The Rise of Edge Computing: Transforming Data Processing at the Edge

Increasingly “futuristic” adaptations of technology are making their way into homes and organizations across the world. These innovations necessitate an advanced way of processing data. Edge computing allows vast amounts of information to be processed close to the source it is created. This type of local processing speeds up interactions with tech and allows sensors to relay information to quick-responding applications.

Without edge computing, there would be no tech innovation. To unpack this hefty claim, let’s explore how the rise of edge computing is transforming the way we use and interact with data daily — and how it will impact the future.

What is Edge Computing?

Simply put, edge computing is the processing of data conducted near the source of that data. In traditional setups, data is sent to data centers or cloud-based applications to be processed. Instead, edge computing utilizes the Internet of Things (IoT) and moves at least some of the data storage and processing to the “edge” of an organization.

This typically means somewhere like a retail floor or warehouse. However, it can be in more consumer-focused settings, like the route that is being traversed via GPS or the hand of the person holding a smartphone. IoT sensors can accumulate data in real-time, and edge computing processes that data in the same place — rather than having to send it over to an enterprise data center or upload it to the cloud first. This reduces latency and speeds up the entire process.

How Edge Computing’s Trajectory Will Change

Think about how you open your smartphone with the touch of a finger or flash of your face. This data is processed as close to you as possible to make the transaction occur as quickly as possible. This type of edge computing is being increasingly adapted to all sorts of tech and business processes to facilitate safer, faster data processing. The advancement of tech necessitates this innovative computing, allowing for quicker response times.

Currently, sensors on vehicles can detect real-time driving threats and respond accordingly. Security cameras in warehouses and retail establishments can detect intruders via face recognition and other algorithmic sequences, responding quickly by alerting authorities, warning staff, shutting down access to important files, and locking down areas.

More recent consumer-facing innovations include VR headsets and 5G-enabled smartphones that must employ edge computing to achieve ultra-low-latency processing. Some of the proposed innovations include smart cities, more detailed remote healthcare monitoring, accurately predictive manufacturing, and increasingly personalized consumer recommendations. These are prime examples of the types of edge computing that will continue to be developed and refined as new tech requires it.

Edge Computing and Artificial Intelligence

Artificial intelligence (AI) is a paramount application in which edge computing is driving advancements. AI investments are already skyrocketing thanks to its significant market impact. However, the risks of AI are clear. There is much discourse surrounding the ethics of AI.

However, edge AI as it already exists can enhance the speed and safety of data processing. AI is a safer way to send personal and sensitive data like financial information — rather than sending it through the cloud. It only sends the pertinent information instead of the entirety of your databank and keeps it localized, reducing the probability of a data breach. Benefits like this make it a clear positive application to the tech industry. User-friendly interfaces and quick response times make for products with enhanced user experiences (UX). UX is supremely important in selling products, so edge computing gives startup and company innovations that edge they need to beat out the competition.

Cybersecurity Considerations

While AI on the edge enhances information privacy, there are still some cybersecurity concerns. The distributed nature of edge computing does require extra measures for secure data management. Localized data simply doesn’t have the robust physical protection that larger data centers can offer. Bad actors can access your specific information by removing a disk drive or transferring information from your hard drive onto a flash drive.

However, this also means that the attacks have to be hyper-targeted. Data centers and companies that process large amounts of data from a variety of sources are typically targeted for their sheer amount of sensitive information. Instead, edge computing can employ cybersecurity measures that protect smaller, localized banks of information. For example, it’s important to use secure data destruction best practices when edge computing. Rather than keeping unused, old data on local devices, you should have a system where you regularly audit and dispose of that data safely. This also frees up more space on your local systems so that they can continue to store and process new data quickly and without issue.

Implications for Business Data Management

Business data management, in particular, requires robust storage and quick processing. Raw data should be able to be processed and analyzed on-site for optimal workflow. Utility plants, warehouses, retail centers, and even smart cities will continue to use edge computing to process information. Data migration from one system to another will make it easier for businesses to onboard new tech and upgrade their processes. The ability of tech to employ predictive analytics and optimize processes will only continue to expand.

With edge computing, businesses can harness real-time insights and adjust accordingly at a much faster rate than if that data was stored elsewhere. AI can even analyze the data and provide recommendations, such as advice for temperature regulation for energy savings in a warehouse. This intersects with the increasing demand for sustainability and further illustrates just why edge computing isn’t going anywhere — except up.

Edge Computing Trends of the Future

Existing technology will continue to adopt edge computing to upgrade itself. New network architectures can be constructed for this existing technology, interweaving edge computing into businesses and homes seamlessly. People and organizations are already used to the technology, and edge computing with AI integrations is user-friendly enough to work on their own without much effort from the end user. The end user will only notice a rapid uptick in the speed of their interactions with the tech.

Data Centers Shutting Down — But Also Innovating

According to Gartner, an estimated 75% of data management will take place through edge computing by 2025. Central data centers will likely begin to shut down a lot of their facilities. However, this doesn’t mean an end to distributed data processing. The facilities that remain open will also gain increased bandwidth from the addition of edge computing into modern tech. That will free up storage space as well as resources to facilitate growth and ingenuity in the space.

AI and Cloud-based Applications Working Together

Cloud-based computing is not poised to be entirely replaced by AI and edge computing, either. Edge computing only offers so much in terms of processing power. Cloud computing needs AI just as much as AI needs cloud computing. Many AI applications need cloud-based infrastructure to work effectively. Businesses and consumers, alike, are likely to continue using both local and cloud-based data storage and processing for their daily tasks.

Industry-Specific Edge Computing Innovations

Businesses and consumers all stand to benefit from faster processing and real-time insights. Some specific industries that will use edge computing to innovate heavily include:

• Healthcare: Better care will be possible through increased use of wearable technology for inpatient and outpatient monitoring of increasingly more vitals, allowing personalized, predictive care onsite and remotely;
• Education: Remote classrooms saw a surge in prevalence during the COVID-19 pandemic, and innovations using edge computing will only advance in this field. Expect to see more augmented reality and virtual assistants in educational settings, as well as generative AI that uses localized data to analyze and respond to student questions and comments.
• Industrial Manufacturing: Warehouses and shipment centers already employ edge computing to predict inventory needs and optimal traffic routes. In the future, local data can be analyzed quickly to enhance processes by providing suggestions for optimized workflow, even down to employee performance. These facilities will also become safer as edge computing becomes more adept at predicting and responding to threats.
• Retail: Personalized recommendations are already available when browsing the internet, but retail operations will be able to employ edge computing to offer real-time, in-person suggestions to shoppers. The IoT will also likely innovate to provide more accurate stock levels and monitor stores for occupancy and threats.

These are just a few examples of how edge computing is changing the future of technology and how the world interacts with it. While it won’t completely replace off-site and cloud-based data storage and processing, it will enhance it and free up bandwidth to allow for further innovation across the board. Improved performance and security will appease concerns regarding the safety and efficacy of edge computing.

Going Forward

Watch for these innovations to pop up everywhere around you — from the phone in your hand to your shopping cart. The changes will be barely detectable to the typical user, but an interest in edge computing can give you a better understanding of how your data is being used. Eco-conscious businesses and consumers can even dive deeper to develop solutions for rampant ecological problems such as excess energy waste. In any case, edge computing is positioned to only get better at handling data, so expect to see an uptick in robust applications in the coming years.

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