Put simply, edge computing is a technology that processes data not at a central server, but at the outer edge of the equipment or network where the data is generated — that is, near the terminal device at the far end of the computing system. In a sense, this runs counter to the traditional structure in which everything happens at a central server, in what's known as the cloud data-processing system. It complements the limitations of the traditional central-server approach and represents the most advanced computing system available today.
So what were the limitations of the traditional central server? Cloud systems have long been welcomed for their high accessibility, letting users upload and download the data they need whenever they need it. But as the Industrial Internet of Things (IIoT) rapidly advanced, a problem emerged: the sheer scale and complexity of data pouring out of manufacturing sites began to exceed the server's limits. An IBM report notes that "a single production line in a modern factory can have 2,000 different pieces of equipment. Each piece of equipment has 100 to 200 sensors, continuously collecting data and generating up to 2,200 terabytes of data a month." This overwhelming volume of data introduced latency in transmission, and combined with security concerns arising in the network process, the need for a new data-processing system became increasingly clear.
As central data servers ran into these difficulties, edge computing gained attention as a new data-processing system. Edge computing works by having IoT sensors collect on-site data — from processes, equipment, and the environment — which is then processed by an edge device installed on-site, with only select analyzed data sent on to a central server. Because data is processed at a local device near where it is generated, rather than over the internet, it shortens processing time, dramatically reduces network latency, and eases bandwidth congestion, greatly improving efficiency. Because data is also analyzed and used in real time, it enables immediate judgment and decision-making, while significantly reducing the security risks that can arise during data transmission.
Often cited as core infrastructure, edge computing can be defined, in the end, as a faster and more reliable data-processing system. Many industries rely on technologies that demand near-instant data transmission, and as explained above, edge computing is used to meet this demand by preventing network delays and minimizing data-processing time. In particular, building an edge computing environment allows for stable processing and analysis — free of central-server bottlenecks or network disconnections — even in remote locations with poor internet connectivity, or in manufacturing processes where downtime translates directly into losses. Sending large volumes of data to a central server or the cloud typically requires substantial bandwidth, which is costly to build and maintain; with edge computing, however, data is filtered and pre-processed before it's sent, which is a standout benefit that significantly cuts operating costs.
Because data processing happens locally and the transmission step is largely skipped, companies are also freed from the data-leakage and other security concerns long associated with using central servers or the cloud — some adopt edge computing specifically to comply with data-sovereignty regulations such as the GDPR or HIPAA. And while expanding a business or pursuing digital transformation typically requires substantial cost and time to build a dedicated data center tailored to the company's IT infrastructure, edge computing devices can be added without demanding significant bandwidth from the network core, giving it a considerable edge in scalability as well. Taken together, it's a system that's useful for boosting productivity and efficiency across the entire operation of a business.
Thanks to these advantages and relatively easy device installation, edge computing is now used across many industries. The most representative example is the self-driving car. Self-driving cars are the quintessential IoT model, needing to judge their surroundings in real time — since any delay between transmitting and receiving data raises risk, edge computing enables independent, on-board data analysis and processing. It's also used in healthcare, analyzing patient data in real time to optimize treatment outcomes, and in smart farms, to monitor crop growth and predict yields. Manufacturing is no exception to edge computing's reach. As IoT devices such as sensors and gateways see wider use in production, edge computing is becoming increasingly common there too — enabling real-time process monitoring, predictive maintenance, and machine learning and analysis to boost productivity. It is also being put to serious use to protect worksite safety, helping prevent incidents such as power outages and fires that can occur on the manufacturing floor.
Edge computing technology has become essential infrastructure for the Fourth Industrial Revolution and now holds the key to the AI future ahead. Its technical strengths and benefits — sharpening the precision and flexibility of factory operations while delivering clear gains in productivity and cost savings — have made it a system that companies considering a shift to, or upgrade of, smart factories inevitably have to consider. That said, as research from IT market analysis and consulting firm IDC shows, synchronizing data across multiple pieces of equipment to integrate a system remains a tricky hurdle. To properly understand and apply edge computing, we recommend turning to Innobase, a manufacturing solutions specialist that delivers optimized service implementations by taking network, security, and system considerations fully into account. As a supplier registered under the Smart Manufacturing Innovation Support Program, we understand each company's unique production processes and provide expert consulting to ensure optimized systems are integrated with each piece of equipment. Partner with Innobase — a company that looks beyond today's changes to the production environment of tomorrow.
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