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Worm on the sensor: What happens when IoT data is bad?

Stephen Lawson | Dec. 19, 2016
Dirty devices at the edge sometimes send messy data to the cloud

More is better

But many IoT sensors are too hard to reach for regular calibration and maintenance. In those cases, redundancy may be the answer, though it’s not a silver bullet.

Duplicates of the same sensor on a machine, in a mine, or in a field generate more inputs, which can be helpful in itself. Weather Underground, part of IBM’s Weather Company business, creates its reports partly with data from uncalibrated, low-cost sensors in consumers’ back yards. For not much money, they give Weather Underground more data points, but quality is a big issue. One sensor may malfunction and report several inches of rain while the one next to it senses none, said John Cohn, the IBM Fellow for Watson IoT.

“The great thing is, if you have enough density of these kinds of sensors, you can ... mathematically find the outliers and reason, from that, that one requires work,” Cohn said.

Companies can also use different sensing devices, especially cameras, to check on sensors that may be having trouble. A video camera combined with image analysis software can detect whether a remote device has gotten dirty, damaged or vandalized, said Doug Bellin, senior manager of global private sector industries at Cisco Systems. Sometimes security cameras already there for something else can do this job.

One technique for verifying different kinds of sensors against each other is called sensor fusion. It weighs inputs from two or more sensors to come to a conclusion.

Sensor fusion is now being implemented in hospitals, where false alarms are rampant, said Stan Schneider, president and CEO of IoT software company Real-Time Innovations (RTI). For example, rather than setting off an alarm every time the blood oxygen sensor on a patient’s finger showed low oxygen, a sensor fusion system would constantly compare that reading with those from other sensors on the patient, like respiration and heart rate monitors.

The phantom sensor

Other sources can also stand in for a sensor that isn’t even there anymore. GE tests each jet engine that comes out of its factories for exhaust gas temperature, a figure that reflects its efficiency, Kodesh said. GE puts one sensor right in the path of the exhaust even though it will always burn up after a few minutes. Meanwhile, sensors in safer spots around the engine collect data at the same time, and by comparing their readings with what the doomed device recorded before it was destroyed, GE can recreate the direct sensor as a virtual one – a mathematical function.

Drawing conclusions from multiple information streams takes the data-quality problem into the realm of machine learning. That’s where the most interesting stuff is happening, IBM’s Cohn says.


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