The primary reason for the growth of edge compute is efficiency. All of that collected data needs to be processed somewhere. And as the volume of IoT data has increased, more and more of the processing is taking place at the edge. Connected devices today are smarter, enabling the ability to program "edge AI" — artificial intelligence at the edge — a growing trend in edge intelligence.
With decades of experience in the rapidly evolving IoT industry, Digi has a complete product offering for optimizing IoT applications with edge compute functionality.
Delivering Only the Important Data
In IoT, massive amounts of data are collected at the edge of the network, but not all of it is useful. On average, most monitoring data tends to be standard “heartbeat” data. If the data isn’t changing significantly, that means things are working well. For example, it wouldn’t make sense to send hours of data to a distant data center, showing that a machine's vital signs haven’t changed.
In the past, companies would send all of their monitoring data into the cloud or to a corporate data center for processing, analysis and storage. As the IoT has grown, however, the volume of data makes this approach impractical. This is where edge compute enters the picture.
Edge compute performs processing close to where the data originates. That can greatly reduce or even eliminate the cost of the bandwidth needed to transmit it to the cloud or the corporate data center. Some applications do need to examine data at the edge. An intelligent or AI-enabled edge compute process can then immediately assess whether the situation demands a response in real time, or send it on to the data center for analysis.
Data collected at the edge falls into roughly three types:
- It needs no further action and does not need to be stored
- It should be retained for later analysis and/or record keeping
- It requires an immediate response
The mission of edge computing is to distinguish between these types of data, identify what level of response is required and act on it accordingly. In most cases it’s far more efficient to perform these functions right there at the edge, where the data is being collected.
When outlier data appears, action may need to be taken. Edge computing can provide a near real-time response to local events thanks to its physical proximity and resulting low latency. No round-trip of data from the edge to the cloud and back again is needed. In addition, the reduced flow of data over the network can produce substantial savings in bandwidth and thus significantly lower networking costs, especially for wireless cellular connections.