You probably know already, but just so we're all on the same page, edge computing refers to IT architecture of a distributed nature. Data processing of a given client's network is processed at said network's edge, right near where the data came from. So, a sensor array that collects information can simultaneously help process that data on an edge network.
The "edge" utilizes IoT to make this possible. Think of it as a sort of miniature cloud on your business campus. As a cloud networks servers together to compound data processing capability, edge networks use multiple devices locally for the same purpose. Another way of looking at it is: edge computing filters useless data from useful data.
There's a flood of information out there, and information is a currency in the modern world; you can do a lot with it. So, what does this mean for your business? Well, that depends on how prevalent edge computing gets. In 2022, it's expected to advance across several key industries, and in several specific ways. We'll explore these edge computing trends here specifically:
Data Filtration via Edge Tech Will Optimize Operations Ahead of Competitors
So, the edge makes data at the source of its composition more accessible, and filters out the "noise", as it were, from data you can use. Now you can operate more optimally and get more done with greater cost-effectiveness.
You can identify redundancies you didn't know were there, maximize equipment utility, put processes in place that will increase the ROI of your operational spending, and the list goes on. Businesses that leverage edge computing toward more optimal functionality now will have an "edge" (pun intended) on competitors.
Eventually, this will be standard practice; right now, people are finding their "rhythm", as it were, in this new tech. Ultimately, there's a competitive window for edge computing trends in 2022, and savvy businesses are definitely going to take it.
As 5G Expands, IoT, AI, ML, and Automation on Edge Networks Does Too
IoT tech facilitates edge computing, and 5G optimizes IoT. Collaterally, Artificial Intelligence and Machine Learning are also developing at break-neck speed. Many AI and ML breakthroughs crossover with IoT. Ultimately, 5G tech is going to act like a rising tide lifting all ships.
In this analogy, the "ships" are AI, ML, and similar software innovations. Edge computing utilizing IoT, AI, and ML on a 5G network will reveal more data, and more quickly. You'll get real-time insights impossible before. Multiple industries have the capability of optimizing with such information toward increasingly efficient automation.
Automation can even initiate failover protections as regards operational redundancies in the event of emergency. All devices can be monitored, and algorithms can be put in place to identify features of tech functionality that could indicate an issue.
Now, as of 2022, few industrial campuses have production floors so automated only one or two people are necessary to manage them, but the technology is already here. The trick is applying such tech infrastructurally. Businesses that get ahead of the pack will, as pointed out earlier, have a definite competitive edge.
Operational Capability Locally Regardless of External Internet Availability
This is something that's already been around for a while, edge computing is just a new innovation. Big campuses with lots of techs have on-site server arrays that already form their own "intranet", which is a sort of on-site internet separate from the "real" internet.
Much of localized "intranet" networks are actually accessible via the dark web, but if all contact with external web options were ceased, said localized intranet would remain. Well, with edge computing the same potentiality exists. If a connection to the cloud or an external internet provider is lost, an edge network keeps functioning.
It's Going to Be Everywhere; Even in Agricultural Communities
Owing to the associated advantages of edge computing, it's to be expected such innovations will transition beyond the tech sector. IoT tech used on agricultural equipment can save time and money for farmers, helping them be more productive and increase their profit.
Agriculture tends to produce marginal annual returns, the wealth being in the land or the livestock (or both). As an example: a $1,000,000 profit for the year might have $960,000 in expenses, leaving the family that farms only $40k in profit.
With edge computing utilizing IoT to help initiate automation, that margin may jump from $40k to $100k or more, allowing farmers to branch out and optimize even further.
The Edge Is Getting Foggy and Will Continue to in 2022
Here's how fog computing works: there's a "compute layer" between the edge and the cloud. Basically, "foggy" edge computing is the "edge" of the "edge" network. Basically, managed fog computing solutions receive information from an edge network before it gets to the cloud--it's a sort of middleman, if you will, that gains access to useful data your business can leverage toward optimization.
The right fog network can parse between data sets to separate out that which is useful from that which isn't. What's relevant will stay on the cloud, what isn't will disappear--unless, of course, there's a reason for a business to keep that non-relevant data.
So, what does this look like in practice? Say there's a temperature sensor in your network that continuously collects temperature data. That data is sent to the cloud, and it's monitored for spikes. Well, fog computing would determine if there were any data relevant enough to send to the cloud, saving time and complication in operations when nothing remarkable is recorded.
Foggy edge IT pros would determine what constituted relevant data, and voila: things like bandwidth and latency evaporate. The bigger the business, the more speed a properly managed foggy edge network brings. It's going to be negligible for smaller operations, but the bandwidth saved is ultimately going to reduce operational costs collaterally over time.
An Expansion of Edge Tech in Retail
Think of local department stores, the security cameras they use, the devices at entrances and exits, self-checkout equipment, lighting, HVAC, shipping, receiving, and all the other little departments such stores must manage.
There's a high potential for edge computing. Now imagine that spread out across hundreds of stores nationwide. That's a big "edge". Expect to see retail stores advance accordingly.
Edge Tech in the Energy Industry
Agriculture and retail aren't the only places where edge computing is useful. Pipelines often stretch thousands of miles. Fitting the whole line with IoT requisite to facilitate edge computing is good for oil companies and the environment collaterally--spills can be anticipated and intervention can be applied prior to an incident. Furthermore, with massive pipelines, manpower just isn't available regardless of edge options.
This kind of tech will actually take the oil industry into new territory. Instead of flying planes down the pipe, data can be gathered and transmitted using edge and foggy edge tech, being managed remotely with greater cost-effectiveness and convenience. Pressure anomalies can be quickly identified, diagnosed, and either rectified or left, depending on the situation. If valves need to be shut down, this can be done remotely.
An Expansion in Edge Workloads
Naturally, advantages of more data collection and filtration toward optimal use will increase the workloads demanded of edge networks, which subsequently will yield breakthroughs in the IT sector for that area. So, the edge itself will become sharper--foggy edge applications are a good indicator of that.
AI Computing on the Edge With IoT-Enabled Vehicles
Computers have been central to automobile functionality for decades now. It was a natural step to extend that computational capability to IoT applications and, ultimately, edge computing. Your vehicle's computer saves mechanics and manufacturers time in diagnosing operational issues with the engine or other functional components of the car.
With IoT applied to vehicular functionality, as the car passes into and out of coverage zones, it can send automatic updates to the manufacturers. Essentially, now each vehicle that is IoT-enabled functions as a sort of case study in the longevity of certain vehicular components. How long will the factory water pump last? What about the transmission? At what point do the majority of vehicles experience issues in component functionality, and what does that look like?
Already, careful data has been kept on things like this related to automotive functionality. For example, there's an older RV made by Volkswagen and Winnebago called the Winnebago Rialta; it's really just a glorified camper van. At any rate, the run of these vehicles went from the mid-nineties to the early aughts.
Computational data and driver reports helped manufacturers realize there was an issue with this particular vehicle's transmission. At about the 100,000-mile mark, most of these vehicles had serious transmission problems. If the vehicle made it past the 100k mark, however, the transmission tended to last to 200k miles. So, there was a manufacturer issue which was noted in the literature well before cloud computing, IoT, or edge computing even existed.
Now that manufacturers and mechanics have access to such data, it becomes possible to anticipate issues and correct them before a driver is left without an option, with a broken-down car by the side of the road.
In a big city with thousands of drivers making moves which produce data via IoT, an edge network develops that can act as a sort of digital "cradle" for drivers, allowing for computational navigation anticipation, expanding safety and security for drivers even in congested areas. So, the advantage to vehicles through IoT and edge computing is twofold: vehicular functionality can be enhanced, as can driver safety. Collaterally, things like road design will ultimately shift over time as data reveals better ways of handling cars.
How Edge Computing and AI Will Impact Autonomous Vehicles
For autonomous vehicles, edge computing may be the saving grace of the innovation. Many autonomous vehicle designs have been put into action as of 2022. By 2016, Uber had a few "driverless" cars in San Francisco, but there have been accidents which have limited the trend; still, as of February, 2022 the "Cruise" line of driverless cars has become a big player in the city; and that trend will only increase over time.
With millions of vehicles providing continuous IoT data to the edge network, now driverless cars can operate much more safely. Not only can trends be used to design safety features into such vehicles, but real-time data concerning traffic on a given route for such a vehicle can also be processed to help the software controlling the vehicle react properly when unexpected dangers on the road show up.
Modern cars contain hundreds of sensors even if they're not "autonomous", meaning the data they produce is available for exploration and can be leveraged toward greater safety by vehicles with savvy edge computing software designed for the processing and application of relevant data. As edge computing trends become more streamlined, the sort of data that is used for this purpose, its transmission to the cloud, and how it is used will necessarily become more streamlined. Through this method of qualifying information, data that isn't sensitive to individual drivers, or that contains other things which are private, can be automatically restricted from cloud data repositories. Doing so effectively will reduce the expense of transmitting data, as only that which is needed will be sent to cloud arrays designed to help manage autonomous vehicles.
As Tesla's market share increases, and more competitors develop, IoT information will form edge networks that are used to manage the battery life of varying electric options. Such networks can help inform drivers where charging centers are and when precisely they'll need to find one based on predictions from patterns in the data.
Furthermore, edge data can contribute to better traffic management. Autonomous vehicles can all "report in", helping computers automatically route traffic around congested areas. The same data can be sent as updates to non-autonomous vehicles, and in fact we are already seeing this a little bit with Google. If you're in Google Maps, and the little voice comes over your speakers advising you to take an alternate route with less traffic, cloud-based IoT and burgeoning edge networks are the foundation from which that informed suggestion came. As computational vehicular design, IoT, edge computing, and autonomous options become more integral, expect such edge-based data applications to increase substantially.