Modern technology collects vast amounts of data from sensors, with one estimate placing global data from Internet of Things devices at about 73 zettabytes (or 73 trillion gigabytes) in 2025. And as more data are collected, the infrastructure required to store, transfer, and run compute on that data also grows.
But what if, instead of collecting all possible data from a sensor, we could be more selective, collecting just enough data to accurately identify whatever we’re looking for? That’s the approach proposed by researchers at Pennsylvania State University and MIT. Their paper, recently published in Nature Scientific Reports, demonstrates how a neural network can achieve an accuracy of more than 90 percent while sampling as little as 10 percent of the original sensor data.
“The way I see it, edge computing is going to take a different direction because of what we did—or not just edge, but also edge used alongside cloud computing,” says Soundar Kumara, an industrial engineering professor at Penn State and co-author on the paper.
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