Deep Learning Meets The Internet Of Things: How New Frameworks Will Drive Mobile application
As mobile devices take over the world, researchers now study how to build deep learning networks that can keep up.
One collaboration between academia and industry has analyzed a number of related deep learning frameworks, and the results look promising.
When Facebook suggests new friends, Netflix recommends movies, Spotify recognizes a song, or Uber accurately predicts when your driver will arrive—they all use “deep learning”—complex algorithms that gather data about you and your environment to provide you with better recommendations and service.
“Recent advances in deep learning have greatly changed the way that computing devices process human-centric content such as images, video, speech, and audio. Applying deep neural networks to IoT devices could thus bring about a generation of applications capable of performing complex sensing and recognition tasks to support a new realm of interactions between humans and their physical surroundings,” say the authors of
Asking the right questions
One collaboration between academia and industry has analyzed a number of related deep learning frameworks, and the results look promising.
When Facebook suggests new friends, Netflix recommends movies, Spotify recognizes a song, or Uber accurately predicts when your driver will arrive—they all use “deep learning”—complex algorithms that gather data about you and your environment to provide you with better recommendations and service.
“Recent advances in deep learning have greatly changed the way that computing devices process human-centric content such as images, video, speech, and audio. Applying deep neural networks to IoT devices could thus bring about a generation of applications capable of performing complex sensing and recognition tasks to support a new realm of interactions between humans and their physical surroundings,” say the authors of .which appears in the May 2018 issue of Computer.
Asking the right questions
The researchers pose four important questions that need to be answered if mobile apps can effectively implement deep neural network technology:What deep neural network structures can effectively process and fuse sensory input data for diverse IoT applications?How can resource consumption of deep learning models be reduced such that they can be efficiently deployed on resource-constrained IoT devices? How can confidence measurements be computed correctly in deep learning predictions for IoT applications .Finally, how can the need for labeled data be minimized in learning?
In short, if deep neural networks can be used successfully in mobile apps, they must be capable of collecting data from a variety of IoT devices, as well as be energy-efficient, accurate, and able to function with minimal data labels.
Deep Sense as a solution for diverse Io T applications
The authors reviewed a general deep learning framework for inputting data from diverse IoT applications, called Deep Sense. The framework contains all the essential elements but can be customized for the learning needs of various Io T apps.
Deep Sense based algorithms (including Deep Sense and its three variants) outperform other baseline algorithms by a large margin, as can be seen in the two comparison graphs below.
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