The networked smart environment, also known as the Internet of Things, with its connected fridges, thermostats, and so forth, will one day be ubiquitous.
One thing that’s almost universal in our understanding of how the tech will work is that the IoT device will connect to the Internet somehow. The device will form the network connectivity, not the user.
The human that’s operating the device isn’t part of the network. In fact, without any authentication of the user, the IoT doesn’t know who has used the smart device.
It’s unfortunate, because that’s useful data. You could get to find out which family member has changed the temperature on the thermostat, for example.
But, with the billions of IoT devices expected to be thrown at us, are we really going to want to be involved in an authentication process with every self-watering plant pot that we interact with? Probably not.
However, there may be a solution, and that’s to track the user through wearable technology that senses the unique electromagnetic radiation that emanates from particular devices.
Scientists at the University of Washington have been experimenting with wearable tech that uses unique electromagnetic radiation signatures “generated by electrical components or motors” in non-IoT devices in this case, “to pinpoint when its wearer flicks a light switch, turns on a stove or even boards a train.”
You find out “which person in the house actually flicked the switch,” according to an article about the study on the University of Washington’s website.
The scientists have been concentrating on normal, current appliances, but in the case of extending the idea to IoT, Big Data could become multi-directional.
Big Data is collected by the device and also by the user. Put both streams together and you could theoretically calculate the person’s detailed interaction with devices and appliances.
Other potential uses are tracking the individual’s overall carbon footprint over a period, which is one of the uses the Washington researchers suggested.
“It’s another way to log what you’re interacting with, so at the end of the day or month you can see how much energy you used,” saidShwetak Patel a professor involved in the project, in the article.
In a day-long test in which a user read on a laptop, cooked dinner, and took a bus ride, among other activities, the system correctly identified 25 out of 29 interactions with various devices and vehicles, the scientists say.
Vocal chord pattern
The system works because devices have individual characteristics, the scientists say.
“When a blender turns on, for instance, modulators change the current profile of the device and create something similar to a vocal cord pattern,” Edward Wang said in the article.
Wrist-mounted off-the shelf sensors are used to capture the frequency range and identify the unique device.
“We think it could be integrated into any wrist-sized product,” Patel said in the article. “The next steps are really to look at what other devices we can detect and work on a prototype that’s wearable.”
Tying it in with future IoT would clearly be the next step.