And global spending on hardware, software, and services for edge is forecast to increase from approximately $208 billion in 2023 to $317 billion in 2026, according to a new report from Accenture (PDF).
“What makes edge so exciting is the potential it has for transforming business across every industry and function from customer engagement and marketing to production and back office operations,” according to Accenture. “In all cases, edge helps make business functions proactive and adaptive — often in real-time — leading to new, optimized experiences for people.”
The top use cases for edge computing are in industries and companies with high data volume and velocity, says Jason Andersen, vice president of strategy and product management at Stratus Technologies, a provider of autonomous edge computing platforms.
Here are six real-world edge computing use cases:
6. Food and Beverage
Food and beverage companies leverage edge computing because of its real-time nature, Andersen says.
“For example, a company that’s processing hundreds of cookies a minute, with that type of load, they start to have a higher degree of automation and a lot of machines involved that are all pushing data out and looking for control from a human operator,” he says.
Real-time visibility is crucial with that level of complexity, he adds.
“Companies with this production level need control to ensure the machines are operating as they’re supposed to, and edge computing can help manage, or at least monitor, the machine doing that process,” Andersen notes.
5. Edge-Enabled Fighter Jets and Drones
Modern aircraft, such as fighter jets and drones, are increasingly complex information systems with advanced onboard electronics and the ability to network with other assets to collect data and analyze it onboard, says Ian Ferguson, vice president of marketing for Lynx Software Technologies, a provider of solutions for the mission-critical edge.
“For example, edge computing allows a fighter jet to share real-time data and coordinate with drone wingmen without waiting on a remote server,” he says. “This accelerates and increases the reliability of critical combat decision making.”
By keeping processing and analytics at the edge, aircraft are able to operate independently when maintaining a network connection is difficult, according to Ferguson. And edge computing also addresses security needs by processing sensitive defense data locally to avoid interception or breaches.
“In short, edge computing unlocks new capabilities by shifting processing and decision making to the tactical edge,” Ferguson says.
4. Bridge Inspection
“One of the most interesting, real-world edge computing use cases that my team is currently working on is bridge inspection,” says Gretchen Stewart, chief data scientist at Intel Public Sector.
As dollars flow to cities and states through the federal Infrastructure Investment and Jobs Act, public works teams are challenged to inspect thousands of bridges and prioritize those most needing repair.
“By equipping drones with computer vision, inspectors can swiftly and accurately identify areas requiring repair or maintenance, enabling timely interventions,” Stewart notes. “These sophisticated algorithms can detect even the tiniest cracks, rust, or signs of wear and tear, leading to an objective ranking of those bridges in the worst condition.”
This combination of computer vision and drones, with analysis performed at the edge, significantly reduces the risks and costs associated with traditional manual inspections.
“In the past, bridge inspections often required specialized equipment and personnel to access hard-to-reach areas, posing safety hazards and logistical challenges,” Stewart says.
With computer vision-equipped drones, inspectors can remotely survey these areas more quickly and minimize disruptions to local communities, she adds. Inspectors can also use this data for predictive maintenance to forecast future repairs that will be needed.
3. Fraud Detection
Moving fraud detection to the edge leveraging content delivery networks, such as Cloudflare and AWS CloudFront, and combining it with security is the only way to manage the technological and cultural drivers shaping the future of digital transactions, says Alisdair Faulkner, co-founder and CEO of Darwinium, a digital fraud prevention company.
“The increased complexity of fraud, the increased transaction load due to the rise of instant payment platforms, such as Zelle and Venmo, and the increasing use of mobile devices to facilitate payments has changed the landscape,” he says. “The field has changed and we need to change with it.”
Edge computing helps companies do that by geographically distributing computation and data storage closer to the users and their devices, resulting in faster distribution, less latency, and improved security/privacy since personal data doesn’t travel as far – if at all, Faulkner says.
Using the edge as a deployment and encryption point provides digital businesses, including those in commerce, banking, retail, gaming, travel, healthcare, government, and other sectors where fraud runs rampant, with a number of benefits.
“One of these benefits is the preservation of data privacy and customer security via encryption at the edge,” Faulkner says. “Encrypting end-user data on the edge allows businesses to store it within their infrastructure, which optimizes user privacy and security.”
2. Early Wildfire Detection
Reducing the frequency and scale of large wildfires requires fast response times, but the remoteness and vastness of forests make it challenging to identify and communicate potential threats, says Carsten Brinkschulte, CEO of Dryad Networks, a provider of fire detection and health monitoring solutions for forests.
However, he says edge computing and AI can help solve this challenge by bringing powerful technology close to the genesis of a wildfire.
“At Dryad, we provide ultra-early detection of wildfires using solar-powered gas sensors in a large-scale IoT mesh network placed in the forest,” Brinkschulte says.
Dryad’s Silvanet solution detects fires within the first hour by applying machine learning to solar-powered gas sensors, detecting hydrogen, carbon monoxide, and volatile organic compounds, he says.
Machine learning enables the sensors to detect and identify gas compositions that could be a smoldering fire while filtering out the smell of a diesel truck driving by, Brinkschulte says.
“Once a potential smoldering fire is detected, information is relayed over a large-scale LoRaWAN mesh network to first responders and forest managers,” he says.
This intelligent detection, analysis, identification, and communication all happens in seconds on-device at the edge, enabling ultra-early detection of wildfires, he explains.
“Edge computing is mandated by the narrow bandwidth of the LoRaWAN mesh network, ruling out traditional cloud-based execution of the AI engine,” Brinkschulte says. “Our mesh gateway technology creates an IoT network that can be deployed deep in the forest where no regular telecommunications network infrastructure can reach.”
Today, companies in all industries must make decisions in microseconds by technology that “thinks,” making edge computing even more necessary in a hyperconnected, data-abundant world, says Vinay Ravuri, CEO of EdgeQ, a 5G chip startup.
Telecom providers are the next significant adopters of edge computing, aiming to converge cloud, compute, and connectivity for the edge.
In particular, 5 G’s low latency and extended coverage become the wireless conduit enabling mobile edge computing to reach its full potential by processing data at the network’s edge rather than sending it back to centralized data centers, Ravuri says.
“5G is expected to support many new end-client devices,” he says. “By deploying edge computing as with 5G, operators can better help harness and monetize the vast amount of data these devices generate locally, saving bandwidth, cycles, and further reducing latency. Combining edge computing with 5G will become a prerequisite for autonomous applications that require real-time processing.”
For example, mobile edge computing applications will quickly expand into the realm of autonomous vehicles and connected vehicles, according to Ravuri.
He says that cars and traffic control systems will need to constantly sense, analyze, and swap data to function correctly. Whether it is autonomous driving or self-delivery vehicles, these “data centers on wheels” will need to sense and communicate with surrounding traffic for safe and efficient navigation.
“This way, the data generated by the cars and the traffic systems is localized without needing to traverse back to a centralized cloud for processing,” Ravuri says.
Additionally, object detection and recognition will become “mission critical.” Deploying edge computing architecture within each vehicle will be necessary as autonomous driving combines high-resolution maps with traffic pattern reports with real-time autonomous response.
“As more and more connected vehicles flood the streets, the network will become incredibly congested, and data movement within the cars and local area networks will become necessary,” Ravuri notes. “The addition of modem chips to each vehicle to enable a resilient, low-latency 5G network would be essential to edge computing.”
Edge use cases are evolutionary, not revolutionary, says Theresa Lanowitz, head of cybersecurity evangelism at AT&T Business. Use points help derive predictable business outcomes at a “macro-level” by collecting and analyzing data at a “micro level.”
“Edge computing is a new generation of computing that involves a change in the computing ecosystem … and the goal of delivering a digital-first experience based on near-real-time information,” she says. “And as use cases evolve, resilience simultaneously gains importance.”