Nov 12 2018

Fog Computing Promises Faster Data Analytics for Internet of Things Devices

A new innovation that allows computation to flow anywhere could drop the time it takes to access IoT data to less than a millisecond.

If the Internet of Things has a hidden superpower, it’s data. But the logistics of capturing and processing that data to yield actionable insights can be easier said than done. Fog computing promises to speed up this process by extending the cloud closer to the point where data originates: the IoT devices on the network. 

Sending data back and forth to the cloud for analysis can be cumbersome: Data consumes bandwidth, processing is slower, and security concerns increase. But fog nodes can be deployed anywhere that a network connection exists. The nodes closest to the network edge can take in data from IoT devices and direct different types of data to the optimal site for analysis.

20.4 billion

The estimated number of connected devices in the Internet of Things in 2020

Source: Gartner, “Gartner Says 8.4 Billion Connected ‘Things’ Will Be in Use in 2017, Up 31 Percent from 2016,” February 2017

“Fog nodes are often at an ideal level of the network — between endpoint sensors, actuators and the cloud — to serve as critical storage resources for the Internet of Things,” says Helder Antunes, chairman of the OpenFog Consortium and a senior director at Cisco, which is credited with coining the term fog computing. “A fog node the size of a shoebox — located on a factory floor, in a vehicle or as part of a smart building — can host tens of terabytes of reliable, high-performance, solid storage, reachable by endpoint things in less than a millisecond.”

Cloud access for that data, by contrast, would take about 100 milliseconds, he says. 

Storage on fog nodes, rather than in the cloud, also supports redundancies and reduces the chance of data loss, says Antunes: “Using a distributed, hierarchical storage architecture including one or more levels of fog-based storage can improve the performance, security, reliability, efficiency and cost of mission-critical IoT networks.” 

MORE FROM EDTECH: Check out how universities are using the internet of things to integrate smart campus tech!

Purdue University Begins Experimenting with Fog Computing

Mung Chiang, dean of the College of Engineering at Purdue University, co-founded the OpenFog Consortium in part to facilitate the adoption of fog computing. 

One of the consortium’s major initiatives has been creating the OpenFog Reference Architecture for fog computing, which the IEEE Standards Association officially adopted in June. The architecture is based on eight core technical principles: security, scalability, openness, autonomy, RAS (reliability, availability and serviceability), agility, hierarchy and programmability.

Much like the IoT itself, fog computing will take at least a decade to mature, Chiang predicts: “It will continue to be a journey and an evolution.”

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