With remote work on the rise and businesses leveraging digital platforms and services more than ever before, edge computing could be the solution for faster, cheaper, and more reliable data processing.
Sometimes faster data processing is a luxury — other times it’s a crucial aspect in decision making for businesses and people alike, especially in times of crisis.
In financial services, for example, traders often need to make real-time decisions based on events or large market shifts, and any lag in data computation can result in an enormous loss of money.
Across healthcare, wearables (including portable EKG devices and sensors for monitoring temperature) are increasingly important for collecting patient data. With the uptick in patient data in hospitals, experiencing even the smallest delay in processing can be a matter of life or death.
While the bulk of data processing for connected devices across industries now happens in the cloud, sending data back and forth across a central server can take seconds too long — and also requires a ton of expensive infrastructure. By the year 2024, an estimated 149 zettabytes — the equivalent of more than 149T gigabytes —will be created globally every single day.
With so many more devices connected to the internet and generating data, cloud computing might not be able to handle it all — or lower latency enough to be useful in critical decision-making moments.
This is where edge computing comes in.
Edge computing represents the fourth major paradigm shift in modern computing. The mainframes of the 1950s and ’60s were the first, followed by the shift to the client/server model of the ’70s and ’80s, a model that lasted well into the early 2000s. The cloud was the third major shift, a development pioneered by Amazon’s AWS. Now, edge computing has the potential to be as disruptive to how we share information as the cloud was to the client/server model.
Edge computing technology promises faster processing at potentially lower costs right at the source of the data and has the power to transform virtually every industry and economic sector. But how does it work?
What Is Edge Computing?
Edge computing enables connected devices to process data closer to where it is created — or the “edge.” This can be either within the device itself (i.e., sensors), or close to the device, and provides an alternative to sending data to a centralized cloud for processing. Some of the biggest players in tech — including Amazon, Microsoft, and Google — are exploring edge computing, potentially giving rise to the next big computing race.
Source: Research Gate
While Amazon Web Services (AWS) continues to lead in the public cloud landscape, it remains to be seen who will emerge as the leader in this nascent edge computing space. In this explainer, we dive more into what edge computing is, the benefits associated with the technology, and its applications across a wide range of industries.
Table of Contents:
- Cloud computing enables a connected world
- The shift to edge computing
- Agriculture and smart farms
- Energy & grid control
- Financial Services
A changing computing landscape
Before we can understand edge computing, we must take a look at how its predecessor — cloud computing — has paved the way for IoT devices worldwide.
CLOUD COMPUTING ENABLES A CONNECTED WORLD
From wearables to connected kitchen appliances, internet-connected devices are everywhere. The global IoT market is expected to exceed $5.4T by 2035. As a result, cloud computing — the process by which many of these smart devices connect to the internet to operate — has become an increasingly dominant trend.
Mentions of “cloud computing”
Cloud computing enables companies to store and process data (among other computing tasks) outside of their own physical hardware and across a network of remote servers — popularly known as the “cloud.”
For example, a person may choose to back up her smartphone using Apple’s iCloud. She can then retrieve her smartphone data via another internet-enabled device, such as her desktop computer, by logging into her account and connecting to the cloud. Her information is no longer confined to the capacity of the internal hard drive on her smartphone or desktop.
This is just one of many cloud computing use cases. Another example is running full-scale applications that are accessed through a web or mobile browser.
While “the cloud” conjures up imagery of a vast, decentralized network upon which much of the world’s data resides, physical data centers comprise much of the cloud’s actual architecture. As of early 2021, there were nearly 1,500 major data centers located across the United States. The vast, warehouse-sized facilities can house tens of thousands of individual servers, and it is these servers that store and process much of the data users rely on every day, from online streaming services to collaborative document editing tools. Major players such as Amazon, Google, and Microsoft typically maintain their own data centers, whereas SaaS companies such as Dropbox and Salesforce often rely on data centers shared by multiple service providers, known as colocation or multitenant data centers.
A user’s physical proximity to a data center often has a direct impact on how quickly that user’s tools and apps can send and receive information, a factor known as latency. For most applications, delays in latency aren’t significantly disruptive. Streaming video services, for example, might experience latency delays of a few milliseconds, which aren’t likely to be perceptible to most users.
But centralized cloud computing is not ideal for all applications and use cases. Edge computing provides solutions where traditional cloud infrastructure may fall short.
THE SHIFT TO EDGE COMPUTING
In our data-heavy future, with billions of devices connected to the internet, faster and more reliable data processing will become crucial.
Cloud computing, given its centralized nature, has proven cost-effective and flexible over recent years, but the rise of IoT and mobile computing has put a strain on networking bandwidth or how much data can be transferred across a network in a given period of time.
Ultimately, not all smart devices need to utilize cloud computing to operate. In some cases, the back and forth can — and should — be avoided, which is a core concept of edge computing.
Given its broad range of applications, from helping autonomous vehicles speed up reaction times to protecting sensitive health data, the edge computing infrastructure market is projected to be worth $450B, according to CB Insights’ Industry Analyst Consensus.
Edge computing enables data to be processed closer to where it’s created (i.e. motors, pumps, generators, or other sensors), reducing the need to transfer data back and forth between the cloud.
Edge computing infrastructure is described as a “mesh network of micro data centers that process or store critical data locally and push all received data to a central data center or cloud storage repository, in a footprint of less than 100 square feet,” according to research firm IDC.
To put it another way, rather than storing and processing vast amounts of data in large, centralized data centers that may be hundreds or even thousands of miles from a device on the network, edge computing relies on a larger, distributed network of much smaller data nodes to reduce latency and increase speed and responsiveness.
For example, a train may contain sensors that can immediately provide the status of its engine. In this scenario, sensor data does not need to travel to a data center, whether on the train or in the cloud, to see whether something is impacting operations or not.
Localizing data processing and storage puts less of a strain on computing networks. When less data is sent to the cloud, the likelihood of latency — the delay in data processing that results from the interaction between the cloud and IoT devices — decreases.
This also places more responsibility on the hardware underlying edge computing technology, which consists of sensors for collecting data and CPUs or GPUs for processing data within connected devices.
As edge computing takes off, it is important to understand another technology that edge devices are involved with: fog computing.
While edge computing refers more specifically to the computational processes being done at or near the “edge” of a network, fog computing refers to the network connections between the edge devices and the cloud.
In other words, fog computing extends the cloud closer to the edge of a network; therefore, “fog computing always uses edge computing, but not the other way around,” according to OpenFog.
Going back to our train scenario: sensors can gather data, but cannot immediately act upon it. For example, if a train engineer wants information on how a train’s wheels and brakes have been operating, they can use sensor data aggregated over time to anticipate whether parts need service or not.
In this situation, data processing uses the edge, but it is not always immediate (unlike determining engine status). Using fog computing, short-term analytics can be assessed at a given point in time and do not require full travel back to a centralized cloud.
Thus, it’s important to understand that while edge computing complements cloud computing and works very closely with fog computing, it is by no means here to replace either.
Advantages of edge computing
Although a nascent space, edge computing offers some obvious benefits, including:
- Real-time or faster data processing and analysis: Data is processed closer to the source, not in an external data center or cloud, which reduces lag time.
- Lower costs: Enterprises spend less on data management solutions for local devices than for cloud and data center networks.
- Less network traffic: With an increasing number of IoT devices, data generation continues to rise at record rates. As a result, network bandwidth becomes more limited, overwhelming the cloud and leading to a greater bottleneck of data.
- Increased application efficiency: With lower latency levels, applications can operate more efficiently and at faster speeds.
Deemphasizing the cloud’s role also decreases the potential for having a single point of failure.
For example, if a company utilizes a centralized cloud to store its data and the cloud goes down, the data becomes inaccessible until the issue is resolved — and could lead to a serious loss of business.
In 2016, Salesforce.com went offline for more than 24 hours due to an outage at Salesforce’s North American 14 site (aka NA14). Clients could not access troves of customer data, from phone numbers to emails, and more — which heavily disrupted business.
Salesforce has since moved its IoT cloud to Amazon AWS, but the outage highlights a major problem with relying solely on the cloud. As of late 2018, only approximately 10% of data created in the enterprise was handled beyond the confines of centralized data centers. By 2025, some estimates suggest that up to 75% of enterprise data will be handled by edge computing nodes — a significant shift in how and where business data is processed and executed.
Relying less on the cloud also means certain devices can operate reliably offline. This is especially useful in locations where internet connectivity is limited — whether in specific geographies with little access or remote, often inaccessible sites like oil fields.
Another key advantage to edge computing relates to security and compliance. This is especially important with governments becoming increasingly concerned with how companies leverage consumer data.
Such is the case with the enforcement of the General Data Protection Regulation (GDPR) in the EU in 2018, which seeks to protect individuals’ personal identifiable information from data abuse.
Because edge devices collect and act upon data at a local level, data transfer to the cloud can be avoided. Sensitive information doesn’t need to pass through a network, and should a cyberattack to the cloud occur, the impact would likely be less dramatic than if the data was in flux.
Edge computing also allows a level of interoperability between emerging connected devices and older, “legacy” devices. It “converts the communication protocols” used by older systems into a language that modern connected devices can comprehend. This means legacy industrial equipment can be seamlessly and efficiently connected to modern IoT platforms.
Edge computing landscape
Today, the edge computing market is still relatively young. But it appears to be gaining more attention, especially as things become more connected.
Mentions of “edge computing”
The same players that have been dominant in cloud computing (Amazon, Google, Microsoft) are emerging as edge computing leaders. The significant financial resources and extensive proprietary network infrastructures of these companies ideally position them to capitalize on this significant shift in computing technologies and diversify their existing cloud service offerings.
Amazon was ahead of the initiative with its move into edge computing with AWS Greengrass in 2017. The service extends AWS to devices so they can “act locally on the data they generate, while still using the cloud for management, analytics, and durable storage.”
The e-commerce giant also offers its FreeRTOS service, which is “an open-source, real-time operating system for microcontrollers.” Microcontrollers are small processors found in everything from sensors to fitness trackers to appliances. With FreeRTOS, these edge devices can be programmed, connected, deployed, and managed.
Microsoft has also made some major moves in the space. The company’s plan to spend $5B in IoT by 2022 involves its edge computing initiatives.
Microsoft announced its Azure IoT Edge solution, which “extends cloud analytics to edge devices,” and can be utilized offline. The company is also looking to focus on artificial intelligence applications at the edge:
Earlier in 2020, the company announced its expansion with Nvidia’s T4 Tensor Core GPU to bring machine learning to the edge. Microsoft acquired IoT/OT security firm CyberX for approximately $165M in June 2020, an acquisition that will allow Microsoft to further develop the security protocols of Azure in industrial settings, such as increasingly automated factories.
Following suit, Google announced 2 new products in July 2018 to help improve development of connected devices at the edge: hardware chip Edge TPU and Cloud IoT Edge, a software stack. According to Google:
“Cloud IoT Edge extends Google Cloud’s powerful data processing and machine learning to billions of edge devices, such as robotic arms, wind turbines, and oil rigs, so they can act on the data from their sensors in real time and predict outcomes locally.”
In late 2020, Google announced it had joined forces with 200 application partners, including Equinix, Palo Alto Networks, and Siemens Advanta, to develop new 5G-enabled edge computing technologies with a diverse range of applications, from offering engaging in-store consumer experiences for retailers to equipment inspections for manufacturers.
Earlier in the year, the company partnered with AT&T to deliver 5G edge computing solutions for enterprises. But interest is not limited to these 3 giants.
As more connected devices emerge, many players within the rising ecosystem are working on software and technology that will enable edge computing to take off.
Hewlett Packard Enterprise (HPE) said in 2018 that it would invest $4B in edge computing over the next 4 years. HPE’s Edgeline Converged Edge Systems is targeted at industrial partners that desire data center-level computing power while often operating in remote conditions.
Other major players in the burgeoning edge computing space include Scale Computing, Vertiv, Huawei Technologies, Fujitsu, and Nokia, among others.
AI chip maker NVIDIA launched Jetson TX2 in 2017, an AI computing platform for edge devices. This followed the release of its predecessor Jetson TX1, and claimed to “[redefine] possibilities for extending advanced AI from the cloud to the edge.” In late 2020, Nvidia released the single-board 2GB Jetson Nano, which retails for just $59 — a demonstration of how accessible edge computing technologies have become for even casual hobbyists and a prime example of how quickly hardware costs are falling for such devices. Many prominent companies have also invested in edge computing, including General Electric, Intel, Dell, IBM, Cisco, Hewlett Packard Enterprise, Microsoft, SAP SE, and AT&T.
In the private market, for example, Dell and Intel have invested in Foghorn, an edge intelligence provider for industrial and commercial IoT applications. Both companies participated in Foghorn’s latest $25M Series C round in February 2020. Dell has also participated in a seed and Series A round to IIoT edge platform IOTech. In January 2021, Dell announced a partnership with virtualization specialist VMware and South Korea’s SK Telecom to develop the OneBox MEC, a turnkey hardware product that offers fully integrated 5G-enabled edge computing services for use cases in industries like construction, healthcare, and retail.
Many of the companies mentioned above, including Cisco, Dell, and Microsoft, came together to form the OpenFog Consortium. The group aimed to standardize applications of this technology. The OpenFog Consortium merged with the Industrial Internet Consortium in 2019, creating the world’s largest organization dedicated to the advancement of IoT, AI, fog, and edge computing.
Edge computing across industries
As the price of sensors and the cost of computing continues to decrease, more “things” will be connected to the internet.
And as more connected devices become available, edge computing will see increasing applications across industries, especially as cloud computing proves inefficient in some cases.
We are already beginning to see its implications across different sectors.
“When we take the power of the cloud down to the device – the edge – we provide the ability to respond, reason and act in real time and in areas with limited or no connectivity … it’s still early days, but we’re starting to see how these new capabilities can be applied towards solving critical world challenges.” – Kevin Scott, Microsoft CTO
From autonomous vehicles to agriculture, here are several sectors that would benefit from edge computing’s potential.
One of the most obvious potential applications of edge computing technology is across transportation — more specifically, autonomous vehicles.
Self-driving vehicles are heavily equipped with all types of sensors, from camera- to radar- to lidar-based systems, to help the vehicle operate.
As described earlier, these autonomous vehicles could utilize edge computing to process data much closer to the vehicle via these sensors, saving precious milliseconds.
And while driverless cars are not yet mainstream, companies are preparing. In 2018, The Automotive Edge Computing Consortium (AECC) announced that it would be launching operations focused on connected car solutions.
“Connected cars are rapidly expanding beyond luxury models and premium brands, to high-volume, mid-market models. The industry will soon reach a tipping point where the volume of vehicle data generated will overwhelm existing cloud, computing, and communications infrastructure resources.” – Kenichi Murata, president and chairman of the AECC
Members include DENSO Corporation, Toyota Motor Corporation, IBM Japan, AT&T, Ericsson, Intel, and others.
Meanwhile, in July 2019, BMW i Ventures and Toyota AI Ventures took part in a $25M Series A round for vision-oriented AI startup Recogni. The California-based company pairs AI with edge processing to support vision systems for autonomous vehicles.
In February 2021, California computer vision startup Recogni raised nearly $49M as part of its Series B round. BMW i Ventures and Toyota AI Ventures backed Recogni’s $25M Series A round in July 2019. Recogni says its vision recognition technologies are capable of classifying 92,105 images per second and identifying 833 separate individuals concurrently. The more images a vehicle’s sensors can process, the faster the autonomous vehicle can respond to unexpected obstacles — a scenario in which milliseconds may mean the difference between life and death.
Like many autonomous vehicle startups, Recogni is targeting “Level 2” or partially automated vehicles with its computer vision technologies. While there may not be a great deal of distinction between traditional and autonomous vehicles to the layperson, there are 5 levels of vehicular autonomy as defined by the Society of Automotive Engineers. Several vehicle manufacturers, including Cadillac and Volvo, currently offer Level 2 automation, among the most common of which is Advanced Driver Assistance Systems (ADAS).
Source: Society of Automotive Engineers
Despite significant advances in edge computing and other autonomous vehicle technologies, fully automated vehicles remain highly ambitious. Both Ford and GM are currently developing and trialing autonomous vehicles, but it will likely be several years before Level 4 and 5 vehicles enter mainstream commercial production for the consumer market.
But it’s not just autonomous vehicles that generate a significant amount of data and require real-time processing. It’s also planes, trains, and other forms of transportation — driverless or not.
Autonomous vehicles and other forms of intelligent transportation represent one of the fastest-growing potential applications of edge computing, which will drive investment in markets for related technologies, particularly computer vision systems and 5G network infrastructure. By 2026, the market for edge computing infrastructure to support autonomous vehicle systems alone is anticipated to exceed $39B.
While autonomous cars are expected to account for a significant amount of this growth, other intelligent transportation systems such as automated mass transit are likely to see substantial growth in the coming years. Edge computing technologies have significant potential to increase the efficiency of urban transportation networks, including buses, which can benefit from technologies such as real-time GPS and fleet monitoring, vehicle telematics, and contactless payment systems. Edge computing could also offer significant improvements in the efficiency and response times of emergency services vehicles and waste collection, as well as logistics route management and real-time traffic monitoring.
People have become increasingly comfortable wearing fitness trackers, glucose monitors, smartwatches, and other health-monitoring wearables.
But to truly capture the benefit of the massive amounts of data being collected, real-time analysis may be necessary — and while many wearable devices connect to the cloud directly, others can operate offline.
Some wearable health monitors can locally analyze pulse data or sleep patterns without connecting to the cloud. Doctors can then evaluate patients on the spot and provide on-demand feedback about their health. Experts say collecting data from smart wearable devices may prove highly useful in cases of a pandemic, where faster data processing near the source has the potential to be life saving.
GE, for example, uses NVIDIA’s chips in their medical devices to improve data processing at the edge, particularly for AI applications.
But the potential for edge computing in healthcare goes far beyond wearables.
Consider the benefits of speedy data processing for remote patient monitoring, inpatient care, and healthcare management for hospitals and clinics.
Doctors and clinicians would be able to offer faster, better care to patients while also adding an additional layer of security to the patient-generated health data (PDHD). The average hospital bed has upwards of 20 connected devices, generating a considerable amount of data. Instead of sending confidential data to the cloud where it could be improperly accessed, it would happen closer to the edge.
Edge computing can also help use big data, along with artificial intelligence and machine learning capabilities to predict patterns in the spread of deadly diseases. For example, US-based BlueDot is a geofencing software company that is using data from social media, text messages, and other online communications to predict the spread of the novel coronavirus (Covid-19).
As previously mentioned, localized data processing means a widespread cloud or network failure will not impact the process. Even if cloud operations were disrupted, these hospital sensors operate independently, and could still function as intended.
Several companies have already begun offering specialized edge computing tools to healthcare providers.
GE Healthcare’s Edison HealthLink is a full-stack solution that can be deployed on-site at hospitals and primary care environments to provide physicians with faster, smarter tools that integrate with electronic medical record systems and other healthcare IT infrastructure. The on-site nature of the solution HealthLink can save physicians valuable time when diagnosing critical, time-sensitive emergencies, such as strokes. The HIPAA-compliant solution can also be updated via firmware, which could offer healthcare systems significant cost savings by prolonging the shelf life of IT hardware.
AT&T recently partnered with the Department of Veterans Affairs (VA) to improve operations at the VA Puget Sound Health Care System in Washington State, which serves more than 112,000 veterans across 14 counties. AT&T’s system improves connectivity between mobile devices, allowing clinicians to track patient movements more accurately throughout the facility, as well as leverage AR/VR technologies in imaging and diagnostic applications. The system relies upon a locally installed 5G Distributed Antenna System, creating a micro-network on-site that spans numerous buildings at the facility.
The technology’s high bandwidth, low latency potential could also make remote surgical procedures significantly more viable by reducing the delay between physician input and robotic surgical implements considerably. AT&T hopes to deploy its MEC edge computing solution to VA facilities across the U.S. upon completion of the trial, which could benefit more than 9M veterans nationwide. Verizon is also developing 5G-enabled edge computing technologies at its 5G Lab in Cambridge, MA, that minimize latency between the surgeon and robotic operator.
Smart manufacturing stands to gain insight from the vast numbers of sensors that are employed in modern factories. (Read about how technology is transforming manufacturing here.)
The decreased latency issues of edge computing could lead to faster, more responsive changes in manufacturing workflow, which would be able to apply insight and action in real-time. This might include shutting down a machine before it overheats.
A factory could employ 2 robots, outfitted with sensors and connected to an edge device, to do the same task. The edge device could run a machine learning model to predict if one of the robots will fail (pictured at the right).
If that edge device determined that a robot’s failure was likely, it would trigger an action to stop it or slow it down. This would allow the factory to assess the potential for malfunctions in real-time.
Robots may also become more self-sufficient and reactive if they can process data themselves.
Edge computing should allow for greater, quicker insight generated from big data, and a greater amount of machine learning to be applied to operations.
The end goals are capitalizing on the untapped value of the massive amount of data being created, preventing safety hazards, and lessening disruptions on the factory floor.
One of the biggest challenges facing manufacturers is automating factories that may rely on hundreds, if not thousands, of disparate systems and processes, from programmable logic controllers (PLCs) that monitor assembly lines to sensors that conduct QA testing on individual components. Industrial edge computing aims to solve the unique challenges inherent to the industrial internet of things (IIoT) by serving as an operating system layer between the factory floor and edge nodes.
Deloitte and Verizon Business announced a collaborative initiative in late 2020 that aims to combine AI, 5G, multi-access edge computing, and advanced networking technologies and turn factories into “real-time enterprises.” Deloitte and Verizon’s smart factory technologies are currently being tested at Verizon’s Customer Technology Center in Richardson, TX, and focus on the automated prediction and identification of defects on production lines.
While interest in industrial edge computing is on the rise, adoption lags behind significantly. As of February 2021, only 27% of manufacturers had implemented edge computing in their facilities. However, more than half of manufacturers intend to embark on edge computing pilot programs within the next 2 years, and almost one in 5 intend to move beyond trial phases into full production during that time frame, suggesting the next several years will be pivotal in the IIoT.
AGRICULTURE AND SMART FARMS
Edge computing is ideal for agriculture, given the often remote locations and hostile conditions of farms that may present bandwidth and connectivity concerns.
Right now, smart farms wanting to improve connectivity are investing in expensive fiber, microwave connections, or having a full-time satellite; edge computing provides a suitable cost-effective alternative.
Smart farms could use sensors to monitor soil conditions, temperature, and weather conditions. Edge computing could then provide farmers with detailed insight quickly as data will be processed locally.
While broadband access, an essential for edge and cloud computing, in rural areas remains a challenge, one area where edge computing can be utilized efficiently is vertical farming, which is the process of growing food and plants in vertically-stacked layers. Since produce is grown in an artificially-controlled environment, local processing of data allows for maintenance of optimal conditions.
Edge computing could also help reduce food wastage, by reducing losses due to lack of infrastructure and faulty technology, among other things. According to the Food and Agriculture Organization, one-third of food produced for consumption is lost due to supply-chain inefficiencies.
Using yield-monitoring equipment fitted with sensors, robots for spraying and weeding and data analytics about soil conditions can lead to a better harvest. The World Economic Forum estimated in 2018 that if 50% to 75% supply chains in developed countries deployed IoT technologies by 2020, it would lead to food savings of 10M-50M tons.
Increasing demands for higher crop yields and greater efficiency are serving as catalysts in the emerging smart agricultural sector. The agricultural analytics sector, in particular — which helps farms improve crop yields, livestock health, soil conditions, and other factors through predictive data — is poised to experience strong growth over the next several years and is expected to reach a value of $2.27B by 2027, a CAGR of 17%.
Several farms across the US have already begun utilizing edge computing and networking technologies to transform their operations.
Hurst Greenery farm in Westboro, MO, aims to become one of America’s first “farms of the future” by implementing machine vision and other technologies as part of former FCC Chairman Ajit Pai’s Precision Agriculture Task Force. The farm relies on a private LTE wireless network that supports crop tracking and real-time temperature and humidity monitoring across 600 acres and 16 greenhouses. The program is being run by Trilogy Networks’ Rural Cloud Initiative (RCI), which hopes to provide similar technologies to the 2,300 farms across the RCI coverage area.
Swans Trail Farm in Snohomish, WA, has embarked on a similar initiative to increase efficiency as part of the Food Resiliency Project spearheaded by the University of Oregon. In partnership with Intel, Microsoft, T-Mobile, and other technology companies, Swans Trail Farm is utilizing 5G network technologies and real-time soil sensors to monitor and improve irrigation at the farm, as well as chemical and nutrient management to improve yields and lower costs.
ENERGY AND GRID CONTROL
Edge computing could prove especially effective across the energy industry, particularly for safety monitoring with oil and gas utilities.
Pressure and humidity sensors, for example, should be closely monitored, and cannot afford a lapse in connectivity, especially as most of these are located in remote areas. If something abnormal — such as an oil pipe overheating — happens and goes unnoticed, a disastrous explosion could occur.
Another benefit is the ability to detect equipment malfunctions in real-time. With grid control, sensors could monitor energy produced by everything from electric vehicles to wind farms to help make decisions around reducing cost and make energy generation more efficient.
Engineering conglomerate Bosch offers an edge-cloud platform that enables energy management at a residential level, while Tantalus Systems is developing smart grid systems that run on the edge to better automate load shifts for both commercial and residential customers.
Edge computing could also help in building fully autonomous energy plants and oil drills. Using sensors to monitor and adjust drilling equipment, the need to manually take care of operations is reduced, if not eliminated.
Energy companies may also find that edge computing can enhance surveillance and security.
Oil giant Saudi Aramco’s Energy Ventures arm and GE Ventures have both invested in IoT security startup Xage, which uses blockchain to distribute the authentication necessary to access edge entry points.
Companies can use drones or robots to generate real-time insights on the conditions of energy stations, even in remote areas. California-based DroneDeploy uses edge computing to generate real-time thermal maps that can signal issues such as equipment shutdown, solar panel overheating, and gas leaks.
For retailers shifting focus to autonomous store development, edge computing can be a vital component of their in-store technology. Retail is expected to become the fastest-growing economic sector in terms of edge computing deployments by 2022, suggesting significant potential for these technologies to improve the shopping experience for consumers and retailers alike.
In January 2018, Amazon opened its first Amazon Go store to the public. The store allows customers to swipe in with a QR code on their mobile app, at which point in-store cameras and sensors identify customers and register what they pick out to buy. These technologies are supported by edge computing, which makes monitoring easier and more scalable.
Amazon is not the only player to have launched a cashierless store. Retailer Ahold Delhaize is piloting a micro-fulfillment store in the Netherlands in partnership with self-checkout startup AiFi, which offers sensors and cameras to support the experience.
Retailers can also utilize edge computing to offer virtual reality shopping assistants in stores.
For example, augmented reality-enabled mirrors in fitting rooms can ensure customers are able to virtually “try-on” one item of clothing in different colors without actually having to bring out all the colors.
Retailers can also use beacons, a bluetooth-enabled technology, that allows for personalized recommendations to be sent to a customer when they enter a store, by quickly processing their online and in-store purchase history locally. Processing all this data through a centralized cloud would be more expensive and time-consuming.
This would also help backroom functions, such as stocking up on popular items at times of peak demand and processing supply chain operations without a lag — especially crucial in a pandemic. Sensors placed on store shelves can help take inventory decisions based on demand and reduce time taken in manually stocking up items. Quicker data processing can help ensure that time, and money, is not lost as a result of sending data to the cloud. Inventory discrepancies cost retailers $1.1T in lost sales every year, and the deployment of edge technologies to improve the efficiency of inventory management could be transformative for retailers’ bottom lines and customer satisfaction.
The consumer experience on retail shop floors is also undergoing radical changes thanks to edge computing technologies. In July 2019, AT&T partnered with commercial robotics specialist Badger Technologies to deploy autonomous robots in retail environments. These robots monitor the condition of retail stores in real-time, analyzing the inventory available on shelves, as well as potential hazards, such as spills. The robots communicate with edge nodes installed on-site via 5G private networks and MEC technologies, giving retailers unprecedented insights into how consumers behave in-store.
Edge computing also helps address privacy concerns in retail. Cashierless checkout tech startup Zippin reports that its cameras don’t use facial recognition, instead relying on edge computing to discern more general characteristics of shoppers.
Furthermore, data collected at the edge, including credit card details and identifiable-traits shared with stores, does not need to be stored in a central server and can be automatically forgotten by devices after processing.
While edge computing promises to revolutionize the shopping experience for consumers, retailers may see even greater benefits, driven primarily by advances in real-time and predictive behavioral analytics. Startups such as Advertima, which raised a $17.5M Series A round in 2020, are developing technologies that leverage AI and machine vision to give retailers even more insights into the decisions consumers make in retail environments.
One of the greatest challenges facing Advertima and similar startups, however, is balancing retailers’ desire for greater behavioral insights with consumer concerns about privacy and security. Advertima claims its machine vision technologies do not utilize biometric technologies that could be used to identify specific individuals, but striking this delicate balance will likely be an ongoing concern as technologies within the space mature and gain broader adoption.
Retailers are also leveraging edge computing to make shopping a safer experience amid the Covid-19 pandemic. AT&T’s CTO of Network Services, Andre Fuetsch, said at the Wells Fargo 5G Forum in June 2020, these technologies could help retailers comply with social distancing regulations via real-time feedback using cameras with computer vision technology.
Edge computing could pave the way for increased security and personalization across banking services.
As a large number of banking services have gone digital, financial institutions are using cloud computing capacities at a much larger level. Banks, therefore, need higher bandwidth and larger capacity to transfer and store the extensive amount of data that is collected.
This is where edge computing can shift the game. By moving data processing closer to the source, banks and other financial institutions can provide services in a more quick and secure manner.
In February 2019, for example, telco incumbents Telstra and Ericsson partnered with the Commonwealth Bank of Australia to explore use cases for 5G and edge computing in banking services.
Additionally, the tech may become more popular as financial services increasingly rely on biometrics. For example, edge computing makes it possible to capture — and forget — facial recognition data on a specific device, rather than having to send this back to a database filled with pictures of users.
One company already exploring this technology is Mastercard. The payment solutions giant recently patented an edge computing kiosk that could help speed up processing and protect user identity during banking transactions.
Other companies are also banking big on edge computing technologies. Spanish financial institution BBVA said it is the country’s first financial institution to implement a private 5G network at its headquarters and is also developing its own edge computing platform. BBVA believes this proprietary platform will enable it to offer consumers greater customization of specific financial products and detect fraud more effectively. The Commonwealth Bank of Australia also embarked on a similar venture in 2019 when it began working with Australian telecommunications company Telstra and Ericsson to implement 5G-enabled edge computing technologies across its branches.
Edge computing may also enable financial services to accelerate response time to critical information. Traders often need to make real-time decisions based on current events or market shifts, and edge computing could help traders, or trading algorithms, quickly analyze and react to large amounts of data produced by the markets.
Intel- and Samsung-backed edge infrastructure startup Pixeom offers a suite of edge services, including financial services solutions that help analyze global markets data.
Quicker computing near the source of the transaction also allows banks to experiment with services geared towards providing increased convenience to users. Poland’s Idea Bank has fitted cars with ATMs, which can be requested just like an Uber. Users can call for the ATM car through an app, and can withdraw money or make deposits.
From wearables to vehicles to robots, IoT devices are gaining momentum.
As we move towards a more connected ecosystem, data generation will continue to skyrocket, especially as 5G technology takes off and enables faster connections.
While a centralized cloud or data center has traditionally been the go-to for data management, processing, and storage, each has its limitations.
Edge computing can provide an alternative solution, but since the technology is still in its infancy, it’s difficult to predict its success moving forward.
Challenges around device capabilities — including the ability to develop software and hardware that can handle computational offloading from the cloud — are likely to arise. Being able to teach machines to toggle between a computation that can be performed at the edge and one that requires the cloud is also a challenge.
Even so, as adoption picks up, there will be more opportunities for companies to test and deploy this technology across sectors.
And while some use cases may prove the value of edge computing more clearly than others, the potential impact on our connected ecosystem as a whole could be game-changing.