For example, IBM uses its Watson analytics platform to understand energy use at IBM facilities in Ireland. Not only can Watson flag a discrepancy if an air-conditioner says it’s off but the total power draw is too high for that to be true, but over time it can learn to identify the particular way in which that air-conditioner draws power when it comes on. With that knowledge, a system that says it’s not on can be caught red-handed.
As a check on faulty data, machine learning does take time to get up to speed, unlike added sensors or cameras.
“It gets smarter the more it runs. The first time it runs, I wouldn’t trust it,” Cisco’s Bellin said. “The thousandth time it runs, it’s ... probably smarter than I am.”
The more critical the IoT system is, the more important is is to deal with bad data. Sensor fusion, for example, is necessary for things like patient health and missile detection because reliability is a big issue when the stakes are that high, RTI’s Schneider said.
But some forms of IoT can probably get by without it multiple sources of data, he said. “You don’t need that in the thermostat in your house.”
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