Time Edge Processing Machine Learning and Machine Vision Work Better with Real Time Edge Processing Among the most promising complementary technologies today are machine learning (ML) and machine vision (MV). Machine learning... READ BLOG Digi ConnectCore 8M Nano development kit Digi ConnectCore 8M Nano development kit VIEW PRODUCT MODEL Digi ConnectCore 8M Nano Digi ConnectCore 8M Nano Embedded system-on-module based on the NXP i.MX 8M Nano processor; designed for longevity and scalability in industrial IoT applications VIEW PRODUCT Digi WR64 Digi WR64 High performance cellular router with dual redundant communications for complex transit and transportation systems VIEW PRODUCT Digi XBee 3 Cellular LTE-M/NB-IoT Digi XBee 3 Cellular LTE-M/NB-IoT Compact, flexible cellular connectivity for IoT devices and gateways VIEW PRODUCT