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Edge Computing and Analytics Will Fuel IoT Deployments in 2017

Posted by Barry Kaufman on Jan 19, 2017 1:58:53 PM
Barry Kaufman

One of the most awe-inspiring aspects of the internet of things (IoT) is the sheer impact of billions of devices connecting to the cloud and uploading massive amounts of data for analysis and strategic use. But what happens when there are so many connected devices and so much data generated that the demands on the infrastructure are overwhelming? Enter edge computing.

Edge computing is a relatively new IoT advancement that moves data processing out of the cloud and down to the device level. By adding on-premise devices like programmable automation controllers (PACs) into your IoT stack, edge computing enables more processing to occur on a local level. The data are then analyzed on-site, and only the necessary data are uploaded to the cloud.

Running parallel to the development of edge computing is another similar solution: fog computing. Fog computing is a term that was coined by Cisco and describes a process for moving intelligence to the local level much like edge computing, but in a slightly different way. In edge computing, processors are added to the device level, with data sometimes being processed directly at your sensors and actuators. In fog computing, the data are still sent upstream from your devices, but instead of being uploaded to the cloud, the data are processed by an on-premise “fog node” or IoT gateway. The mass of data can then be used for analysis and monitoring at the local level with only the necessary or changed information being uploaded to the cloud.

There are pros and cons to both approaches, but the benefits of local processing in general could have huge implications for IoT deployments at the industrial and enterprise levels.

Let’s consider a quick example.

At any IoT-enabled manufacturing plant, the amount of data being collected and stored can be enormous. Every second, sensors are monitoring temperature levels, moisture levels, light levels, energy levels, machine performance diagnostics, and a huge amount of other data points that are critical to on-site performance. Without edge or fog computing, all of this information gets uploaded to the cloud and then pushed to programs that deliver analysis and monitoring to key decision makers within the enterprise.

As IoT deployments have grown, the amount of data being collected has multiplied, and large operations are now processing more and more of this kind of data every day. Uploading this amount of raw data can slow transfer speeds to a crawl, preventing decision makers from seeing mission-critical information in a timely fashion.

What happens if our example manufacturing plant includes a cleanroom that has been environmentally compromised? Perhaps moisture has begun to seep in from a leak the sensors in the cleanroom have detected this and tried to push an alarm to the cloud. If the network is overwhelmed by non-critical data transfer, this major development could take longer to get to an engineer, thus resulting in a longer contamination period in the cleanroom and a greater overall impact to the enterprise. This is and will be a very real and serious problem faced by many enterprises as IoT is deployed more in 2017.

With edge or fog computing, this leak would be caught almost immediately and pushed to on-premise software through the local area network, alerting responsible engineers in seconds – sometimes even less. The data are then uploaded to the cloud for future analysis, and a potential major crisis is averted.

The implications of this for IoT growth in 2017 are immense. IoT vendors are gearing up for the wave of enterprises that will turn to edge/fog computing to fuel their IoT systems, and many new products and services are being brought to market as a result.

In June of 2016, Cisco and IBM announced a partnership designed to couple IBM’s Watson IoT capabilities with the power of Cisco’s edge analytics. These two IoT giants intend to “target companies operating on the edge of computer networks such as oil rigs, factories, shipping companies and mines, where time is of the essence, but bandwidth is often lacking” (more on this in Cisco’s official announcement post). With this collaboration, key personnel at the enabled locations would have access to powerful, real-time insights that may not have been available in the past due to limited connectivity issues.

Intel and Hewlett Packard Enterprise are among the other tech giants leading the way at the edge. With new intelligent gateway products designed to push data and analytics from the cloud to the local level, these vendors are contributing to the ever-important technology that makes all this possible.

This all sets the stage for a huge year of growth in 2017 for both IoT as a whole and edge/fog computing specifically. At Fast Lane, we are gearing up for these changes to have a big impact and we think you should too. We offer classes, programs and services that can help get you and your employees up to speed on new developments in the world of IoT. As things continue to change and evolve, check out our IoT enablement services, and don’t let your business get left behind.

Topics: Internet of Things

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