Internet of Things (IoT) devices are flooding the world. In fact, studies show that we can expect over 75 billion IoT devices to be active by 2025.

From smart voice assistants to in-store beacons, brands are experimenting with touch points in a bid to improve the customer experience and collect data in new and inventive ways.

The only problem is the massive influx of data being collected from each device. How (and where) can such vasts amounts of data be processed?

Edge computing and fog computing are two potential solutions, but what are these two technologies, and what are the differences between the two? We’ve asked industry experts for insight.

Related Article: Edge Computing: What It is and How It's a Game-Changer

What is the Difference Between Edge Computing and Fog Computing?

Both fog computing and edge computing provide the same functionalities in terms of pushing both data and intelligence to analytic platforms that are situated either on, or close to where the data originated from, whether that’s screens, speakers, motors, pumps or sensors.

“Fog computing and edge computing are effectively the same thing. Both are concerned with leveraging the computing capabilities within a local network to carry out computation tasks that would ordinarily have been carried out in the cloud,” said Jessica Califano, head of marketing and communications at Temboo.

Both technologies can help organizations reduce their reliance on cloud-based platforms to analyze data, which often leads to latency issues, and instead be able to make data-driven decisions faster. The main difference between edge computing and fog computing comes down to where the processing of that data takes place.

“Edge computing usually occurs directly on the devices to which the sensors are attached or a gateway device that is physically “close” to the sensors. Fog computing moves the edge computing activities to processors that are connected to the LAN or into the LAN hardware itself so they may be physically more distant from the sensors and actuators.” said Paul Butterworth, co-founder and CTO at Vantiq.

So, with Fog computing, the data is processed within a fog node or IoT gateway which is situated within the LAN. As for edge computing, the data is processed on the device or sensor itself without being transferred anywhere.  

What Are The Pros and Cons of Edge Computing?


According to Kyle Bernhardy, CTO at HarperDB, one major benefit to edge computing is that data isn’t transferred, and is more secure. “Edge computing maintains all data and processing on the device that initially created it. This keeps the data discrete and contained within the source of truth, the originating device,” he explained.

Jason Anderson, VP of Business Line Management at Stratus Technologies, also highlighted how edge computing can help to keep costs low:

“Edge computing technology saves time and resources in the maintenance of operations by collecting and analyzing data in real-time. Networks on the edge provide near-real-time analytics that helps to optimize performance and increase uptime,” Anderson said.


However, the one significant drawback to edge computing, as Chris Nelson, VP of Engineering at OSIsoft pointed out, is striking the balance between keeping data at the edge and bringing it into a central cloud when necessary.

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“Companies may struggle to understand the balance between bringing data to the cloud vs. processing it at the edge. In terms of cost, sometimes it’s more effective to analyze data locally, however, in some cases the data may need to go to the cloud,” Nelson said.

It can become a complex issue for brands to handle, as data sets that require more sophisticated algorithms are better handled in the cloud, whereas simpler analytical processes are best kept at the edge.

Related Article: Cloud Computing Takes a Back Seat to ... Edge Computing. Or Is It Fog?

What Are The Pros and Cons of Fog Computing?


With data storage and processing taking place in LAN in a fog computing architecture, it enables organizations to, “aggregate data from multi-devices into regional stores,” said Bernhardy. That’s in contrast to collecting data from a single touch point or device, or a single set of devices that are connected to the cloud.  

Furthermore, as fog computing enables firms to collect data from various different devices, it also has a larger capacity to process more data than edge computing. “Fog is able to handle more data at once and actually improves upon edge’s capabilities through its ability to process real-time requests. The best time to implement fog computing is when you have millions of connected devices sharing data back and forth,” explained Anderson.


While Bernhardy acknowledges fog computing’s advantage of being able to connect with more devices and hence process more data than edge computing, he also identified that this dimension of fog computing is also a potential drawback.

“More infrastructure [and thus more investment] is needed [for fog computing] and you are relying on data consistency across a large network,” he said.

What’s your take on edge computing vs. fog computing? Is one significantly more useful or efficient? Share your views in the comments below.

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