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 2025, an estimated 463 exabytes worth of data will be created per day globally — equivalent to over 200 million DVDs per 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. The technology promises faster processing at lower costs right at the source of the data.
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.
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 $1.1T by 2026, growing nearly five-fold from its 2018 size of $190B. 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.
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.
Because of its rising popularity, cloud computing has attracted major tech players like Amazon, Google, Microsoft, and IBM. Of the major public cloud providers, Amazon Web Services (AWS) and Microsoft Azure take the No.1 and No. 2 spots, respectively, according to a 2019 survey conducted by private cloud management company RightScale.
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.
The consolidation and centralized nature of cloud computing has proven cost-effective and flexible over recent years, but the rise of IoT and mobile computing has put a strain on networking bandwidth.
Ultimately, not all smart devices need to utilize cloud computing to operate. In some cases, the back and forth can — and should — be avoided.
That’s where edge computing comes in.
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 $700B by 2028, 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. Source: HPE
It 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.
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, he 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.
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 then 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.
The same players that have been dominant in cloud computing (Amazon, Google, Microsoft) are emerging as edge computing leaders.
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.
Following suit, Google announced two 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.”
Earlier in 2020, the company partnered with AT&T to deliver 5G edge computing solutions for enterprises.
But interest is not limited to these three 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 four 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.
The Edgeline Converged Edge Systems (Model EL 1000 pictured left) promises to provide industrial operations — such as across oil rigs, factories, or copper mines — with insight from connected devices without relying on sending the data to the cloud or data center.
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 follows the release of its predecessor Jetson TX1, and claims to “[redefine] possibilities for extending advanced AI from the cloud to the edge.”
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.
Many of the companies mentioned above, including Cisco, Dell, and Microsoft, have also come together to form the OpenFog Consortium. The group aims to standardize applications of this technology.
Edge computing across industries
As the price of sensors and 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 cars 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
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.
For example, the airplane manufacturer Bombardier’s C-Series has been heavily outfitted with sensors to immediately detect engine performance problems. Over 12 hours of flying, the airplane generates 844 TB of data. Edge computing allows real-time processing of the data, so the company can proactively deal with engine issues.
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.
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 two 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.
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%-75% supply chains in developed countries deployed Internet of Things technologies by 2020, it would lead to food savings of 10M-50M tons.
Energy & 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.
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.
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.
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.
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.
This report was created with data from CB Insights’ emerging technology insights platform, which offers clarity into emerging tech and new business strategies through tools like:
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