October 21, 2019 12:14 CET

Edge Computing & IoT: The Impact

Edge computing will change everything

By 2020, 83% of enterprise workloads will be handled in the cloud. Adopting the cloud can mean reduced operational costs, better scalability, faster app deployment, and increased reliability.  But here’s the rub: with a predicted 30 billion plus IoT devices being globally deployed by 2020, the amount of data that will be stored in the cloud is mind-boggling, as is the ability to derive tangible value from it. Additionally, when it comes to IoT, data constantly being sent from a device to the cloud means an increased risk of it being compromised.

Handling data outside the main cloud – at its ‘edge’ – can reduce data center loads and operational costs while improving performance and increasing security. Welcome to Edge Computing.

What is Edge Computing?

In the simplest terms, edge computing is the processing and analyzing of data at the network edge, closest to the point of action. This means it is an on-device approach to data, so instead of sending endless streams of data to the cloud (where much of it bears no practical value and is never used), the data is handled and stored locally, either on the IoT device itself or at the nearest network node.

In other words, instead of having a centralized, remote cloud doing all the heavy lifting, the data is handled and stored locally.

This on-device approach reduces latency for critical applications while lowering dependence on the cloud and allowing you to better manage the huge amounts of data generated by your IoT solution.

How Does It Work?

IoT sensors produce enormous amounts of data and if you’re using cloud computing, the data is immediately transferred there to be processed and stored. If action is required, the cloud will analyze the data it has received before sending its response back to the device. This normally takes less than a second to happen, but there is always the possibility of a delay or an interruption, which can be an enormous problem when we’re talking about things like autonomous vehicles, where even waiting a millisecond for a response would be unacceptable.

With edge computing, you don’t need to send data from the sensors anywhere. The device itself or the nearest network node is responsible for processing the data and can respond immediately if action is required. The result is that your IoT device is no longer dependent on an Internet connection and can instead function as a standalone network node.

The Benefits

The main purpose of edge computing is to decentralize data handling, and this on-device approach leads to a number of advantages over traditional cloud computing.

1. Data Security

Data being continuously sent from the device to the cloud is at a big risk of being compromised. With edge computing, data is decentralized and distributed among the devices where it is produced, making it difficult to take down the whole network or compromise all of your data with a single attack.  This also helps in terms of GDPR compliance, because sensitive or private information is no longer being sent to and stored in the cloud, where it could be vulnerable.

2. Speed

Time is of the essence when it comes to many IoT applications and sending data back and forth between the device and the data center comes with an inevitable time lag.  By storing and processing data at or close to the source, you reduce this lag and can analyze and take action on data in near-realtime, without unnecessary delays.

3. Scalability

Storing and processing data at the edge means you don’t need a substantial amount of cloud storage, allowing you to scale your IoT network as needed. You can expand your computing capacity through a combination of IoT devices and edge data centers.

4. Improved Efficiency

IoT devices can gather unprecedented amounts of actionable data with edge computing, because instead of waiting for a device to log in and interact with centralized servers, edge computing devices are always connected and therefore always gathering data for future analysis. The data can be processed locally to deliver a fast response, or it can be delivered centrally, where it can be dissected to identify trends and notable data points. This, in turn, can help your business make better decisions and meet the needs of your customers more efficiently.

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