A Machine Learning Framework for the Internet of Things

Project Summary (NSF Award 1553340)
The Internet of Things describes a network of devices, from RFID tags, to smart thermostats, to light bulbs, that can sense and communicate information. It is predicted that by 2020, there will be 25 to 50 billion devices in the Internet of Things. This massive network and the data it generates will enable new applications in a wide range of critical domains including environmental management, smart infrastructure, and healthcare. To achieve this vision, it is crucial to be able to quickly analyze and learn from the massive amount of generated data. 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.

The proposed research integrates tools and theory from machine learning, distributed computing, and networked systems in three main thrusts that include; a computational framework that provides an abstraction for algorithm design and implementation that is flexible enough to support a wide collection of machine learning methods, a framework implementation that provides a stable platform for algorithm developers by masking device heterogeneity, devices failures, and the network dynamics of the Internet of Things, and development and implementation of techniques to adapt the network and computation to support algorithm execution with performance guarantees.


  • Stacy Patterson - PI
  • Erika Mackin - PhD Student
  • Yuhao Yi - Visiting Scholar
  • Perry Adams - Undergraduate Researcher

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