Edge Computing: A Better Alternative Than Cloud for IoT in 2024
Have you ever wondered whether it wouldn’t have been better if there was a coffee shop in your apartment building? There is one two blocks away from your house. But by the time a cup is delivered to you, it has lost some of its heat.
That is an analogy for cloud computing.
All IoT devices in your surrounding receive and transmit your data onto the cloud, either for storage or for processing. But currently, most cloud computing services are administered by a handful of providers: Amazon, Microsoft, Google, and IBM. The complication is that there are billions of devices, but only a couple of cloud services. This will cause databases to get overworked and experience latency in the processing of the given tasks.
In this article, we will go through the reasons why the use of edge computing for IoT devices can be a better alternative than the cloud, and showcase some examples of IoT devices using edge computing for storage and the processing of the data.
What is edge computing?
The word edge in computing is the processing of data not in a location “far away” on the cloud, but rather at the edge, or near the source, of the data. We could think of it as the cloud off-shoring its functionality to areas near the device.
In our coffee shop analogy, edge computing would be the equivalent of having a coffee shop next to each apartment building, delivering higher quality drinks, in a shorter interval.
It should be noted that the cloud will not be made entirely redundant, per se, but rather that, either the edge will be powerful enough to carry out the processing, or it at least will be able to send a compressed and processed file, instead of raw data.
What are benefits of edge computing?
The following are the benefits of edge computing:
- Lower latency: Voice assistants usually rely on the cloud to provide you with information. Siri, for instance, does not have access to a database located on your phone, but rather gets all its answers from Apple’s cloud databases. The round-trip from your device to the cloud and back — not mentioning the time it takes for the device to process your speech and for the cloud to unpack what’s been said — can be noticeable and cause latency. If; however, the cloud delegates its functionality on the edge of the device, then the latency will be noticeably less.
- Privacy and security: Your iPhone, for instance, encrypts and stores your biometrics on the edge of the device, rather than storing it on the cloud. That’s why even if hackers gain access to Apple’s database, they will not be able to access your biometrics, simply because they are not there. Edge computing mitigates security risks by decentralizing data storage.
- Bandwidth: Multiple devices transmitting information simultaneously onto the cloud for processing and storage puts strain on internet bandwidth. IoT devices that take advantage of the edge have the capability of processing and filtering the raw data at the source, and they then transmitting it onto the cloud, if need be. The sending of only compressed files will reduce bandwidth consumption and save money.
- Scalable: If a company increases its IoT devices, it will have to purchase more storage capacity on the cloud for each additional unit. In edge computing; however, because most of the processing of data happens at the edge, the company will pay less, if no cost at all, for the storage of the compressed and processed data on the cloud.
Advice for IT leaders: You can reduce your IoT ecosystem latency, privacy, and bandwidth issues and increase your scalability by using edge computing.
What are the challenges of edge computing?
The following are the challenges of edge computing:
- Maintenance: A benefit of cloud computing is that the service providers themselves are responsible for maintaining the functionality of the cloud. But with edge computing, on the other hand, the responsibility for monitoring, maintaining, and troubleshooting of the edge lies with the user.
- Solution: Intel filed a patent which involves a design tool composed of multiple chipsets that are able to identify the software and hardware resources needed to troubleshoot certain issues in order to reduce the human effort.
- Monitoring: Within the cloud, the health data of all devices can be stored there. With edge computing; however, especially with heterogeneous devices running different applications, monitoring their health and integrity can be difficult.
- Intelligent software that holistically manages highly distributed heterogeneous environments, accomplished by collecting, unifying and processing data from a wide range of sources.
Advice for IT leaders: You can mitigate these issues by investing in appropriate software tools that can maintain and monitor the edge of your IoT devices.
What are the use cases of edge computing in IoT?
The following are examples of IoT devices using edge computing:
Driverless cars are the quintessential example of edge computing. Due to latency, privacy, and bandwidth issues, it is time-consuming for IoT sensors in the car to, say, identify a kid on the road, send a processed image to the cloud, and then be commanded to engage the brakes. The kid may be overrun by then. But edge computing allows for all those steps to be done centrally, more reliably, and quickly.
IoT in healthcare has made for round-the-clock monitoring and reporting of patients’ vitals. Smart wearables, for instance, can monitor the heart rate of patients and share it with physicists via the cloud. But that poses its security issues, since if the cloud gets hacked, then the patients’ data might get exploited.
The use of edge computing at the site of the hospital; however, would make for the processing of sensitive material at the source. Less transfer of sensitive data between the devices and the cloud means improved security for the hospital and the patients alike.
Online gaming is made possible through the sharing of the content with the gamers from the cloud. During rush hours; however, players might experience latency as the servers and the cloud get overwhelmed. Gaming companies, to solve the issue, are looking to implant edge servers either inside the consoles or near them to reduce the distance data has to travel through.
For more on IoT
- If you are interested in learning about how IoT is being used in manufacturing, click here.
- If you want to learn about how IoT is making agriculture smarter, click here.
- And if you want to learn about the building blocks of IoT architecture, click here.
If you believe your business will benefit from an IoT edge solution, feel free to check out our data-driven list of IoT edge providers.
And if you believe your business would benefit from an IoT solution, head over to our data-driven hub of IoT solutions and tools.
We will help you choose the best vendors for your business:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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