With the forecast of over 30 billion IoT devices being deployed globally by 2020, the amount of data stored in the cloud is hard to imagine. Not to mention the processing power needed to derive any tangible value from it.
No wonder business owners are increasingly looking to improve the performance and reduce operational costs of their IoT products. One of the ways to do so is by handling the data outside of the main cloud or at its “edge”.
So, if you are planning to build an IoT app for your business or want to optimize your current products, edge computing might be a safe bet for you. To learn more about the advantages of edge computing and its real-life use cases, read on.
With 83% of enterprise workloads being handled in the cloud (by 2020), according to the LogicMonitor survey, cloud adoption is soaring. And there are some good reasons for that: reduced operational costs, better scalability, faster app deployment, increased reliability, etc.
Yet, when it comes to IoT, traditional cloud computing has a number of drawbacks. Here are three of them:
- Data security threats. Data is constantly being transmitted back and forth between the cloud and a device, and as such, the risk of privacy violation is heightened.
- Performance issues. IoT applications rely heavily on real-time actions. Yet, the processing speed of your cloud-based app often depends on the actual distance between the device itself and the server location.
- Operational costs coincidentally grow as the amount of data produced and shared increases.
On top of that, most data sourced to the cloud often bears no practical value and is never used. So, why waste your organization’s resources and cloud storage space on irrelevant and useless data when you can handle and keep it right where it’s generated – on the device?
This is why more businesses are facing the edge computing vs cloud computing dilemma.
So, let’s get straight to the point and find out how edge computing helps IoT, and why it’s better than the cloud.
Let’s start with the definition of edge computing.
The term refers to the computation of data that happens right where the data is produced, i.e. “at the edge” of the IoT network.
So, instead of having a centralized, remote cloud to do all the work, the data is handled and stored locally, i.e. on the IoT device itself or at the nearest network node.
But how does edge computing work?
To explain how it works in real life, we can take any smart device out there as an example.
Every IoT sensor produces tons of data every second. In the case of cloud computing, the data is instantly transferred to the central, unified cloud database where it’s processed and stored.
If there’s any action required, the central server will send its response back to the device upon receiving and analyzing the acquired data.
While the whole process typically takes less than a second to complete, there might be situations when the response may be delayed or interrupted. This can happen due to a network glitch, weak internet connection, or simply because the data center is located too far from the device.
Now, in case of edge computing, you don’t need to send the data acquired by the IoT sensors anywhere. The device itself or the nearest network node (e.g. the router) is responsible for data processing and can respond in a proper manner if action is required.
As a result, the IoT device is no longer dependent on the internet connection and can function as a standalone network node.
As you can see, the main purpose of edge computing is to decentralize data handling. This leads to a number of advantages over the traditional cloud.
Namely, there are 5 main advantages of edge computing for IoT:
1. Increased data security
While IoT solutions represent a perfect target for cyber attacks, edge computing can help you secure your networks and improve overall data privacy.
Because the data is decentralized, distributed among the devices where it is produced, it’s difficult to take down the whole network or compromise all of the data with a single attack.
This approach is also preferred in terms of GDPR compliance: the less sensitive information is sent through your network and stored in your cloud, the better.
2. Better app performance
As mentioned above, it takes some time for the data travel back and forth between the device and the data center.
By storing and processing the data close to its source, you reduce the lag time and improve the overall app performance. As a result, you can analyze the data in real-time, without delays.
3. Reduced operational costs
When you store and process most of the data “at the edge”, you don’t need an abundance of cloud storage. Plus, you can filter out the unnecessary information and backup only the relevant data.
As a result, your infrastructure costs will inevitably go down.
4. Improved business efficiency and reliability
Lower data traffic and reduced cloud storage, in turn, lead to more efficient business operations.
Additionally, connection issues won’t be extremely problematic as they are for other IoT products that rely on the cloud. This is due to the fact that your devices can work autonomously, without internet connection.
5. Unlimited scalability
Unlike cloud, edge computing allows you to scale your IoT network as needed, without reference to the available storage (or its costs).
As a result of the listed benefits, edge computing really shines when it comes to time-sensitive tasks.
Namely, McKinsey finds that the industries with the most edge computing use cases are
- travel, transportation, and logistics
Image source: mckinsey.com
Here are 3 practical IoT edge computing examples to demonstrate how it can be used across the listed industries:
- Autonomous vehicles
Self-driving cars represent one of the important IoT edge computing use cases.
A moving vehicle simply cannot rely on a remote server to decide if it needs to stop when there’s a pedestrian crossing the road in front of it. The decision needs to be made immediately. The data has to be processed on the spot, regardless of the internet connection.
Plus, vehicles (while on the road) can communicate with each other more efficiently because they don’t need to send data about accidents, weather conditions, traffic or detours to the remote server first.
- Healthcare devices
One more practical case for edge computing lies within the domain of health monitors and other wearable devices. When used in telemedicine for keeping track of the patient’s chronic conditions, they can become real life-savers.
For example, a heart rate monitor capable of analyzing health data independently, can instantly provide the necessary response to alert caregivers when a patient needs their help.
Robot-assisted surgery is another use case for edge computing in healthcare, especially when every nanosecond can mean the difference between life and death. These robots need to be able to analyze data on their own in order to provide assistance in surgery safely, quickly and accurately.
- Security solutions
Any security system should be able to respond to security threats within seconds. That is why it makes sense to use edge computing for surveillance systems.
As a result, with on-device video processing, cameras can detect motion, identify trespassers, and instantly alert users in case of trespassing or suspicious activity.
So, instead of transferring tons of raw data to the servers for processing, such cameras save your internet traffic, reduce the bandwidth and cloud storage while improving the speed and the accuracy of a response.
In addition to the listed examples, there are more use cases for edge computing:
- Traffic lights or wind turbines don’t need to communicate with the cloud 24/7 (or it also happens that sometimes they can’t connect to the server at all) and can be completely automated.
- Streetlights can create a self-sustaining, autonomous system by communicating directly with each other instead of being mediated by the remote cloud.
- Smart sensors in agriculture don’t need to turn to the central server to decide when they need to water the plants nearby or add fertilizers. They can easily perform the routine tasks on their own and sync with the main cloud once in a while.
And this list can go on.
By 2022, 75% of enterprise data will be processed outside of the cloud (as well as traditional data centers), according to Gartner. As a result, the size of the edge computing market will surpass $13 billion worldwide within the same timeframe.
Taking into account the existing trend, IoT developers and business owners should seriously consider edge computing for their upcoming products. Otherwise, they will be left behind once the competitors take full advantage of this technology.
So, if you are planning to invest in IoT development or want to improve your current products, start by finding a reliable technology partner to help you navigate the pitfalls along the way.
Our team at Eastern Peak has a solid IoT development portfolio and can help you develop an optimal transition strategy for your existing products or build a new one from scratch.
Contact us now and receive free expert advice on how to get started with your edge computing strategy.