Consider the molecule, which contains atoms in various orbits spinning around a nucleus. Now, assume that the molecule is our computing environment. In normal enterprise cases, the computing power is distributed to a central data center (the nucleus). The atoms in the outlying orbits around the nucleus signify edge devices that take their direction from the data center.

However, in edge computing architectures, those edge devices have no direct interaction with the data center. Instead, the interactions between these orbiting data sources take place at or near where they’re physically located, which is at or within an access network’s boundary, at the network’s edge. Because the traveling distance of that data has been drastically shortened, this leads to substantially reduced latency rates.

Edge computing infrastructure is usually administered by various service providers that typically place compute resources at or within the execution layer at the boundary of an access network. The execution layer is a band that is located near the exterior edge of a computing environment that manages the assignment, performance and completion of computing tasks. Placing assets near the execution layer makes it possible for edge computing to offer faster task performance.

The various service providers, which together create a digital ecosystem, can include suppliers, apps and third-party data service providers. Edge devices can also be used in on-premises situations.

Autonomous vehicles (AVs) are self-driving cars and trucks that use edge computing to help car navigation systems gather and interpret the unending stream of data supplied by various sensor inputs (such as radar, LiDAR and traffic cameras). And since traffic situations change by the moment, it’s important that the navigation system be able to interpret and act on this data in real time.

The Department of Energy defines AVs as vehicles that are outfitted with the technology that can operate that vehicle without the driver’s direct control. Now, there are at least 25 different automakers that have already begun some form of AV implementation. The group includes leading manufacturers such as BMW, Ford, Mercedes-Benz Group AG, Tesla and Cadillac.

Now, we’ve entered the phase of implementation where manufacturers are testing their prototypes. There are numerous aspects that make this stage of development especially tricky.

For one thing, autonomous vehicles have been and are being tested in actual traffic conditions, where driving circumstances can change almost instantly. And now, as automakers are incorporating technologies that will surely cause some drivers to pay less attention to the actual driving chores themselves, they are also trying to add features to make sure that drivers of AVs don’t become too distracted.

For example, the Mercedes Drive Pilot system keeps a dashboard camera that is trained on the driver’s face. So, while it’s true that the driver can amuse herself by playing an actual video game on the dashboard, if the camera senses she has left the driver’s seat or is otherwise incapacitated (due to accidental sleeping), the system shuts down. This system is being tested as a startup program in Nevada, where such cars can be driven, but only at speeds under 40 MPH.

Another huge point to consider is the thorny matter of traffic management. Edge computing tackles traffic management problems by locally processing data gathered at traffic intersections. This has several benefits, such as increased safety for pedestrians, better traffic conditions and smoother route coordination for emergency vehicles.

Edge computing even supports the “platooning” of truck convoys, in which a human operator can drive a lead truck while the trucks behind it remain connected in a virtual daisy-chain and in complete lockstep via controlling radio signals

Along with being able to navigate routes, AVs must be trained to share the road and make momentary allowances for poor driving from human drivers, as well as from other AVs. Further, it should be noted that this technology carries with it other infrastructure costs, such as the expense of retrofitting traffic features with edge devices like IoT sensors to communicate instantly with passing AVs and update them to changing traffic patterns, construction updates or weather warnings.

Edge computing puts a new spin on content delivery networks, helping performers and their talents reach a broader spectrum of audience. It does this by using a cache to keep its web pages, music and streaming-video stream content at the edge. That’s how edge computing can lower latency levels and ensure better-quality playback of video and audio when the consumer is streaming content.

The same basic principle is being used by publishers that deliver cloud-based gaming experiences, where games are played in remote servers that route the game action to the player’s screen. These publishers and their games also benefit from edge computing’s reduced latency, which greatly assists virtual reality (VR) applications.

Perhaps the most important usage of edge computing occurs in hospitals and other medical facilities, where the speed of information can literally mean the difference between life and death. Edge computing combats latency through locally based data processing so key patient data can be instantly routed to healthcare professionals for real-time analysis of health information.

With edge computing, doctors can get the real-time information that they need and nursing staff can create complete dashboards for individual patients. Such access to data becomes even more critical with the severity of a surgical procedure, and hospitals rely upon edge computing during remote-controlled procedures, such as robot-assisted surgeries.

Edge computing has other health benefits, as well. As it stands, there are countless situations in hospitals where various monitoring devices and other diagnostic equipment are not connected. The alternative for healthcare providers to lose that steady stream of useful data would normally be storing the data on a third-party cloud.

But another key area that has emerged in recent years is patient privacy. The laws and standards that are enforced through Health Insurance Portability and Accountability Act (HIPAA) protocols were designed in part to help ensure the data privacy rights of US citizens. Edge computing supports those standards by allowing data to be processed locally, instead of on a third-party cloud, which can suffer from security risks.

Factories are rife with opportunities for using edge computing. Edge computing assists in coordinating automation efforts and in making sure that there is a sufficient supply of raw assets needed for manufacturing.

One valuable way edge computing assists manufacturing efforts is through tiny machine learning (tinyML), which supports predictive maintenance practices by finding manufacturing anomalies. Positive results of tinyML include early detection of any needed maintenance procedures, lessened periods of downtime, limited latency and reduced operational costs.

Wearable technology depends upon edge computing to outfit end users with cutting-edge clothing that performs tech functions, such as jackets that contain charging docks for electronic devices.

Farming is often discussed within the context of sustainability, but there’s also the manufacturing aspect of agriculture. Edge computing helps farms in rural locations get consistent access to high-speed connectivity, which they desperately need in order to take advantage of advanced agriculture apps. Edge computing enables farmers to use private wireless networks in rural areas, which supports their use of automation and data analytics. By giving farmers access to real-time information, crop yields can be maximized and their efficiency improved.

When it comes to providing a pleasing customer experience, retailers are always looking for a competitive edge. Edge computing gives retail providers several ways to establish unforgettable user experiences. For many retailers, grid computing offers another way to get a step ahead of competitors, especially within the e-commerce space. Grid computing is a type of distributed computing where a group of machines and/or networks work together for a common computing purpose.

In addition, there’s new technology that allows facial recognition technology to be used with customers. When fully integrated, this technology will enable stores to keep check-out lines running briskly.

Another powerful argument for the use of edge computing involves restocking efforts to make sure inventory keeps pace with store demand. This can be achieved by using cameras and RFID tags and deploying object recognition software in conjunction with existing product information.

Further, as these consumers browse in the store, they can receive helpful reminders and product recommendations about past purchases. This attention to detail helps create a richer and more personalized shopping experience.

No more important issue surrounds computing than data security, and edge computing is specifically designed to enhance security. It starts at the “front gate” by trying to stop malware from infecting organizations’ computer systems through implementing cybersecurity protocols that keep malware from ever reaching their intended endpoint targets within a system.

All industries probably have at least some need for added cybersecurity measures. But some industries (such as defense contractors) have a special need for security that supersedes all other considerations. For companies operating within such a space, edge computing offers the ultimate in security, using local data processing to keep sensitive information away from the potential exposures posed by cloud computing.

Of course, heightened security is also key for financial organizations, and one way edge computing is helping fintech companies is by providing enhanced fraud-detection capabilities. When data processing occurs closer to its original source, it speeds up data analysis and fraudulent transactions can be caught faster.

Because it possesses so many potential benefits for businesses, it may be surprising to learn that edge computing can also assist the environment. One way is by using edge computing to monitor protected species of wildlife inhabiting remote places. Edge computing can help wildlife officials and park rangers identify and stop poaching activities, sometimes before these offenses can even occur.

Another highly significant usage of edge computing involves energy management. Edge computing supports the use of smart grids, which can deliver energy more efficiently and help businesses leave a smaller carbon footprint. Grid computing is a type of distributed computing where a group of machines and/or networks work together for a common computing purpose. Resources are utilized in an optimized manner, thus reducing the amount of waste that can occur when large amounts of power are consumed.

Speaking of energy management, edge computing also supports the remote monitoring of oil and gas assets, and that’s no small feat given some of the rugged locales where oil is drilled (for example, an ocean floor). Edge computing fosters the use of real-time analytics and does it closer to the specific asset, limiting the need for cloud connectivity.

As civil engineers craft urban designs, an increasing number of them are including smart cities in their planning to help drive civic innovation and increased sustainability. By the same token, urban engineers are using edge computing to help them compute measurements that are related to the predictive maintenance of structures, as well as apps related to their overall structural health.

For municipalities, edge computing assists local governments, traffic agencies and various transportation entities by helping them manage their fleets of city vehicles by using the latest real-time conditions. Edge computing platforms can also be used to analyze traffic patterns and relieve congestion in those areas.

In addition to those services, edge devices can be used to process usage data in the field, wherever it exists. Municipal staffers can use edge devices to capture data from public infrastructure, power grids and other sources of data that may signify that urgent action is needed.