Deep Learning for Internet of Things

Introduction

The rapid emergence of Internet of Things (IoT) devices has dramatically transformed modern society. Recently, with the rapid development of Artificial Intelligence (AI) and the Internet of Things (IoT), their combination, i.e., the Artificial Intelligence of Things (AIoT), is emerging. The broad penetration of IoT devices in consumer market enables unprecedented sensing capabilities by providing fine-grained, heterogeneous and temporal data. By exploring the enriched data generated by IoT devices, numerous AIoT applications ranging from smart homes and smart vehicles to large-scale smart cities and smart factories are being developed. However, the current AIoT applications are subject to a variety of limitations including resource-constrained devices, energy-efficient communication/computing, data privacy, and small sample problem, etc. With the broad application of deep learning (DL) algorithms, the fusion of DL and IoT (DL-IoT) is on the rise to address the aforementioned challenges.

We are organizing DL-IoT at ICDM 2020, to be held in Sorrento, Italy, on November 17 2020. The workshop’s primary goal is to examine the potential challenges of deep learning algorithms in the IoT era, and to explore advanced deep learning algorithms and edge intelligence for IoT applications. This workshop will provide an interactive forum for discussion on recent and ongoing developments, key issues and challenges, and practices related to deep learning for Internet of Things. We welcome contributions within a broad range of AIoT and DL-IoT.

Topics of Interest

This workshop will provide an interactive forum for discussion on recent and ongoing developments, key issues and challenges, and practices related to DL-IoT. Researchers and practitioners from academia, industry, and government are invited to present their work and perspectives and participate in the workshop.

Topics of interest include, but are not limited to:

  • Deep learning for IoT and industrial IoT
  • Distributed learning in IoT
  • Edge intelligence for IoT
  • Cross-domain knowledge transfer in IoT
  • Advanced deep learning for the security issues in IoT
  • Energy-efficient deep learning algorithms for IoT
  • Advanced deep learning for big data and streaming analytics in IoT
  • Novel applications of deep learning in smart home, smart city, smart transportation, business, and smart factory

Important Dates

  • Workshop paper submissions: August 24, 2020
  • Workshop paper notification: September 17, 2020
  • Camera-ready deadline and copyright forms: September 24, 2020
  • Conference dates: November 17, 2020

Submission Guidelines

Paper submissions should be limited to a maximum of ten (10) pages, in the IEEE 2-column format , including the bibliography and any possible appendices. Submissions longer than 10 pages will be rejected without review. All submissions will be reviewed by the Program Committee on the basis of technical quality, relevance to scope of the conference, originality, significance, and clarity. The following sections give further information for authors.

Algorithms and resources used in a paper should be described as completely as possible to allow reproducibility. This includes experimental methodology, empirical evaluations, and results. Authors are strongly encouraged to make their code and data publicly available whenever possible. In addition, authors are strongly encouraged to also report, whenever possible, results for their methods on publicly available datasets.Algorithms and resources used in a paper should be described as completely as possible to allow reproducibility. This includes experimental methodology, empirical evaluations, and results. Authors are strongly encouraged to make their code and data publicly available whenever possible. In addition, authors are strongly encouraged to also report, whenever possible, results for their methods on publicly available datasets.

Accepted papers will be published in the conference proceedings by the IEEE Computer Society Press. All manuscripts are submitted as full papers and are reviewed based on their scientific merit. There is no separate abstract submission step. There are no separate industrial, application, short paper or poster tracks during submission. Manuscripts must be submitted electronically in online submission system. We do not accept email submissions.

Organizers

  • Bin Guo, Northwestern Polytechnical University, P.R.China, guob@nwpu.edu.cn
  • Yunji Liang, Northwestern Polytechnical University, P.R.China, liangyunji@nwpu.edu.cn
  • Lina Yao, University of New South Wales, Australia, lina.yao@unsw.edu.au

Program Committee

  • Uttam Ghosh,   Tennessee State University,   USA
  • Bin Guo,   Northwestern Polytechnical University,   China
  • Yunji Liang,   Northwestern Polytechnical University,   China
  • Flora Salim,   RMIT University,   USA
  • Sagar Samtani,   Indiana University,   USA
  • Longfei Shangguan,   Microsoft Cloud & AI,   China
  • Jiangtao Wang,   Lancaster University,   UK
  • Xianzhi Wang,   University of Technology Sydney,   Australia
  • Xing Xie,   Microsoft Research Asia ,  China
  • Lina Yao,   University of New South Wales,   Australia
  • Wei Zhang,   University of Adelaide,   Australia

Contact US

Email:  liangyunji@nwpu.edu.cn