March 2021

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The Impacts of New Sensor and Data Fusion Technologies

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Networked sensors and sensor data fusion are driving new Smart System innovations enabling a whole new generation of applications that are self-sensing, self-controlling, self-optimizing and “self-aware”—automatically, without human intervention. As networks continue to invade the “physical” world, solution designers are seeing the new values that come from the growing interactions between sensors, machines, systems and people.

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Do you understand how fast the world is changing? One indication of the speed is the fact that we’re drowning in unprocessed information. We’re creating data at 2X the rate we’re deploying traditional bandwidth to carry it, but almost all the data created to date has never been analyzed. More than half the data created by physical or operational systems loses any value that could be derived through analysis in less than a single second.

Yet according to the National Science Foundation, there will soon be trillions of sensors on the earth. And forecasters predict that in just a few more years there will be more processing power in smart phones than in all the servers and storage devices in data centers on the earth today. Ready or not, we’re rushing into the future of truly distributed systems and intelligence.

Revolutions always begin by sensing small things and drawing inferences. For example, there is great value in knowing how people use “white goods” like home appliances. If you embedded a microprocessor in the plastic of an electrical outlet, you’d have true local processing as opposed to processing in a remote cloud. From there, you could infer almost anything by sensing the electrical current “signature” and its usage profile—not just energy used, and whether a washing machine’s motor is about to fail, but the fact that the consumer just washed a load of whites versus a load of colored clothes. If you had an inkjet printer plugged into that same outlet, you could know whether the consumer was printing colored pages versus black-and-white.

Analysis of data from a “sweat patch” for measuring human perspiration can reveal the emotional state of an athlete wearing it, as well as the level of physical stress she’s under. If a worker is wearing the patch, you can see if they’re being exposed to the many dangerous substances that exist in factories, farms, and other workplaces.

Weather is another complex phenomenon that can be greatly understood by correlating data values collected from simple pressure, temperature, and moisture sensors with additional spatial and temporal parameters that place the data into a richer context. I could be running a fleet based on weather forecasts, but if I add the data from sensor packs mounted directly on my vehicles, I’m less likely to be impacted by unexpected weather conditions. The closer that systems are to real-time, the more efficient and cost effective they can be. All such data, with its related context, has extraordinary value to everyone.


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