Researchers at Rensselaer Polytechnic Institute (RPI) are building tools and developing a framework that developers can use to easily perform data analytics over a multitude of devices.
Rather than having developers design custom algorithms for each network of devices, the framework of software RPI has in mind sits on all the network devices. The cloud will automatically manage communication between the devices and deal with device and network failures. Said lead researcher Stacy Patterson, RPI Clare Boothe Luce Assistant Professor of Computer Science, “Now the developer only needs to provide a little bit of code to say ‘this is how I want it to work,’ and this framework will take care of the rest.”
The project, “Toward a Machine Learning Framework for the Internet of Things,” is an extension of Patterson’s current research into enhancing the utility of sensors embedded in automobiles, by creating real-time networks that allow automobiles to pool their individual information into a larger shared picture of driving conditions in the area. Current approaches for Big Data analytics require full data transfer to a platform with large computational power, such as the cloud. Given the projected explosion in the number of devices and the resulting data generation rate, this is not feasible.
Patterson said she has three goals in IoT research. The first is to develop a computational framework that reduces the problem to an abstraction, anticipating considerations like the type and quality of data, the number of devices, and how the data are related across devices. “What kinds of relationships are people interested in with this data, and how does that embed down to the physical world? This is ultimately about searching for a pattern in how I would solve the kinds of problems that interest people,” Patterson said. The second goal is to provide a stable platform that masks the differences between devices and compensates for failed devices or computers and lost data. Finally, she will build tools to enforce a standard for speed and accuracy of the framework.
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