Publication Type

Journal Article

Publication Date

5-2016

Abstract

The proliferation of mobile devices, such as smartphones and Internet of Things gadgets, has resulted in the recent mobile big data era. Collecting mobile big data is unprofitable unless suitable analytics and learning methods are utilized to extract meaningful information and hidden patterns from data. This article presents an overview and brief tutorial on deep learning in mobile big data analytics and discusses a scalable learning framework over Apache Spark. Specifically, distributed deep learning is executed as an iterative MapReduce computing on many Spark workers. Each Spark worker learns a partial deep model on a partition of the overall mobile, and a master deep model is then built by averaging the parameters of all partial models. This Spark-based framework speeds up the learning of deep models consisting of many hidden layers and millions of parameters. We use a context-aware activity recognition application with a real-world dataset containing millions of samples to validate our framework and assess its speedup effectiveness.

Keywords

Mobile communication, Machine learning, Computational modeling, Mobile handsets, Sparks, Big data, Sensors

Discipline

Computer Sciences

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Network

Volume

30

Issue

3

First Page

22

Last Page

29

ISSN

0890-8044

Identifier

10.1109/MNET.2016.7474340

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Copyright Owner and License

IEEE

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1109/MNET.2016.7474340

Share

COinS