All kinds of devices and systems are using edge computing systems in the business world and the everyday. The edge is changing how everyone processes data, but it is facing some key challenges. Here’s a look at the hurdles it must overcome and the trends pushing it forward.
- What is edge computing?
- Edge computing challenges
- Edge computing trends
- What’s in our edge-powered future?
What is edge computing?
Imagine pressing your thumb to the touch ID sensor on your smartphone and waiting five or 10 minutes for it to unlock. Thanks to edge computing, this isn’t necessary. It’s a vital part of all sorts of systems and devices today, from cellphones to automation control systems in manufacturing plants.
Edge computing essentially consists of keeping computing devices as close to sensors and systems as possible. For instance, instead of having an automation system send information to a data center for processing, it’s done immediately by computing infrastructure just a few feet away. The idea is to reduce the time required to process and react to data as much as possible.
Edge computing challenges
Edge computing is a revolutionary technology, but it is still relatively new. Engineers and programmers are still working out a few key challenges.
Security is a major concern today for all types of technology. Cyberthreats have been rising over recent years alongside the cost of successful attacks. In 2021, there was a 105% global increase in ransomware attacks alone. The range of these cyberattacks is growing, with targets no longer limited to big-name brands or large data centers.
Hackers also tend to take advantage of newer technologies, betting that they are less likely to have adequate security infrastructure. Edge computing can make systems more secure by keeping processing local. However, the infrastructure and devices themselves need their own security measures. This includes access control, traffic monitoring, and data backup and protection protocols.
In the future, edge computing devices and processors must have dedicated security protocols built in and may even need their own onboard antivirus and anti-malware software. Encryption from sensors and devices to edge computing processing may also help.
Another major challenge facing edge computing is hardware. Edge computing requires some baseline infrastructure to operate successfully, such as adequate bandwidth and data storage space.
With computing going on locally and running so quickly, edge computing needs plenty of bandwidth to avoid experiencing bottlenecks. It also leads to large amounts of information being processed locally that must be stored. It could remain at a large data center if it were still being processed there. That may not be an ideal solution for edge computing, though.
These aren’t insurmountable challenges, but they can pose issues for some organizations. For instance, a business might want to implement hyperautomation, a business-driven automation method that can benefit greatly from edge computing. However, that company’s edge computing strategy may fall flat if it does not have the funds or space for adequate data storage resources. Hardware can also be a major challenge when scaling over the long term.
Leaders must find ways to make bandwidth and data storage needs more manageable. This will make edge computing more widely and easily accessible.
Edge computing trends
Challenges aren’t the only thing facing edge computing — this technology is also in a period of exciting growth and innovation. Some key trends are appearing, including 5G connectivity.
Edge computing with 5G
5G connectivity is the latest evolution of wireless communications and offers some vital benefits for edge computing. It provides new peak speeds and lower latency, which are extremely helpful.
Latency and bandwidth are among the key challenges facing edge computing. It will likely see a boost as 5G becomes more widely available in the years ahead.
Two emerging technologies will benefit greatly from edge computing and likely lead to a rise in its adoption. Autonomous vehicles and the metaverse are on the verge of mainstream use, with key advances over recent years.
SEE: Metaverse cheat sheet: Everything you need to know (free PDF) (TechRepublic)
For instance, autonomous vehicle developer TuSimple completed its first solo truck drive on a public road in late 2021. The technology still needs work, but it is steadily getting closer to fruition. Other developers, such as Tesla Motors, already have basic self-driving capabilities in their vehicles. Edge computing is vital for autonomous cars — sensor data must be processed instantaneously to operate them successfully.
The metaverse will also increase the adoption of edge computing technologies. Rendering VR experiences requires fast, intense computing power. The edge is the clear solution to bringing this to a mainstream audience. VR headsets designed to handle live gaming experiences in the metaverse may soon feature it as an industry standard.
Combining IoT and the edge
IoT technology has been increasing over recent years, especially in industrial applications such as engineering, the supply chain, and manufacturing. Organizations that utilize increasingly more IoT devices need computing solutions to keep up with rapid, continuous data processing.
Edge computing can help organizations get the most value out of their IoT devices. Processing data from the edge lets companies respond to data more quickly and optimize systems that rely on IoT, such as automated workflows.
What’s in our edge-powered future?
Edge computing might not seem like a huge change for computing and data processing. However, it is transforming how individuals and organizations handle information and use their devices. Things can be processed and analyzed faster than ever before. In industrial settings, this could even save lives.
Edge computing supports the evolution of emerging technologies like autonomous driving and VR, as well as today’s booming tech like IoT. 5G is taking the edge to new heights, and edge computing is on track to revolutionize the future of technology.
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