Edge Computing - SoojungHong/AI GitHub Wiki

What is Edge computing

(reference from : https://www.ge.com/digital/blog/what-edge-computing)

Using technology innovations, industrial companies are beginning to drive new levels of performance and productivity. And while cloud computing is a major enabler of industrial transformation, edge computing is rapidly becoming a key part of the Industrial Internet of Things (IIoT) equation to accelerate digital transformation.

Edge computing is not a new concept, but several trends have come together to create an opportunity to help industrial organizations turn massive amounts of machine-based data into actionable intelligence closer to the source of the data.

In the context of IIoT, 'edge' refers to the computing infrastructure that exists close to the sources of data, for example, industrial machines (e.g. wind turbine, magnetic resonance (MR) scanner, undersea blowout preventers), industrial controllers such as SCADA systems, and time series databases aggregating data from a variety of equipment and sensors. These devices typically reside away from the centralize computing available in the cloud.

Wikipedia defines Edge Computing as “pushing the frontier of computing applications, data, and services away from centralized nodes to the logical extremes of a network. It enables analytics and data gathering to occur at the source of the data. This approach requires leveraging resources that may not be continuously connected to a network such as laptops, smartphones, tablets and sensors.”

The role of edge computing to date has mostly been used to ingest, store, filter, and send data to cloud systems. We are at a point in time, however, where these computing systems are packing more compute, storage, and analytic power to consume and act on the data at the machine location. This capability will be more than valuable to industrial organizations—it will be indispensable.

What does Edge Computing mean for industry?

By applying big data, advanced analytics, and machine learning to operations, industrials can reduce unplanned downtime, improve asset performance, lower cost of maintenance, and open up potential for new business models that capture as-yet untapped value from machine data.

as more compute, store, and analytic capability is bundled into smaller devices that sit closer to the source of data—namely, industrial machines—edge computing will be instrumental in enabling edge processing to deliver on the promise of the Industrial IoT.

While this concept isn’t new, there are several key drivers making it a more viable reality today:

Cost of compute and sensors continue to plunge (돌진)
More computing power executed in smaller footprint devices (such as a gateway or sensor hub)
Ever-growing volume of data from machines and/or the environment (e.g. weather or market pricing)
Modern machine learning and analytics

Possible outcome

The business implications of this technology are compelling. While there are many outcomes that it can enable for industrial organizations, the Edge Computing Consortium identifies the following:

Predictive maintenance
    Reducing costs
    Security assurance
    Product-to-service extension (new revenue streams)
Energy Efficiency Management
    Lower energy consumption
    Lower maintenance costs
    Higher reliability
Smart manufacturing
    Increased customer demands mean product service life is dramatically reduced
        Customization of production modes
        Small-quantity and multi-batch modes are beginning to replace high-volume manufacturing
Flexible device replacement
    Flexible adjustments to production plan
    Rapid deployment of new processes and models